Preface
This document describes the PGI Fortran interfaces to cuBLAS, cuFFT, cuRAND, and cuSPARSE, which are CUDA Libraries used in scientific and engineering applications built upon the CUDA computing architecture.
Intended Audience
This guide is intended for application programmers, scientists and engineers proficient in programming with the Fortran language. This guide assumes some familiarity with either CUDA Fortran or OpenACC.
Organization
The organization of this document is as follows:
- Introduction
- contains a general introduction to Fortran interfaces, OpenACC, CUDA Fortran, and CUDA Library functions
- BLAS Runtime Library APIs
- describes the Fortran interfaces to the various cuBLAS libraries
- FFT Runtime Library APIs
- describes the module types, definitions and Fortran interfaces to the cuFFT library
- Random Number Runtime APIs
- describes the Fortran interfaces to the host and device cuRAND libraries
- Sparse Matrix Runtime APIs
- describes the module types, definitions and Fortran interfaces to the cuSPARSE Library
- Examples
- provides sample code and an explanation of each of the simple examples.
Conventions
This guide uses the following conventions:
- italic
- is used for emphasis.
- Constant Width
- is used for filenames, directories, arguments, options, examples, and for language statements in the text, including assembly language statements.
- Bold
- is used for commands.
- [ item1 ]
- in general, square brackets indicate optional items. In this case item1 is optional. In the context of p/t-sets, square brackets are required to specify a p/t-set.
- { item2 | item 3 }
- braces indicate that a selection is required. In this case, you must select either item2 or item3.
- filename ...
- ellipsis indicate a repetition. Zero or more of the preceding item may occur. In this example, multiple filenames are allowed.
- FORTRAN
- Fortran language statements are shown in the text of this guide using a reduced fixed point size.
- C/C++
- C/C++ language statements are shown in the test of this guide using a reduced fixed point size.
The PGI compilers and tools are supported on a wide variety of Linux, macOS and Windows operating systems running on 64-bit x86-compatible processors, and on Linux running on OpenPOWER processors. (Currently, the PGI debugger is supported on x86-64/x64 only.) See the Compatibility and Installation section on the PGI website for a comprehensive listing of supported platforms.
1. Introduction
This document provides a reference for calling CUDA Library functions from PGI Fortran. It can be used from Fortran code using the OpenACC programming model, or from PGI CUDA Fortran. Currently, the CUDA libraries which PGI provides pre-built interface modules for, and which are documented here, are:
- cuBLAS, an implementation of the BLAS.
- cuFFT, a library of Fast Fourier Transform (FFT) routines.
- cuRAND, a library for random number generation.
- cuSPARSE, a library of linear algebra routines used with sparse matrices.
The OpenACC Application Program Interface is a collection of compiler directives and runtime routines that allows the programmer to specify loops and regions of code for offloading from a host CPU to an attached accelerator, such as a GPU. The OpenACC API was designed and is maintained by an industry consortium. See the OpenACC website for more information about the OpenACC API.
CUDA Fortran is a small set of extensions to Fortran that supports and is built upon the CUDA computing architecture. CUDA Fortran includes a Fortran 2003 compiler and tool chain for programming NVIDIA GPUs using Fortran, and is an analog to NVIDIA's CUDA C compiler. Compared to the PGI Accelerator and OpenACC directives-based model and compilers, CUDA Fortran is a lower-level explicit programming model with substantial runtime library components that give expert programmers direct control of all aspects of GPGPU programming.
This document does not contain explanations or purposes of the library functions, nor does it contain details of the approach used in the CUDA implementation to target GPUs. For that information, please see the appropriate library document that comes with the NVIDIA CUDA Toolkit. This document does provide the Fortran module contents: derived types, enumerations, and interfaces, to make use of the libraries from Fortran rather than from C or C++.
Many of the examples used in this document are provided in the PGI compiler and tools distribution, along with Makefiles, and are stored in the yearly directory, such as 2016/CUDA-Libraries.
1.1. Fortran Interfaces and Wrappers
Almost all of the function interfaces shown in this document make use of features from the Fortran 2003 iso_c_binding intrinsic module. This module provides a standard way for dealing with isues such as inter-language data types, capitalization, adding underscores to symbol names, or passing arguments by value.
Often, the iso_c_binding module enables Fortran programs containing properly written interfaces to call directly into the C library functions. In some cases, PGI has written small wrappers around the C library function, to make the Fortran call site more "Fortran-like", hiding some issues exposed in the C interfaces like handle management, host vs. device pointer management, or character and complex data type issues.
In a small number of cases, the C Library may contain multiple entry points to handle different data types, perhaps an int in one function and a size_t in another, otherwise the functions are identical. In these cases, PGI may provide just one generic Fortran interface, and will call the appropriate C function under the hood.
1.2. Using CUDA Libraries from OpenACC Host Code
All four of the libraries covered in this document contain functions which are callable from OpenACC host code. Most functions take some arguments which are expected to be device pointers (the address of a variable in device global memory). There are several ways to do that in OpenACC.
If the call is lexically nested within an OpenACC data directive, the PGI Fortran compiler, in the presence of an explicit interface such as those provided by the PGI library modules, will default to passing the device pointer when required.
subroutine hostcall(a, b, n) use cublas real a(n), b(n) !$acc data copy(a, b) call cublasSswap(n, a, 1, b, 1) !$acc end data return end
A Fortran interface is made explicit when you use the module that contains it, as in the line use cublas in the example above. If you look ahead to the actual interface for cublasSswap, you will see that the arrays a and b are declared with the CUDA Fortran device attribute, so they take only device addresses as arguments.
It is more acceptable and general when using OpenACC to pass device pointers to subprograms by using the host_data clause as most implementations don't have a way to mark arguments as device pointers. The host_data construct with the use_device clause makes the device addresses available in host code for passing to the subprogram.
use cufft use openacc . . . !$acc data copyin(a), copyout(b,c) ierr = cufftPlan2D(iplan1,m,n,CUFFT_C2C) ierr = ierr + cufftSetStream(iplan1,acc_get_cuda_stream(acc_async_sync)) !$acc host_data use_device(a,b,c) ierr = ierr + cufftExecC2C(iplan1,a,b,CUFFT_FORWARD) ierr = ierr + cufftExecC2C(iplan1,b,c,CUFFT_INVERSE) !$acc end host_data ! scale c !$acc kernels c = c / (m*n) !$acc end kernels !$acc end data
This code snippet also shows an example of sharing the stream that OpenACC and the cuFFT library use. Every library in this document has a function for setting the CUDA stream which the library runs on. Usually, when using OpenACC, you want the OpenACC kernels to run on the same stream as the library functions. In the case above, this guarantees that the kernel c = c / (m*n) does not start until the FFT operations complete. The function acc_get_cuda_stream and the definition for acc_async_sync are in the openacc module.
1.3. Using CUDA Libraries from OpenACC Device Code
Two libraries are available from withn OpenACC compute regions, though the two can behave quite differently. Functions in both the openacc_cublas module and the openacc_curand module are marked acc routine seq. The cuBLAS interfaces from device code closely mirror what's available from the host, but the underlying implementation may launch a new kernel using CUDA dynamic parallelism. The routines should not be called by multiple threads if you expect the threads to cooperate together to compute the answer.
subroutine testdev( a, b, n ) use openacc_cublas real :: a(n), b(n) type(cublasHandle) :: h !$acc parallel num_gangs(1) copy(a,b,h) j = cublasCreate(h) j = cublasSswap(h,n,a,1,b,1) j = cublasDestroy(h) !$acc end parallel return end subroutine
When using the openacc_cublas module, you must link with -lcublas_device (or defaultlib:cublas_device on Windows) and compile and link with -Mcuda.
The cuRAND device library is all contained within CUDA header files. In device code, it is designed to return one or a small number of random numbers per thread. The thread's random generators run independently of each other, and it is usually advised for performance reasons to give each thread a different seed, rather than a different offset.
program t use openacc_curand integer, parameter :: n = 500 real a(n,n,4) type(curandStateXORWOW) :: h integer(8) :: seed, seq, offset a = 0.0 !$acc parallel num_gangs(n) vector_length(n) copy(a) !$acc loop gang do j = 1, n !$acc loop vector private(h) do i = 1, n seed = 12345_8 + j*n*n + i*2 seq = 0_8 offset = 0_8 call curand_init(seed, seq, offset, h) !$acc loop seq do k = 1, 4 a(i,j,k) = curand_uniform(h) end do end do end do !$acc end parallel print *,maxval(a),minval(a),sum(a)/(n*n*4) end
When using the openacc_curand module, since all the code is contained in CUDA header files, you do not need any additional libraries on the link line. However, since the current implementation relies on CUDA compilation, you must compile with -ta=tesla,nollvm.
1.4. Using CUDA Libraries from CUDA Fortran Host Code
The predominant usage model for the library functions listed in this document is to call them from CUDA Host code. CUDA Fortran allows some special capabilities in that the compiler is able to recognize the device and managed attribute in resolving generic interfaces. Device actual arguments can only match the interface's device dummy arguments; managed actual arguments, by precedence, match managed dummy arguments first, then device dummies, then host.
program testisamax ! link with -Mcudalib=cublas -lblas use cublas real*4 x(1000) real*4, device :: xd(1000) real*4, managed :: xm(1000) call random_number(x) ! Call host BLAS j = isamax(1000,x,1) xd = x ! Call cuBLAS k = isamax(1000,xd,1) print *,j.eq.k xm = x ! Also calls cuBLAS k = isamax(1000,xm,1) print *,j.eq.k end
Using the cudafor module, the full set of CUDA functionality is available to programmers for managing CUDA events, streams, synchronization, and asynchronous behaviors. CUDA Fortran can be used in OpenMP programs, and the CUDA Libraries in this document are thread safe with respect to host CPU threads. Further examples are included in chapter Examples.
1.5. Using CUDA Libraries from CUDA Fortran Device Code
The cuBLAS and cuRAND libraries have functions callable from CUDA Fortran device code, and their interfaces are accessed via the cublas_device and curand_device modules, respectively. The module interfaces are very similar to the modules used in OpenACC device code, but for CUDA Fortran, each subroutine and function is declared attributes(device), and the subroutines and functions do not need to be marked as acc routine seq.
! cuBLAS in device code requires -Mcuda=cc35 or higher ! since it potentially uses dynamic parallelism to launch kernels. ! pgfortran -Mcuda=cc35 testcu.cuf -lcublas_device attributes(global) subroutine testcu( a, b, n ) use cublas_device real, device :: a(*), b(*) type(cublasHandle) :: h integer, value :: n i = threadIdx%x if (i.eq.1) then j = cublasCreate(h) j = cublasSswap(h,n,a,1,b,1) j = cublasDestroy(h) end if return end subroutine
Using the device cuRAND library with CUDA Fortran also requires compiling with -Mcuda=nollvm so the CUDA in the cuRAND headers can get compiled.
module mrand use curand_device integer, parameter :: n = 500 contains attributes(global) subroutine randsub(a) real, device :: a(n,n,4) type(curandStateXORWOW) :: h integer(8) :: seed, seq, offset j = blockIdx%x; i = threadIdx%x seed = 12345_8 + j*n*n + i*2 seq = 0_8 offset = 0_8 call curand_init(seed, seq, offset, h) do k = 1, 4 a(i,j,k) = curand_uniform(h) end do end subroutine end module program t ! pgfortran -Mcuda=nollvm t.cuf use mrand use cudafor ! recognize maxval, minval, sum w/managed real, managed :: a(n,n,4) a = 0.0 call randsub<<<n,n>>>(a) print *,maxval(a),minval(a),sum(a)/(n*n*4) end program
1.6. Pointer Modes in cuBLAS and cuSPARSE
Because the PGI Fortran compiler can distinguish between host and device arguments, the PGI modules for interfacing to cuBLAS and cuSPARSE handle pointer modes differently than CUDA C, which requires setting the mode explicitly for scalar arguments. Examples of scalar arguments which can reside either on the host or device are the alpha and beta scale factors to the *gemm functions.
Typically, when using the normal "non-_v2" interfaces in the cuBLAS and cuSPARSE modules, the runtime wrappers will implicitly add the setting and restoring of the library pointer modes behind the scenes. This adds some negligible but non-zero overhead to the calls.
To avoid the implicit getting and setting of the pointer mode with every invocation of a library function do the following:
-
For the BLAS, use the cublas_v2 module, and the v2 entry points, such as cublasIsamax_v2. It is the programmer's responsibility to properly set the pointer mode when needed. Examples of scalar arguments which do require setting the pointer mode are the alpha and beta scale factors passed to the *gemm routines, and the scalar results returned from the v2 versions of the *amax(), *amin(), *asum(), *rotg(), *rotmg(), *nrm2(), and *dot() functions. In the v2 interfaces shown in the chapter 2, these scalar arguments will have the comment ! device or host variable. Examples of scalar arguments which do not require setting the pointer mode are increments, extents, and lengths such as incx, incy, n, lda, ldb, and ldc.
-
For the cuSPARSE library, each function listed in chapter 5 which contains scalar arguments with the comment ! device or host variable has a corresponding v2 interface, though it is not documented here. For instance, in addition to the interface named cusparseSaxpyi, there is another interface named cusparseSaxpyi_v2 with the exact same argument list which calls into the cuSPARSE library directly and will not implicitly get or set the library pointer mode.
The CUDA default pointer mode is that the scalar arguments reside on the host. The PGI runtime does not change that setting.
1.7. Writing Your Own CUDA Interfaces
Despite the large number of interfaces included in the modules described in this document, users will have the need from time-to-time to write their own interfaces to new libraries or their own tuned CUDA, perhaps written in C/C++. There are some standard techniques to use, and some non-standard PGI extensions which can make creating working interfaces easier.
! cufftExecC2C interface cufftExecC2C integer function cufftExecC2C( plan, idata, odata, direction ) bind(C,name='cufftExecC2C') integer, value :: plan complex, device, dimension(*) :: idata, odata integer, value :: direction end function cufftExecC2C end interface cufftExecC2C
This interface calls the C library function directly. You can deal with Fortran's capitalization issues by putting the properly capitalized C function in the bind(C) attribute. If the C function expects input arguments passed by value, you can add the value attribute to the dummy declaration as well. A nice feature of Fortran is that the interface can change, but the code at the call site may not have to. The compiler changes the details of the call to fit the interface.
Now suppose a user of this interface would like to call this function with REAL data (F77 code is notorious for mixing REAL and COMPLEX declarations). There are two ways to do this:
! cufftExecC2C interface cufftExecC2C integer function cufftExecC2C( plan, idata, odata, direction ) bind(C,name='cufftExecC2C') integer, value :: plan complex, device, dimension(*) :: idata, odata integer, value :: direction end function cufftExecC2C integer function cufftExecR2R( plan, idata, odata, direction ) bind(C,name='cufftExecC2C') integer, value :: plan real, device, dimension(*) :: idata, odata integer, value :: direction end function cufftExecR2R end interface cufftExecC2C
Here the C name hasn't changed. The compiler will now accept actual arguments corresponding to idata and odata that are declared REAL. A generic interface is created named cufftExecC2C. If you have problems debugging your generic interface, as a debugging aid you can try calling the specific name, cufftExecR2R in this case, to help diagnose the problem.
A commonly used extension which is supported by PGI is ignore_tkr. A programmer can use it in an interface to instruct the compiler to ignore any combination of the type, kind, and rank during the interface matching process. The previous example using ignore_tkr looks like this:
! cufftExecC2C interface cufftExecC2C integer function cufftExecC2C( plan, idata, odata, direction ) bind(C,name='cufftExecC2C') integer, value :: plan !dir$ ignore_tkr(tr) idata, (tr) odata complex, device, dimension(*) :: idata, odata integer, value :: direction end function cufftExecC2C end interface cufftExecC2C
Now the compiler will ignore both the type and rank (F77 could also be sloppy in its handling of array dimensions) of idata and odata when matching the call site to the interface. An unfortunate side-effect is that the interface will now allow integer, logical, and character data for idata and odata. It is up to the implementor to determine if that is acceptable.
A final aid, specific to PGI, worth mentioning here is ignore_tkr (d), which ignores the device attribute of an actual argument during interface matching.
Of course, if you write a wrapper, a narrow strip of code between the Fortran call and your library function, you are not limited by the simple transormations that a compiler can do, such as those listed here. As mentioned earlier, many of the interfaces provided in the cuBLAS and cuSPARSE modules use wrappers.
A common request is a way for Fortran programmers to take advantage of the thrust library. Explaining thrust and C++ programming is outside of the scope of this document, but this simple example can show how to take advantage of the excellent sort capabilities in thrust:
// Filename: csort.cu // nvcc -c -arch sm_35 csort.cu #include <thrust/device_vector.h> #include <thrust/copy.h> #include <thrust/sort.h> extern "C" { //Sort for integer arrays void thrust_int_sort_wrapper( int *data, int N) { thrust::device_ptr <int> dev_ptr(data); thrust::sort(dev_ptr, dev_ptr+N); } //Sort for float arrays void thrust_float_sort_wrapper( float *data, int N) { thrust::device_ptr <float> dev_ptr(data); thrust::sort(dev_ptr, dev_ptr+N); } //Sort for double arrays void thrust_double_sort_wrapper( double *data, int N) { thrust::device_ptr <double> dev_ptr(data); thrust::sort(dev_ptr, dev_ptr+N); } }
Set up interface to the sort subroutine in Fortran and calls are simple:
program t interface sort subroutine sort_int(array, n) & bind(C,name='thrust_int_sort_wrapper') integer(4), device, dimension(*) :: array integer(4), value :: n end subroutine end interface integer(4), parameter :: n = 100 integer(4), device :: a_d(n) integer(4) :: a_h(n) !$cuf kernel do do i = 1, n a_d(i) = 1 + mod(47*i,n) end do call sort(a_d, n) a_h = a_d nres = count(a_h .eq. (/(i,i=1,n)/)) if (nres.eq.n) then print *,"test PASSED" else print *,"test FAILED" endif end
1.8. PGI Fortran Compiler Options
The PGI Fortran compiler driver is called pgfortran. General information on the compiler options which can be passed to pgfortran can be obtained by typing pgfortran -help. To enable targeting NVIDIA GPUs using OpenACC, use pgfortran -ta=tesla. To enable targeting NVIDIA GPUs using CUDA Fortran, use pgfortran -Mcuda. CUDA Fortran is also supported by the PGI Fortran compilers when the filename uses the .cuf extension. Uppercase file extensions, .F90 or .CUF, for example, may also be used, in which case the program is processed by the preprocessor before being compiled.
Other options which are pertinent to the examples in this document are:
-
--Mcudalib[=cublas|cufft|curand|cusparse]: this option adds the appropriate versions of the CUDA-optimized libraries to the link line. It handles static and dynamic linking, and platform (Linux, Windows, macOS) differences unobtrusively.
-
--Mcuda=[no]llvm: this option chooses between two choices for the compiler back-end code generator. Currently, using the cuRAND library from device code requires -Mcuda=nollvm.
-
--ta=tesla:cc35: this option compiles for compute capability 3.5. Certain device functionality, such as dynamic parallelism in the cuBLAS library, requires compute capability 3.5 or higher.
-
--lcublas_device: this adds the cuBLAS device library to set of linker options. On Windows, use --defaultlib:cublas_device.
2. BLAS Runtime APIs
This section describes the Fortran interfaces to the CUDA BLAS libraries. There are currently four somewhat separate collections of function entry points which are commonly referred to as the cuBLAS:
-
The original CUDA implementation of the BLAS routines, referred to as the legacy API, which are callable from the host and expect and operate on device data.
-
The newer "v2" CUDA implementation of the BLAS routines, plus some extensions for batched operations. These are also callable from the host and operate on device data. In Fortran terms, these entry points have been changed from subroutines to functions which return status.
-
Another implementation of the BLAS routines using the v2 entry points, callable from device code and which may take advantage of dynamic parallelism.
-
A new cuBLAS XT library which can target multiple GPUs using only host-resident data.
PGI currently ships with five Fortran modules which programmers can use to call into this cuBLAS functionality:
-
cublas, which provides interfaces to into the main cublas library. Both the legacy and v2 names are supported. In this module, the cublas names (such as cublasSaxpy) use the legacy calling conventions. Interfaces to a host BLAS library (for instance libblas.a in the PGI distribution) are also included in the cublas module.
-
cublas_v2, which is similar to the cublas module in most ways except the cublas names (such as cublasSaxpy) use the v2 calling conventions. For instance, instead of a subroutine, cublasSaxpy is a function which takes a handle as the first argument and returns an integer containing the status of the call.
-
cublasxt, which interfaces directly to the cublasXT API.
-
cublas_device, which is useable from CUDA Fortran device code and interfaces into the static cuBLAS Library cublas_device.a. The legacy cuBLAS API is not supported in this library or module.
-
openacc_cublas, which is useable from OpenACC device code and also provides interfaces into cublas_device.a. For convenience, this module marks each function as "!$acc routine seq". The legacy cuBLAS API is not supported in this library or module.
The v2 routines are integer functions that return an error status code; they return a value of CUBLAS_STATUS_SUCCESS if the call was successful, or other cuBLAS status return value if there was an error.
Documented interfaces to the traditional BLAS names in the subsequent sections, which contain the comment ! device or host variable should not be confused with the pointer mode issue from section 1.6. The traditional BLAS names are overloaded generic names in the cublas module. For instance, in this interface
subroutine scopy(n, x, incx, y, incy) integer :: n real(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
The arrays x and y can either both be device arrays, in which case cublasScopy is called via the generic interface, or they can both be host arrays, in which case scopy from the host BLAS library is called. Using CUDA Fortran managed data as actual arguments to scopy poses an interesting case, and calling cublasScopy is chosen by default. If you wish to call the host library version of scopy with managed data, don't expose the generic scopy interface at the call site.
Unless a specific kind is provided, in the following interfaces the plain integer type implies integer(4) and the plain real type implies real(4).
2.1. CUBLAS Definitions and Helper Functions
This section contains definitions and data types used in the cuBLAS library and interfaces to the cuBLAS Helper Functions.
The cublas module contains the following derived type definitions:
TYPE cublasHandle TYPE(C_PTR) :: handle END TYPE
The cuBLAS module contains the following enumerations:
enum, bind(c) enumerator :: CUBLAS_STATUS_SUCCESS =0 enumerator :: CUBLAS_STATUS_NOT_INITIALIZED =1 enumerator :: CUBLAS_STATUS_ALLOC_FAILED =3 enumerator :: CUBLAS_STATUS_INVALID_VALUE =7 enumerator :: CUBLAS_STATUS_ARCH_MISMATCH =8 enumerator :: CUBLAS_STATUS_MAPPING_ERROR =11 enumerator :: CUBLAS_STATUS_EXECUTION_FAILED=13 enumerator :: CUBLAS_STATUS_INTERNAL_ERROR =14 end enum
enum, bind(c) enumerator :: CUBLAS_FILL_MODE_LOWER=0 enumerator :: CUBLAS_FILL_MODE_UPPER=1 end enum
enum, bind(c) enumerator :: CUBLAS_DIAG_NON_UNIT=0 enumerator :: CUBLAS_DIAG_UNIT=1 end enum
enum, bind(c) enumerator :: CUBLAS_SIDE_LEFT =0 enumerator :: CUBLAS_SIDE_RIGHT=1 end enum
enum, bind(c) enumerator :: CUBLAS_OP_N=0 enumerator :: CUBLAS_OP_T=1 enumerator :: CUBLAS_OP_C=2 end enum
enum, bind(c) enumerator :: CUBLAS_POINTER_MODE_HOST = 0 enumerator :: CUBLAS_POINTER_MODE_DEVICE = 1 end enum
2.1.1. cublasCreate
This function initializes the CUBLAS library and creates a handle to an opaque structure holding the CUBLAS library context. It allocates hardware resources on the host and device and must be called prior to making any other CUBLAS library calls. The CUBLAS library context is tied to the current CUDA device. To use the library on multiple devices, one CUBLAS handle needs to be created for each device. Furthermore, for a given device, multiple CUBLAS handles with different configuration can be created. Because cublasCreate allocates some internal resources and the release of those resources by calling cublasDestroy will implicitly call cublasDeviceSynchronize, it is recommended to minimize the number of cublasCreate/cublasDestroy occurences. For multi-threaded applications that use the same device from different threads, the recommended programming model is to create one CUBLAS handle per thread and use that CUBLAS handle for the entire life of the thread.
integer(4) function cublasCreate(handle) type(cublasHandle) :: handle
2.1.2. cublasDestroy
This function releases hardware resources used by the CUBLAS library. This function is usually the last call with a particular handle to the CUBLAS library. Because cublasCreate allocates some internal resources and the release of those resources by calling cublasDestroy will implicitly call cublasDeviceSynchronize, it is recommended to minimize the number of cublasCreate/cublasDestroy occurences.
integer(4) function cublasDestroy(handle) type(cublasHandle) :: handle
2.1.3. cublasGetVersion
This function returns the version number of the cuBLAS library.
integer(4) function cublasGetVersion(handle, version) type(cublasHandle) :: handle integer(4) :: version
2.1.4. cublasSetStream
This function sets the cuBLAS library stream, which will be used to execute all subsequent calls to the cuBLAS library functions. If the cuBLAS library stream is not set, all kernels use the default NULL stream. In particular, this routine can be used to change the stream between kernel launches and then to reset the cuBLAS library stream back to NULL.
integer(4) function cublasSetStream(handle, stream) type(cublasHandle) :: handle integer(kind=cuda_stream_kind()) :: stream
2.1.5. cublasGetStream
This function gets the cuBLAS library stream, which is being used to execute all calls to the cuBLAS library functions. If the cuBLAS library stream is not set, all kernels use the default NULL stream.
integer(4) function cublasGetStream(handle, stream) type(cublasHandle) :: handle integer(kind=cuda_stream_kind()) :: stream
2.1.6. cublasGetPointerMode
This function obtains the pointer mode used by the cuBLAS library. In the cublas module, the pointer mode is set and reset on a call-by-call basis depending on the whether the device attribute is set on scalar actual arguments. See section 1.6 for a discussion of pointer modes.
integer(4) function cublasGetPointerMode(handle, mode) type(cublasHandle) :: handle integer(4) :: mode
2.1.7. cublasSetPointerMode
This function sets the pointer mode used by the cuBLAS library. When using the cublas module, the pointer mode is set on a call-by-call basis depending on the whether the device attribute is set on scalar actual arguments. When using the cublas_v2 module with v2 interfaces, it is the programmer's responsibility to make calls to cublasSetPointerMode so scalar arguments are handled correctly by the library. See section 1.6 for a discussion of pointer modes.
integer(4) function cublasSetPointerMode(handle, mode) type(cublasHandle) :: handle integer(4) :: mode
2.1.8. cublasGetAtomicsMode
This function obtains the atomics mode used by the cuBLAS library.
integer(4) function cublasGetAtomicsMode(handle, mode) type(cublasHandle) :: handle integer(4) :: mode
2.1.9. cublasSetAtomicsMode
This function sets the atomics mode used by the cuBLAS library. Some routines in the cuBLAS library have alternate implementations that use atomics to accumulate results. These alternate implementations may run faster but may also generate results which are not identical from one run to the other. The default is to not allow atomics in cuBLAS functions.
integer(4) function cublasSetAtomicsMode(handle, mode) type(cublasHandle) :: handle integer(4) :: mode
2.1.10. cublasGetHandle
This function gets the cuBLAS handle currently in use by a thread. The CUDA Fortran runtime keeps track of a CPU thread's current handle, if you are either using the legacy BLAS API, or do not wish to pass the handle through to low-level functions or subroutines manually.
type(cublashandle) function cublasGetHandle()
integer(4) function cublasGetHandle(handle) type(cublasHandle) :: handle
2.1.11. cublasSetVector
This function copies n elements from a vector x in host memory space to a vector y in GPU memory space. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of vector x and y is overloaded to take any data type, but the size of the data type must still be specified in bytes. This functionality can also be implemented using cudaMemcpy or array assignment statements.
integer(4) function cublassetvector(n, elemsize, x, incx, y, incy) integer :: n, elemsize, incx, incy integer*1, dimension(*) :: x integer*1, device, dimension(*) :: y
2.1.12. cublasGetVector
This function copies n elements from a vector x in GPU memory space to a vector y in host memory space. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of vector x and y is overloaded to take any data type, but the size of the data type must still be specified in bytes. This functionality can also be implemented using cudaMemcpy or array assignment statements.
integer(4) function cublasgetvector(n, elemsize, x, incx, y, incy) integer :: n, elemsize, incx, incy integer*1, device, dimension(*) :: x integer*1, dimension(*) :: y
2.1.13. cublasSetMatrix
This function copies a tile of rows x cols elements from a matrix A in host memory space to a matrix B in GPU memory space. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of Matrix A and B is overloaded to take any data type, but the size of the data type must still be specified in bytes. This functionality can also be implemented using cudaMemcpy, cudaMemcpy2D, or array assignment statements.
integer(4) function cublassetmatrix(rows, cols, elemsize, a, lda, b, ldb) integer :: rows, cols, elemsize, lda, ldb integer*1, dimension(lda, *) :: a integer*1, device, dimension(ldb, *) :: b
2.1.14. cublasGetMatrix
This function copies a tile of rows x cols elements from a matrix A in GPU memory space to a matrix B in host memory space. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of Matrix A and B is overloaded to take any data type, but the size of the data type must still be specified in bytes. This functionality can also be implemented using cudaMemcpy, cudaMemcpy2D, or array assignment statements.
integer(4) function cublasgetmatrix(rows, cols, elemsize, a, lda, b, ldb) integer :: rows, cols, elemsize, lda, ldb integer*1, device, dimension(lda, *) :: a integer*1, dimension(ldb, *) :: b
2.1.15. cublasSetVectorAsync
This function copies n elements from a vector x in host memory space to a vector y in GPU memory space, asynchronously, on the given CUDA stream. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of vector x and y is overloaded to take any data type, but the size of the data type must still be specified in bytes. This functionality can also be implemented using cudaMemcpyAsync.
integer(4) function cublassetvectorasync(n, elemsize, x, incx, y, incy, stream) integer :: n, elemsize, incx, incy integer*1, dimension(*) :: x integer*1, device, dimension(*) :: y integer(kind=cuda_stream_kind()) :: stream
2.1.16. cublasGetVectorAsync
This function copies n elements from a vector x in host memory space to a vector y in GPU memory space, asynchronously, on the given CUDA stream. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of vector x and y is overloaded to take any data type, but the size of the data type must still be specified in bytes. This functionality can also be implemented using cudaMemcpyAsync.
integer(4) function cublasgetvectorasync(n, elemsize, x, incx, y, incy, stream) integer :: n, elemsize, incx, incy integer*1, device, dimension(*) :: x integer*1, dimension(*) :: y integer(kind=cuda_stream_kind()) :: stream
2.1.17. cublasSetMatrixAsync
This function copies a tile of rows x cols elements from a matrix A in host memory space to a matrix B in GPU memory space, asynchronously using the specified stream. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of Matrix A and B is overloaded to take any data type, but the size of the data type must still be specified in bytes. This functionality can also be implemented using cudaMemcpyAsync or cudaMemcpy2DAsync.
integer(4) function cublassetmatrixasync(rows, cols, elemsize, a, lda, b, ldb, stream) integer :: rows, cols, elemsize, lda, ldb integer*1, dimension(lda, *) :: a integer*1, device, dimension(ldb, *) :: b integer(kind=cuda_stream_kind()) :: stream
2.1.18. cublasGetMatrixAsync
This function copies a tile of rows x cols elements from a matrix A in GPU memory space to a matrix B in host memory space, asynchronously, using the specified stream. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of Matrix A and B is overloaded to take any data type, but the size of the data type must still be specified in bytes. This functionality can also be implemented using cudaMemcpyAsync or cudaMemcpy2DAsync.
integer(4) function cublasgetmatrixasync(rows, cols, elemsize, a, lda, b, ldb, stream) integer :: rows, cols, elemsize, lda, ldb integer*1, device, dimension(lda, *) :: a integer*1, dimension(ldb, *) :: b integer(kind=cuda_stream_kind()) :: stream
2.2. Single Precision Functions and Subroutines
This section contains interfaces to the single precision BLAS and cuBLAS functions and subroutines.
2.2.1. isamax
ISAMAX finds the index of the element having the maximum absolute value.
integer(4) function isamax(n, x, incx) integer :: n real(4), device, dimension(*) :: x ! device or host variable integer :: incx
integer(4) function cublasIsamax(n, x, incx) integer :: n real(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasIsamax_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.2.2. isamin
ISAMIN finds the index of the element having the minimum absolute value.
integer(4) function isamin(n, x, incx) integer :: n real(4), device, dimension(*) :: x ! device or host variable integer :: incx
integer(4) function cublasIsamin(n, x, incx) integer :: n real(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasIsamin_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.2.3. sasum
SASUM takes the sum of the absolute values.
real(4) function sasum(n, x, incx) integer :: n real(4), device, dimension(*) :: x ! device or host variable integer :: incx
real(4) function cublasSasum(n, x, incx) integer :: n real(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasSasum_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx real(4), device :: res ! device or host variable
2.2.4. saxpy
SAXPY constant times a vector plus a vector.
subroutine saxpy(n, a, x, incx, y, incy) integer :: n real(4), device :: a ! device or host variable real(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasSaxpy(n, a, x, incx, y, incy) integer :: n real(4), device :: a ! device or host variable real(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasSaxpy_v2(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4), device :: a ! device or host variable real(4), device, dimension(*) :: x, y integer :: incx, incy
2.2.5. scopy
SCOPY copies a vector, x, to a vector, y.
subroutine scopy(n, x, incx, y, incy) integer :: n real(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasScopy(n, x, incx, y, incy) integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasScopy_v2(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
2.2.6. sdot
SDOT forms the dot product of two vectors.
real(4) function sdot(n, x, incx, y, incy) integer :: n real(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
real(4) function cublasSdot(n, x, incx, y, incy) integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasSdot_v2(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy real(4), device :: res ! device or host variable
2.2.7. snrm2
SNRM2 returns the euclidean norm of a vector via the function name, so that SNRM2 := sqrt( x'*x ).
real(4) function snrm2(n, x, incx) integer :: n real(4), device, dimension(*) :: x ! device or host variable integer :: incx
real(4) function cublasSnrm2(n, x, incx) integer :: n real(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasSnrm2_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx real(4), device :: res ! device or host variable
2.2.8. srot
SROT applies a plane rotation.
subroutine srot(n, x, incx, y, incy, sc, ss) integer :: n real(4), device :: sc, ss ! device or host variable real(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasSrot(n, x, incx, y, incy, sc, ss) integer :: n real(4), device :: sc, ss ! device or host variable real(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasSrot_v2(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(4), device :: sc, ss ! device or host variable real(4), device, dimension(*) :: x, y integer :: incx, incy
2.2.9. srotg
SROTG constructs a Givens plane rotation.
subroutine srotg(sa, sb, sc, ss) real(4), device :: sa, sb, sc, ss ! device or host variable
subroutine cublasSrotg(sa, sb, sc, ss) real(4), device :: sa, sb, sc, ss ! device or host variable
integer(4) function cublasSrotg_v2(h, sa, sb, sc, ss) type(cublasHandle) :: h real(4), device :: sa, sb, sc, ss ! device or host variable
2.2.10. srotm
SROTM applies the modified Givens transformation, H, to the 2 by N matrix (SX**T) , where **T indicates transpose. The elements of SX are in (SX**T) SX(LX+I*INCX), I = 0 to N-1, where LX = 1 if INCX .GE. 0, ELSE LX = (-INCX)*N, and similarly for SY using LY and INCY. With SPARAM(1)=SFLAG, H has one of the following forms.. SFLAG=-1.E0 SFLAG=0.E0 SFLAG=1.E0 SFLAG=-2.E0 (SH11 SH12) (1.E0 SH12) (SH11 1.E0) (1.E0 0.E0) H=( ) ( ) ( ) ( ) (SH21 SH22), (SH21 1.E0), (-1.E0 SH22), (0.E0 1.E0). See SROTMG for a description of data storage in SPARAM.
subroutine srotm(n, x, incx, y, incy, param) integer :: n real(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasSrotm(n, x, incx, y, incy, param) integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy real(4), device :: param(*) ! device or host variable
integer(4) function cublasSrotm_v2(h, n, x, incx, y, incy, param) type(cublasHandle) :: h integer :: n real(4), device :: param(*) ! device or host variable real(4), device, dimension(*) :: x, y integer :: incx, incy
2.2.11. srotmg
SROTMG constructs the modified Givens transformation matrix H which zeros the second component of the 2-vector (SQRT(SD1)*SX1,SQRT(SD2)*SY2)**T. With SPARAM(1)=SFLAG, H has one of the following forms.. SFLAG=-1.E0 SFLAG=0.E0 SFLAG=1.E0 SFLAG=-2.E0 (SH11 SH12) (1.E0 SH12) (SH11 1.E0) (1.E0 0.E0) H=( ) ( ) ( ) ( ) (SH21 SH22), (SH21 1.E0), (-1.E0 SH22), (0.E0 1.E0). Locations 2-4 of SPARAM contain SH11,SH21,SH12, and SH22 respectively. (Values of 1.E0, -1.E0, or 0.E0 implied by the value of SPARAM(1) are not stored in SPARAM.)
subroutine srotmg(d1, d2, x1, y1, param) real(4), device :: d1, d2, x1, y1, param(*) ! device or host variable
subroutine cublasSrotmg(d1, d2, x1, y1, param) real(4), device :: d1, d2, x1, y1, param(*) ! device or host variable
integer(4) function cublasSrotmg_v2(h, d1, d2, x1, y1, param) type(cublasHandle) :: h real(4), device :: d1, d2, x1, y1, param(*) ! device or host variable
2.2.12. sscal
SSCAL scales a vector by a constant.
subroutine sscal(n, a, x, incx) integer :: n real(4), device :: a ! device or host variable real(4), device, dimension(*) :: x ! device or host variable integer :: incx
subroutine cublasSscal(n, a, x, incx) integer :: n real(4), device :: a ! device or host variable real(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasSscal_v2(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(4), device :: a ! device or host variable real(4), device, dimension(*) :: x integer :: incx
2.2.13. sswap
SSWAP interchanges two vectors.
subroutine sswap(n, x, incx, y, incy) integer :: n real(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasSswap(n, x, incx, y, incy) integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasSswap_v2(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
2.2.14. sgbmv
SGBMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n band matrix, with kl sub-diagonals and ku super-diagonals.
subroutine sgbmv(t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, kl, ku, lda, incx, incy real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x, y ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSgbmv(t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, kl, ku, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSgbmv_v2(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
2.2.15. sgemv
SGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix.
subroutine sgemv(t, m, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x, y ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSgemv(t, m, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSgemv_v2(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
2.2.16. sger
SGER performs the rank 1 operation A := alpha*x*y**T + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix.
subroutine sger(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x, y ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasSger(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha ! device or host variable
integer(4) function cublasSger_v2(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha ! device or host variable
2.2.17. ssbmv
SSBMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric band matrix, with k super-diagonals.
subroutine ssbmv(t, n, k, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: k, n, lda, incx, incy real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x, y ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSsbmv(t, n, k, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: k, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSsbmv_v2(h, t, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: k, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
2.2.18. sspmv
SSPMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix, supplied in packed form.
subroutine sspmv(t, n, alpha, a, x, incx, beta, y, incy) character*1 :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSspmv(t, n, alpha, a, x, incx, beta, y, incy) character*1 :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSspmv_v2(h, t, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y real(4), device :: alpha, beta ! device or host variable
2.2.19. sspr
SSPR performs the symmetric rank 1 operation A := alpha*x*x**T + A, where alpha is a real scalar, x is an n element vector and A is an n by n symmetric matrix, supplied in packed form.
subroutine sspr(t, n, alpha, x, incx, a) character*1 :: t integer :: n, incx real(4), device, dimension(*) :: a, x ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasSspr(t, n, alpha, x, incx, a) character*1 :: t integer :: n, incx real(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
integer(4) function cublasSspr_v2(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx real(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
2.2.20. sspr2
SSPR2 performs the symmetric rank 2 operation A := alpha*x*y**T + alpha*y*x**T + A, where alpha is a scalar, x and y are n element vectors and A is an n by n symmetric matrix, supplied in packed form.
subroutine sspr2(t, n, alpha, x, incx, y, incy, a) character*1 :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasSspr2(t, n, alpha, x, incx, y, incy, a) character*1 :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y real(4), device :: alpha ! device or host variable
integer(4) function cublasSspr2_v2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y real(4), device :: alpha ! device or host variable
2.2.21. ssymv
SSYMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix.
subroutine ssymv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x, y ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSsymv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSsymv_v2(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
2.2.22. ssyr
SSYR performs the symmetric rank 1 operation A := alpha*x*x**T + A, where alpha is a real scalar, x is an n element vector and A is an n by n symmetric matrix.
subroutine ssyr(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasSsyr(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x real(4), device :: alpha ! device or host variable
integer(4) function cublasSsyr_v2(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x real(4), device :: alpha ! device or host variable
2.2.23. ssyr2
SSYR2 performs the symmetric rank 2 operation A := alpha*x*y**T + alpha*y*x**T + A, where alpha is a scalar, x and y are n element vectors and A is an n by n symmetric matrix.
subroutine ssyr2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x, y ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasSsyr2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha ! device or host variable
integer(4) function cublasSsyr2_v2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha ! device or host variable
2.2.24. stbmv
STBMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals.
subroutine stbmv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x ! device or host variable
subroutine cublasStbmv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
integer(4) function cublasStbmv_v2(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.2.25. stbsv
STBSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine stbsv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x ! device or host variable
subroutine cublasStbsv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
integer(4) function cublasStbsv_v2(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.2.26. stpmv
STPMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form.
subroutine stpmv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x ! device or host variable
subroutine cublasStpmv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x
integer(4) function cublasStpmv_v2(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x
2.2.27. stpsv
STPSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine stpsv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x ! device or host variable
subroutine cublasStpsv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x
integer(4) function cublasStpsv_v2(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x
2.2.28. strmv
STRMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix.
subroutine strmv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x ! device or host variable
subroutine cublasStrmv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
integer(4) function cublasStrmv_v2(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.2.29. strsv
STRSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine strsv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(*) :: x ! device or host variable
subroutine cublasStrsv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
integer(4) function cublasStrsv_v2(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.2.30. sgemm
SGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
subroutine sgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: transa, transb integer :: m, n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(ldb, *) :: b ! device or host variable real(4), device, dimension(ldc, *) :: c ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: transa, transb integer :: m, n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSgemm_v2(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.2.31. ssymm
SSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices.
subroutine ssymm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(ldb, *) :: b ! device or host variable real(4), device, dimension(ldc, *) :: c ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSsymm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSsymm_v2(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.2.32. ssyrk
SSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
subroutine ssyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(ldc, *) :: c ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSsyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSsyrk_v2(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.2.33. ssyr2k
SSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
subroutine ssyr2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(ldb, *) :: b ! device or host variable real(4), device, dimension(ldc, *) :: c ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSsyr2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSsyr2k_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.2.34. ssyrkx
SSYRKX performs a variation of the symmetric rank k update C := alpha*A*B**T + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix stored in lower or upper mode, and A and B are n by k matrices. This routine can be used when B is in such a way that the result is guaranteed to be symmetric. See the CUBLAS documentation for more details.
subroutine ssyrkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(ldb, *) :: b ! device or host variable real(4), device, dimension(ldc, *) :: c ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasSsyrkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasSsyrkx_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.2.35. strmm
STRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ), where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T.
subroutine strmm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(ldb, *) :: b ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasStrmm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device :: alpha ! device or host variable
integer(4) function cublasStrmm_v2(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha ! device or host variable
2.2.36. strsm
STRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B.
subroutine strsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb real(4), device, dimension(lda, *) :: a ! device or host variable real(4), device, dimension(ldb, *) :: b ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasStrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device :: alpha ! device or host variable
integer(4) function cublasStrsm_v2(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device :: alpha ! device or host variable
2.2.37. cublasSgetrfBatched
SGETRF computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges. The factorization has the form A = P * L * U where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n). This is the right-looking Level 3 BLAS version of the algorithm.
integer(4) function cublasSgetrfBatched(h, n, Aarray, lda, ipvt, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) integer, device :: info(*) integer :: batchCount
2.2.38. cublasSgetriBatched
SGETRI computes the inverse of a matrix using the LU factorization computed by SGETRF. This method inverts U and then computes inv(A) by solving the system inv(A)*L = inv(U) for inv(A).
integer(4) function cublasSgetriBatched(h, n, Aarray, lda, ipvt, Carray, ldc, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Carray(*) integer :: ldc integer, device :: info(*) integer :: batchCount
2.2.39. cublasSgetrsBatched
SGETRS solves a system of linear equations A * X = B or A**T * X = B with a general N-by-N matrix A using the LU factorization computed by SGETRF.
integer(4) function cublasSgetrsBatched(h, trans, n, nrhs, Aarray, lda, ipvt, Barray, ldb, info, batchCount) type(cublasHandle) :: h integer :: trans ! integer or character(1) variable integer :: n, nrhs type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Barray(*) integer :: ldb integer :: info(*) integer :: batchCount
2.2.40. cublasSgemmBatched
SGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
integer(4) function cublasSgemmBatched(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa ! integer or character(1) variable integer :: transb ! integer or character(1) variable integer :: m, n, k real(4), device :: alpha ! device or host variable type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb real(4), device :: beta ! device or host variable type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
integer(4) function cublasSgemmBatched_v2(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa integer :: transb integer :: m, n, k real(4), device :: alpha ! device or host variable type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb real(4), device :: beta ! device or host variable type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
2.2.41. cublasStrsmBatched
STRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B.
integer(4) function cublasStrsmBatched( h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side ! integer or character(1) variable integer :: uplo ! integer or character(1) variable integer :: trans ! integer or character(1) variable integer :: diag ! integer or character(1) variable integer :: m, n real(4), device :: alpha ! device or host variable type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
integer(4) function cublasStrsmBatched_v2( h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side integer :: uplo integer :: trans integer :: diag integer :: m, n real(4), device :: alpha ! device or host variable type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
2.2.42. cublasSmatinvBatched
cublasSmatinvBatched is a short cut of cublasSgetrfBatched plus cublasSgetriBatched. However it only works if n is less than 32. If not, the user has to go through cublasSgetrfBatched and cublasSgetriBatched.
integer(4) function cublasSmatinvBatched(h, n, Aarray, lda, Ainv, lda_inv, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Ainv(*) integer :: lda_inv integer, device :: info(*) integer :: batchCount
2.2.43. cublasSgeqrfBatched
SGEQRF computes a QR factorization of a real M-by-N matrix A: A = Q * R.
integer(4) function cublasSgeqrfBatched(h, m, n, Aarray, lda, Tau, info, batchCount) type(cublasHandle) :: h integer :: m, n type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Tau(*) integer :: info(*) integer :: batchCount
2.2.44. cublasSgelsBatched
SGELS solves overdetermined or underdetermined real linear systems involving an M-by-N matrix A, or its transpose, using a QR or LQ factorization of A. It is assumed that A has full rank. The following options are provided: 1. If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A*X ||. 2. If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B. 3. If TRANS = 'T' and m >= n: find the minimum norm solution of an undetermined system A**T * X = B. 4. If TRANS = 'T' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A**T * X ||. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X.
integer(4) function cublasSgelsBatched(h, trans, m, n, nrhs, Aarray, lda, Carray, ldc, info, devinfo, batchCount) type(cublasHandle) :: h integer :: trans ! integer or character(1) variable integer :: m, n, nrhs type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Carray(*) integer :: ldc integer :: info(*) integer, device :: devinfo(*) integer :: batchCount
2.3. Double Precision Functions and Subroutines
This section contains interfaces to the double precision BLAS and cuBLAS functions and subroutines.
2.3.1. idamax
IDAMAX finds the the index of the element having the maximum absolute value.
integer(4) function idamax(n, x, incx) integer :: n real(8), device, dimension(*) :: x ! device or host variable integer :: incx
integer(4) function cublasIdamax(n, x, incx) integer :: n real(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasIdamax_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.3.2. idamin
IDAMIN finds the index of the element having the minimum absolute value.
integer(4) function idamin(n, x, incx) integer :: n real(8), device, dimension(*) :: x ! device or host variable integer :: incx
integer(4) function cublasIdamin(n, x, incx) integer :: n real(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasIdamin_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.3.3. dasum
DASUM takes the sum of the absolute values.
real(8) function dasum(n, x, incx) integer :: n real(8), device, dimension(*) :: x ! device or host variable integer :: incx
real(8) function cublasDasum(n, x, incx) integer :: n real(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasDasum_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx real(8), device :: res ! device or host variable
2.3.4. daxpy
DAXPY constant times a vector plus a vector.
subroutine daxpy(n, a, x, incx, y, incy) integer :: n real(8), device :: a ! device or host variable real(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasDaxpy(n, a, x, incx, y, incy) integer :: n real(8), device :: a ! device or host variable real(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasDaxpy_v2(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8), device :: a ! device or host variable real(8), device, dimension(*) :: x, y integer :: incx, incy
2.3.5. dcopy
DCOPY copies a vector, x, to a vector, y.
subroutine dcopy(n, x, incx, y, incy) integer :: n real(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasDcopy(n, x, incx, y, incy) integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasDcopy_v2(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
2.3.6. ddot
DDOT forms the dot product of two vectors.
real(8) function ddot(n, x, incx, y, incy) integer :: n real(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
real(8) function cublasDdot(n, x, incx, y, incy) integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasDdot_v2(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy real(8), device :: res ! device or host variable
2.3.7. dnrm2
DNRM2 returns the euclidean norm of a vector via the function name, so that DNRM2 := sqrt( x'*x )
real(8) function dnrm2(n, x, incx) integer :: n real(8), device, dimension(*) :: x ! device or host variable integer :: incx
real(8) function cublasDnrm2(n, x, incx) integer :: n real(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasDnrm2_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx real(8), device :: res ! device or host variable
2.3.8. drot
DROT applies a plane rotation.
subroutine drot(n, x, incx, y, incy, sc, ss) integer :: n real(8), device :: sc, ss ! device or host variable real(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasDrot(n, x, incx, y, incy, sc, ss) integer :: n real(8), device :: sc, ss ! device or host variable real(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasDrot_v2(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(8), device :: sc, ss ! device or host variable real(8), device, dimension(*) :: x, y integer :: incx, incy
2.3.9. drotg
DROTG constructs a Givens plane rotation.
subroutine drotg(sa, sb, sc, ss) real(8), device :: sa, sb, sc, ss ! device or host variable
subroutine cublasDrotg(sa, sb, sc, ss) real(8), device :: sa, sb, sc, ss ! device or host variable
integer(4) function cublasDrotg_v2(h, sa, sb, sc, ss) type(cublasHandle) :: h real(8), device :: sa, sb, sc, ss ! device or host variable
2.3.10. drotm
DROTM applies the modified Givens transformation, H, to the 2 by N matrix (DX**T) , where **T indicates transpose. The elements of DX are in (DX**T) DX(LX+I*INCX), I = 0 to N-1, where LX = 1 if INCX .GE. 0, ELSE LX = (-INCX)*N, and similarly for DY using LY and INCY. With DPARAM(1)=DFLAG, H has one of the following forms.. DFLAG=-1.D0 DFLAG=0.D0 DFLAG=1.D0 DFLAG=-2.D0 (DH11 DH12) (1.D0 DH12) (DH11 1.D0) (1.D0 0.D0) H=( ) ( ) ( ) ( ) (DH21 DH22), (DH21 1.D0), (-1.D0 DH22), (0.D0 1.D0). See DROTMG for a description of data storage in DPARAM.
subroutine drotm(n, x, incx, y, incy, param) integer :: n real(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasDrotm(n, x, incx, y, incy, param) integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy real(8), device :: param(*) ! device or host variable
integer(4) function cublasDrotm_v2(h, n, x, incx, y, incy, param) type(cublasHandle) :: h integer :: n real(8), device :: param(*) ! device or host variable real(8), device, dimension(*) :: x, y integer :: incx, incy
2.3.11. drotmg
DROTMG constructs the modified Givens transformation matrix H which zeros the second component of the 2-vector (SQRT(DD1)*DX1,SQRT(DD2)*DY2)**T. With DPARAM(1)=DFLAG, H has one of the following forms.. DFLAG=-1.D0 DFLAG=0.D0 DFLAG=1.D0 DFLAG=-2.D0 (DH11 DH12) (1.D0 DH12) (DH11 1.D0) (1.D0 0.D0) H=( ) ( ) ( ) ( ) (DH21 DH22), (DH21 1.D0), (-1.D0 DH22), (0.D0 1.D0). Locations 2-4 of DPARAM contain DH11, DH21, DH12, and DH22 respectively. (Values of 1.D0, -1.D0, of 0.D0 implied by the value of DPARAM(1) are not stored in DPARAM.)
subroutine drotmg(d1, d2, x1, y1, param) real(8), device :: d1, d2, x1, y1, param(*) ! device or host variable
subroutine cublasDrotmg(d1, d2, x1, y1, param) real(8), device :: d1, d2, x1, y1, param(*) ! device or host variable
integer(4) function cublasDrotmg_v2(h, d1, d2, x1, y1, param) type(cublasHandle) :: h real(8), device :: d1, d2, x1, y1, param(*) ! device or host variable
2.3.12. dscal
DSCAL scales a vector by a constant.
subroutine dscal(n, a, x, incx) integer :: n real(8), device :: a ! device or host variable real(8), device, dimension(*) :: x ! device or host variable integer :: incx
subroutine cublasDscal(n, a, x, incx) integer :: n real(8), device :: a ! device or host variable real(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasDscal_v2(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(8), device :: a ! device or host variable real(8), device, dimension(*) :: x integer :: incx
2.3.13. dswap
interchanges two vectors.
subroutine dswap(n, x, incx, y, incy) integer :: n real(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasDswap(n, x, incx, y, incy) integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasDswap_v2(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
2.3.14. dgbmv
DGBMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n band matrix, with kl sub-diagonals and ku super-diagonals.
subroutine dgbmv(t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, kl, ku, lda, incx, incy real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x, y ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDgbmv(t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, kl, ku, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDgbmv_v2(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
2.3.15. dgemv
DGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix.
subroutine dgemv(t, m, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x, y ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDgemv(t, m, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDgemv_v2(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
2.3.16. dger
DGER performs the rank 1 operation A := alpha*x*y**T + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix.
subroutine dger(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x, y ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasDger(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha ! device or host variable
integer(4) function cublasDger_v2(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha ! device or host variable
2.3.17. dsbmv
DSBMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric band matrix, with k super-diagonals.
subroutine dsbmv(t, n, k, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: k, n, lda, incx, incy real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x, y ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDsbmv(t, n, k, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: k, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDsbmv_v2(h, t, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: k, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
2.3.18. dspmv
DSPMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix, supplied in packed form.
subroutine dspmv(t, n, alpha, a, x, incx, beta, y, incy) character*1 :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDspmv(t, n, alpha, a, x, incx, beta, y, incy) character*1 :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDspmv_v2(h, t, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y real(8), device :: alpha, beta ! device or host variable
2.3.19. dspr
DSPR performs the symmetric rank 1 operation A := alpha*x*x**T + A, where alpha is a real scalar, x is an n element vector and A is an n by n symmetric matrix, supplied in packed form.
subroutine dspr(t, n, alpha, x, incx, a) character*1 :: t integer :: n, incx real(8), device, dimension(*) :: a, x ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasDspr(t, n, alpha, x, incx, a) character*1 :: t integer :: n, incx real(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
integer(4) function cublasDspr_v2(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx real(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
2.3.20. dspr2
DSPR2 performs the symmetric rank 2 operation A := alpha*x*y**T + alpha*y*x**T + A, where alpha is a scalar, x and y are n element vectors and A is an n by n symmetric matrix, supplied in packed form.
subroutine dspr2(t, n, alpha, x, incx, y, incy, a) character*1 :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasDspr2(t, n, alpha, x, incx, y, incy, a) character*1 :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y real(8), device :: alpha ! device or host variable
integer(4) function cublasDspr2_v2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y real(8), device :: alpha ! device or host variable
2.3.21. dsymv
DSYMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix.
subroutine dsymv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x, y ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDsymv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDsymv_v2(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
2.3.22. dsyr
DSYR performs the symmetric rank 1 operation A := alpha*x*x**T + A, where alpha is a real scalar, x is an n element vector and A is an n by n symmetric matrix.
subroutine dsyr(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasDsyr(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x real(8), device :: alpha ! device or host variable
integer(4) function cublasDsyr_v2(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x real(8), device :: alpha ! device or host variable
2.3.23. dsyr2
DSYR2 performs the symmetric rank 2 operation A := alpha*x*y**T + alpha*y*x**T + A, where alpha is a scalar, x and y are n element vectors and A is an n by n symmetric matrix.
subroutine dsyr2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x, y ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasDsyr2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha ! device or host variable
integer(4) function cublasDsyr2_v2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha ! device or host variable
2.3.24. dtbmv
DTBMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals.
subroutine dtbmv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x ! device or host variable
subroutine cublasDtbmv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
integer(4) function cublasDtbmv_v2(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.3.25. dtbsv
DTBSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine dtbsv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x ! device or host variable
subroutine cublasDtbsv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
integer(4) function cublasDtbsv_v2(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.3.26. dtpmv
DTPMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form.
subroutine dtpmv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x ! device or host variable
subroutine cublasDtpmv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x
integer(4) function cublasDtpmv_v2(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x
2.3.27. dtpsv
DTPSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine dtpsv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x ! device or host variable
subroutine cublasDtpsv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x
integer(4) function cublasDtpsv_v2(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x
2.3.28. dtrmv
DTRMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix.
subroutine dtrmv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x ! device or host variable
subroutine cublasDtrmv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
integer(4) function cublasDtrmv_v2(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.3.29. dtrsv
DTRSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine dtrsv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(*) :: x ! device or host variable
subroutine cublasDtrsv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
integer(4) function cublasDtrsv_v2(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.3.30. dgemm
DGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
subroutine dgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: transa, transb integer :: m, n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(ldb, *) :: b ! device or host variable real(8), device, dimension(ldc, *) :: c ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: transa, transb integer :: m, n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDgemm_v2(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.3.31. dsymm
DSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices.
subroutine dsymm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(ldb, *) :: b ! device or host variable real(8), device, dimension(ldc, *) :: c ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDsymm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDsymm_v2(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.3.32. dsyrk
DSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
subroutine dsyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(ldc, *) :: c ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDsyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDsyrk_v2(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.3.33. dsyr2k
DSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
subroutine dsyr2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(ldb, *) :: b ! device or host variable real(8), device, dimension(ldc, *) :: c ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDsyr2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDsyr2k_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.3.34. dsyrkx
DSYRKX performs a variation of the symmetric rank k update C := alpha*A*B**T + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix stored in lower or upper mode, and A and B are n by k matrices. This routine can be used when B is in such a way that the result is guaranteed to be symmetric. See the CUBLAS documentation for more details.
subroutine dsyrkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(ldb, *) :: b ! device or host variable real(8), device, dimension(ldc, *) :: c ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasDsyrkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasDsyrkx_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.3.35. dtrmm
DTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ), where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T.
subroutine dtrmm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(ldb, *) :: b ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasDtrmm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device :: alpha ! device or host variable
integer(4) function cublasDtrmm_v2(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha ! device or host variable
2.3.36. dtrsm
DTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B.
subroutine dtrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb real(8), device, dimension(lda, *) :: a ! device or host variable real(8), device, dimension(ldb, *) :: b ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasDtrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device :: alpha ! device or host variable
integer(4) function cublasDtrsm_v2(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device :: alpha ! device or host variable
2.3.37. cublasDgetrfBatched
DGETRF computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges. The factorization has the form A = P * L * U where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n). This is the right-looking Level 3 BLAS version of the algorithm.
integer(4) function cublasDgetrfBatched(h, n, Aarray, lda, ipvt, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) integer, device :: info(*) integer :: batchCount
2.3.38. cublasDgetriBatched
DGETRI computes the inverse of a matrix using the LU factorization computed by DGETRF. This method inverts U and then computes inv(A) by solving the system inv(A)*L = inv(U) for inv(A).
integer(4) function cublasDgetriBatched(h, n, Aarray, lda, ipvt, Carray, ldc, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Carray(*) integer :: ldc integer, device :: info(*) integer :: batchCount
2.3.39. cublasDgetrsBatched
DGETRS solves a system of linear equations A * X = B or A**T * X = B with a general N-by-N matrix A using the LU factorization computed by DGETRF.
integer(4) function cublasDgetrsBatched(h, trans, n, nrhs, Aarray, lda, ipvt, Barray, ldb, info, batchCount) type(cublasHandle) :: h integer :: trans ! integer or character(1) variable integer :: n, nrhs type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Barray(*) integer :: ldb integer :: info(*) integer :: batchCount
2.3.40. cublasDgemmBatched
DGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
integer(4) function cublasDgemmBatched(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa ! integer or character(1) variable integer :: transb ! integer or character(1) variable integer :: m, n, k real(8), device :: alpha ! device or host variable type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb real(8), device :: beta ! device or host variable type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
integer(4) function cublasDgemmBatched_v2(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa integer :: transb integer :: m, n, k real(8), device :: alpha ! device or host variable type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb real(8), device :: beta ! device or host variable type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
2.3.41. cublasDtrsmBatched
DTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B.
integer(4) function cublasDtrsmBatched( h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side ! integer or character(1) variable integer :: uplo ! integer or character(1) variable integer :: trans ! integer or character(1) variable integer :: diag ! integer or character(1) variable integer :: m, n real(8), device :: alpha ! device or host variable type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
integer(4) function cublasDtrsmBatched_v2( h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side integer :: uplo integer :: trans integer :: diag integer :: m, n real(8), device :: alpha ! device or host variable type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
2.3.42. cublasDmatinvBatched
cublasDmatinvBatched is a short cut of cublasDgetrfBatched plus cublasDgetriBatched. However it only works if n is less than 32. If not, the user has to go through cublasDgetrfBatched and cublasDgetriBatched.
integer(4) function cublasDmatinvBatched(h, n, Aarray, lda, Ainv, lda_inv, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Ainv(*) integer :: lda_inv integer, device :: info(*) integer :: batchCount
2.3.43. cublasDgeqrfBatched
DGEQRF computes a QR factorization of a real M-by-N matrix A: A = Q * R.
integer(4) function cublasDgeqrfBatched(h, m, n, Aarray, lda, Tau, info, batchCount) type(cublasHandle) :: h integer :: m, n type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Tau(*) integer :: info(*) integer :: batchCount
2.3.44. cublasDgelsBatched
DGELS solves overdetermined or underdetermined real linear systems involving an M-by-N matrix A, or its transpose, using a QR or LQ factorization of A. It is assumed that A has full rank. The following options are provided: 1. If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A*X ||. 2. If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B. 3. If TRANS = 'T' and m >= n: find the minimum norm solution of an undetermined system A**T * X = B. 4. If TRANS = 'T' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A**T * X ||. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X.
integer(4) function cublasDgelsBatched(h, trans, m, n, nrhs, Aarray, lda, Carray, ldc, info, devinfo, batchCount) type(cublasHandle) :: h integer :: trans ! integer or character(1) variable integer :: m, n, nrhs type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Carray(*) integer :: ldc integer :: info(*) integer, device :: devinfo(*) integer :: batchCount
2.4. Single Precision Complex Functions and Subroutines
This section contains interfaces to the single precision complex BLAS and cuBLAS functions and subroutines.
2.4.1. icamax
ICAMAX finds the index of the element having the maximum absolute value.
integer(4) function icamax(n, x, incx) integer :: n complex(4), device, dimension(*) :: x ! device or host variable integer :: incx
integer(4) function cublasIcamax(n, x, incx) integer :: n complex(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasIcamax_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.4.2. icamin
ICAMIN finds the index of the element having the minimum absolute value.
integer(4) function icamin(n, x, incx) integer :: n complex(4), device, dimension(*) :: x ! device or host variable integer :: incx
integer(4) function cublasIcamin(n, x, incx) integer :: n complex(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasIcamin_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.4.3. scasum
SCASUM takes the sum of the absolute values of a complex vector and returns a single precision result.
real(4) function scasum(n, x, incx) integer :: n complex(4), device, dimension(*) :: x ! device or host variable integer :: incx
real(4) function cublasScasum(n, x, incx) integer :: n complex(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasScasum_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx real(4), device :: res ! device or host variable
2.4.4. caxpy
CAXPY constant times a vector plus a vector.
subroutine caxpy(n, a, x, incx, y, incy) integer :: n complex(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasCaxpy(n, a, x, incx, y, incy) integer :: n complex(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasCaxpy_v2(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.4.5. ccopy
CCOPY copies a vector x to a vector y.
subroutine ccopy(n, x, incx, y, incy) integer :: n complex(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasCcopy(n, x, incx, y, incy) integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasCcopy_v2(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.4.6. cdotc
forms the dot product of two vectors, conjugating the first vector.
complex(4) function cdotc(n, x, incx, y, incy) integer :: n complex(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
complex(4) function cublasCdotc(n, x, incx, y, incy) integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasCdotc_v2(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy complex(4), device :: res ! device or host variable
2.4.7. cdotu
CDOTU forms the dot product of two vectors.
complex(4) function cdotu(n, x, incx, y, incy) integer :: n complex(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
complex(4) function cublasCdotu(n, x, incx, y, incy) integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasCdotu_v2(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy complex(4), device :: res ! device or host variable
2.4.8. scnrm2
SCNRM2 returns the euclidean norm of a vector via the function name, so that SCNRM2 := sqrt( x**H*x )
real(4) function scnrm2(n, x, incx) integer :: n complex(4), device, dimension(*) :: x ! device or host variable integer :: incx
real(4) function cublasScnrm2(n, x, incx) integer :: n complex(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasScnrm2_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx real(4), device :: res ! device or host variable
2.4.9. crot
CROT applies a plane rotation, where the cos (C) is real and the sin (S) is complex, and the vectors CX and CY are complex.
subroutine crot(n, x, incx, y, incy, sc, ss) integer :: n real(4), device :: sc ! device or host variable complex(4), device :: ss ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasCrot(n, x, incx, y, incy, sc, ss) integer :: n real(4), device :: sc ! device or host variable complex(4), device :: ss ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasCrot_v2(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(4), device :: sc ! device or host variable complex(4), device :: ss ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.4.10. csrot
CSROT applies a plane rotation, where the cos and sin (c and s) are real and the vectors cx and cy are complex.
subroutine csrot(n, x, incx, y, incy, sc, ss) integer :: n real(4), device :: sc, ss ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasCsrot(n, x, incx, y, incy, sc, ss) integer :: n real(4), device :: sc, ss ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasCsrot_v2(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(4), device :: sc, ss ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.4.11. crotg
CROTG determines a complex Givens rotation.
subroutine crotg(sa, sb, sc, ss) complex(4), device :: sa, sb, ss ! device or host variable real(4), device :: sc ! device or host variable
subroutine cublasCrotg(sa, sb, sc, ss) complex(4), device :: sa, sb, ss ! device or host variable real(4), device :: sc ! device or host variable
integer(4) function cublasCrotg_v2(h, sa, sb, sc, ss) type(cublasHandle) :: h complex(4), device :: sa, sb, ss ! device or host variable real(4), device :: sc ! device or host variable
2.4.12. cscal
CSCAL scales a vector by a constant.
subroutine cscal(n, a, x, incx) integer :: n complex(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x ! device or host variable integer :: incx
subroutine cublasCscal(n, a, x, incx) integer :: n complex(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasCscal_v2(h, n, a, x, incx) type(cublasHandle) :: h integer :: n complex(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x integer :: incx
2.4.13. csscal
CSSCAL scales a complex vector by a real constant.
subroutine csscal(n, a, x, incx) integer :: n real(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x ! device or host variable integer :: incx
subroutine cublasCsscal(n, a, x, incx) integer :: n real(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x integer :: incx
integer(4) function cublasCsscal_v2(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x integer :: incx
2.4.14. cswap
CSWAP interchanges two vectors.
subroutine cswap(n, x, incx, y, incy) integer :: n complex(4), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasCswap(n, x, incx, y, incy) integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasCswap_v2(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.4.15. cgbmv
CGBMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n band matrix, with kl sub-diagonals and ku super-diagonals.
subroutine cgbmv(t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, kl, ku, lda, incx, incy complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasCgbmv(t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, kl, ku, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCgbmv_v2(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.4.16. cgemv
CGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix.
subroutine cgemv(t, m, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasCgemv(t, m, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCgemv_v2(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.4.17. cgerc
CGERC performs the rank 1 operation A := alpha*x*y**H + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix.
subroutine cgerc(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable complex(4), device :: alpha ! device or host variable
subroutine cublasCgerc(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
integer(4) function cublasCgerc_v2(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
2.4.18. cgeru
CGERU performs the rank 1 operation A := alpha*x*y**T + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix.
subroutine cgeru(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable complex(4), device :: alpha ! device or host variable
subroutine cublasCgeru(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
integer(4) function cublasCgeru_v2(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
2.4.19. csymv
CSYMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix.
subroutine csymv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasCsymv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCsymv_v2(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.4.20. csyr
CSYR performs the symmetric rank 1 operation A := alpha*x*x**H + A, where alpha is a complex scalar, x is an n element vector and A is an n by n symmetric matrix.
subroutine csyr(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x ! device or host variable complex(4), device :: alpha ! device or host variable
subroutine cublasCsyr(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x complex(4), device :: alpha ! device or host variable
integer(4) function cublasCsyr_v2(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x complex(4), device :: alpha ! device or host variable
2.4.21. csyr2
CSYR2 performs the symmetric rank 2 operation A := alpha*x*y' + alpha*y*x' + A, where alpha is a complex scalar, x and y are n element vectors and A is an n by n SY matrix.
subroutine csyr2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable complex(4), device :: alpha ! device or host variable
subroutine cublasCsyr2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
integer(4) function cublasCsyr2_v2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
2.4.22. ctbmv
CTBMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals.
subroutine ctbmv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x ! device or host variable
subroutine cublasCtbmv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
integer(4) function cublasCtbmv_v2(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.4.23. ctbsv
CTBSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine ctbsv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x ! device or host variable
subroutine cublasCtbsv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
integer(4) function cublasCtbsv_v2(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.4.24. ctpmv
CTPMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form.
subroutine ctpmv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x ! device or host variable
subroutine cublasCtpmv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x
integer(4) function cublasCtpmv_v2(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x
2.4.25. ctpsv
CTPSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine ctpsv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x ! device or host variable
subroutine cublasCtpsv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x
integer(4) function cublasCtpsv_v2(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x
2.4.26. ctrmv
CTRMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix.
subroutine ctrmv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x ! device or host variable
subroutine cublasCtrmv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
integer(4) function cublasCtrmv_v2(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.4.27. ctrsv
CTRSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine ctrsv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x ! device or host variable
subroutine cublasCtrsv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
integer(4) function cublasCtrsv_v2(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.4.28. chbmv
CHBMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian band matrix, with k super-diagonals.
subroutine chbmv(uplo, n, k, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: k, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasChbmv(uplo, n, k, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: k, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasChbmv_v2(h, uplo, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: k, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.4.29. chemv
CHEMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian matrix.
subroutine chemv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(*) :: x, y ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasChemv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasChemv_v2(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.4.30. chpmv
CHPMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian matrix, supplied in packed form.
subroutine chpmv(uplo, n, alpha, a, x, incx, beta, y, incy) character*1 :: uplo integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasChpmv(uplo, n, alpha, a, x, incx, beta, y, incy) character*1 :: uplo integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasChpmv_v2(h, uplo, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha, beta ! device or host variable
2.4.31. cher
CHER performs the hermitian rank 1 operation A := alpha*x*x**H + A, where alpha is a real scalar, x is an n element vector and A is an n by n hermitian matrix.
subroutine cher(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda complex(4), device, dimension(*) :: a, x ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasCher(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda complex(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
integer(4) function cublasCher_v2(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
2.4.32. cher2
CHER2 performs the hermitian rank 2 operation A := alpha*x*y**H + conjg( alpha )*y*x**H + A, where alpha is a scalar, x and y are n element vectors and A is an n by n hermitian matrix.
subroutine cher2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda complex(4), device, dimension(*) :: a, x, y ! device or host variable complex(4), device :: alpha ! device or host variable
subroutine cublasCher2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha ! device or host variable
integer(4) function cublasCher2_v2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha ! device or host variable
2.4.33. chpr
CHPR performs the hermitian rank 1 operation A := alpha*x*x**H + A, where alpha is a real scalar, x is an n element vector and A is an n by n hermitian matrix, supplied in packed form.
subroutine chpr(t, n, alpha, x, incx, a) character*1 :: t integer :: n, incx complex(4), device, dimension(*) :: a, x ! device or host variable real(4), device :: alpha ! device or host variable
subroutine cublasChpr(t, n, alpha, x, incx, a) character*1 :: t integer :: n, incx complex(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
integer(4) function cublasChpr_v2(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx complex(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
2.4.34. chpr2
CHPR2 performs the hermitian rank 2 operation A := alpha*x*y**H + conjg( alpha )*y*x**H + A, where alpha is a scalar, x and y are n element vectors and A is an n by n hermitian matrix, supplied in packed form.
subroutine chpr2(t, n, alpha, x, incx, y, incy, a) character*1 :: t integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y ! device or host variable complex(4), device :: alpha ! device or host variable
subroutine cublasChpr2(t, n, alpha, x, incx, y, incy, a) character*1 :: t integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha ! device or host variable
integer(4) function cublasChpr2_v2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha ! device or host variable
2.4.35. cgemm
CGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
subroutine cgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasCgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCgemm_v2(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.4.36. csymm
CSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices.
subroutine csymm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasCsymm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCsymm_v2(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.4.37. csyrk
CSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
subroutine csyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasCsyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCsyrk_v2(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.4.38. csyr2k
CSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
subroutine csyr2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasCsyr2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCsyr2k_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.4.39. csyrkx
CSYRKX performs a variation of the symmetric rank k update C := alpha*A*B**T + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix stored in lower or upper mode, and A and B are n by k matrices. This routine can be used when B is in such a way that the result is guaranteed to be symmetric. See the CUBLAS documentation for more details.
subroutine csyrkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasCsyrkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCsyrkx_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.4.40. ctrmm
CTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H.
subroutine ctrmm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device :: alpha ! device or host variable
subroutine cublasCtrmm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device :: alpha ! device or host variable
integer(4) function cublasCtrmm_v2(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha ! device or host variable
2.4.41. ctrsm
CTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B.
subroutine ctrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device :: alpha ! device or host variable
subroutine cublasCtrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device :: alpha ! device or host variable
integer(4) function cublasCtrsm_v2(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device :: alpha ! device or host variable
2.4.42. chemm
CHEMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is an hermitian matrix and B and C are m by n matrices.
subroutine chemm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable complex(4), device :: alpha, beta ! device or host variable
subroutine cublasChemm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
integer(4) function cublasChemm_v2(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.4.43. cherk
CHERK performs one of the hermitian rank k operations C := alpha*A*A**H + beta*C, or C := alpha*A**H*A + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
subroutine cherk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable real(4), device :: alpha, beta ! device or host variable
subroutine cublasCherk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
integer(4) function cublasCherk_v2(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.4.44. cher2k
CHER2K performs one of the hermitian rank 2k operations C := alpha*A*B**H + conjg( alpha )*B*A**H + beta*C, or C := alpha*A**H*B + conjg( alpha )*B**H*A + beta*C, where alpha and beta are scalars with beta real, C is an n by n hermitian matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
subroutine cher2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable complex(4), device :: alpha ! device or host variable real(4), device :: beta ! device or host variable
subroutine cublasCher2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha ! device or host variable real(4), device :: beta ! device or host variable
integer(4) function cublasCher2k_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha ! device or host variable real(4), device :: beta ! device or host variable
2.4.45. cherkx
CHERKX performs a variation of the hermitian rank k operations C := alpha*A*B**H + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix stored in lower or upper mode, and A and B are n by k matrices. See the CUBLAS documentation for more details.
subroutine cherkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a ! device or host variable complex(4), device, dimension(ldb, *) :: b ! device or host variable complex(4), device, dimension(ldc, *) :: c ! device or host variable complex(4), device :: alpha ! device or host variable real(4), device :: beta ! device or host variable
subroutine cublasCherkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha ! device or host variable real(4), device :: beta ! device or host variable
integer(4) function cublasCherkx_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha ! device or host variable real(4), device :: beta ! device or host variable
2.4.46. cublasCgetrfBatched
CGETRF computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges. The factorization has the form A = P * L * U where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n). This is the right-looking Level 3 BLAS version of the algorithm.
integer(4) function cublasCgetrfBatched(h, n, Aarray, lda, ipvt, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) integer, device :: info(*) integer :: batchCount
2.4.47. cublasCgetriBatched
CGETRI computes the inverse of a matrix using the LU factorization computed by CGETRF. This method inverts U and then computes inv(A) by solving the system inv(A)*L = inv(U) for inv(A).
integer(4) function cublasCgetriBatched(h, n, Aarray, lda, ipvt, Carray, ldc, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Carray(*) integer :: ldc integer, device :: info(*) integer :: batchCount
2.4.48. cublasCgetrsBatched
CGETRS solves a system of linear equations A * X = B, A**T * X = B, or A**H * X = B with a general N-by-N matrix A using the LU factorization computed by CGETRF.
integer(4) function cublasCgetrsBatched(h, trans, n, nrhs, Aarray, lda, ipvt, Barray, ldb, info, batchCount) type(cublasHandle) :: h integer :: trans ! integer or character(1) variable integer :: n, nrhs type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Barray(*) integer :: ldb integer :: info(*) integer :: batchCount
2.4.49. cublasCgemmBatched
CGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
integer(4) function cublasCgemmBatched(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa ! integer or character(1) variable integer :: transb ! integer or character(1) variable integer :: m, n, k complex(4), device :: alpha ! device or host variable type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb complex(4), device :: beta ! device or host variable type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
integer(4) function cublasCgemmBatched_v2(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa integer :: transb integer :: m, n, k complex(4), device :: alpha ! device or host variable type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb complex(4), device :: beta ! device or host variable type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
2.4.50. cublasCtrsmBatched
CTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B.
integer(4) function cublasCtrsmBatched( h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side ! integer or character(1) variable integer :: uplo ! integer or character(1) variable integer :: trans ! integer or character(1) variable integer :: diag ! integer or character(1) variable integer :: m, n complex(4), device :: alpha ! device or host variable type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
integer(4) function cublasCtrsmBatched_v2( h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side integer :: uplo integer :: trans integer :: diag integer :: m, n complex(4), device :: alpha ! device or host variable type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
2.4.51. cublasCmatinvBatched
cublasCmatinvBatched is a short cut of cublasCgetrfBatched plus cublasCgetriBatched. However it only works if n is less than 32. If not, the user has to go through cublasCgetrfBatched and cublasCgetriBatched.
integer(4) function cublasCmatinvBatched(h, n, Aarray, lda, Ainv, lda_inv, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Ainv(*) integer :: lda_inv integer, device :: info(*) integer :: batchCount
2.4.52. cublasCgeqrfBatched
CGEQRF computes a QR factorization of a complex M-by-N matrix A: A = Q * R.
integer(4) function cublasCgeqrfBatched(h, m, n, Aarray, lda, Tau, info, batchCount) type(cublasHandle) :: h integer :: m, n type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Tau(*) integer :: info(*) integer :: batchCount
2.4.53. cublasCgelsBatched
CGELS solves overdetermined or underdetermined complex linear systems involving an M-by-N matrix A, or its conjugate-transpose, using a QR or LQ factorization of A. It is assumed that A has full rank. The following options are provided: 1. If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A*X ||. 2. If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B. 3. If TRANS = 'C' and m >= n: find the minimum norm solution of an undetermined system A**H * X = B. 4. If TRANS = 'C' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A**H * X ||. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X.
integer(4) function cublasCgelsBatched(h, trans, m, n, nrhs, Aarray, lda, Carray, ldc, info, devinfo, batchCount) type(cublasHandle) :: h integer :: trans ! integer or character(1) variable integer :: m, n, nrhs type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Carray(*) integer :: ldc integer :: info(*) integer, device :: devinfo(*) integer :: batchCount
2.5. Double Precision Complex Functions and Subroutines
This section contains interfaces to the double precision complex BLAS and cuBLAS functions and subroutines.
2.5.1. izamax
IZAMAX finds the index of the element having the maximum absolute value.
integer(4) function izamax(n, x, incx) integer :: n complex(8), device, dimension(*) :: x ! device or host variable integer :: incx
integer(4) function cublasIzamax(n, x, incx) integer :: n complex(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasIzamax_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.5.2. izamin
IZAMIN finds the index of the element having the minimum absolute value.
integer(4) function izamin(n, x, incx) integer :: n complex(8), device, dimension(*) :: x ! device or host variable integer :: incx
integer(4) function cublasIzamin(n, x, incx) integer :: n complex(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasIzamin_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.5.3. dzasum
DZASUM takes the sum of the absolute values.
real(8) function dzasum(n, x, incx) integer :: n complex(8), device, dimension(*) :: x ! device or host variable integer :: incx
real(8) function cublasDzasum(n, x, incx) integer :: n complex(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasDzasum_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx real(8), device :: res ! device or host variable
2.5.4. zaxpy
ZAXPY constant times a vector plus a vector.
subroutine zaxpy(n, a, x, incx, y, incy) integer :: n complex(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasZaxpy(n, a, x, incx, y, incy) integer :: n complex(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasZaxpy_v2(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.5.5. zcopy
ZCOPY copies a vector, x, to a vector, y.
subroutine zcopy(n, x, incx, y, incy) integer :: n complex(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasZcopy(n, x, incx, y, incy) integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasZcopy_v2(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.5.6. zdotc
ZDOTC forms the dot product of a vector.
complex(8) function zdotc(n, x, incx, y, incy) integer :: n complex(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
complex(8) function cublasZdotc(n, x, incx, y, incy) integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasZdotc_v2(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy complex(8), device :: res ! device or host variable
2.5.7. zdotu
ZDOTU forms the dot product of two vectors.
complex(8) function zdotu(n, x, incx, y, incy) integer :: n complex(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
complex(8) function cublasZdotu(n, x, incx, y, incy) integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasZdotu_v2(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy complex(8), device :: res ! device or host variable
2.5.8. dznrm2
DZNRM2 returns the euclidean norm of a vector via the function name, so that DZNRM2 := sqrt( x**H*x )
real(8) function dznrm2(n, x, incx) integer :: n complex(8), device, dimension(*) :: x ! device or host variable integer :: incx
real(8) function cublasDznrm2(n, x, incx) integer :: n complex(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasDznrm2_v2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx real(8), device :: res ! device or host variable
2.5.9. zrot
ZROT applies a plane rotation, where the cos (C) is real and the sin (S) is complex, and the vectors CX and CY are complex.
subroutine zrot(n, x, incx, y, incy, sc, ss) integer :: n real(8), device :: sc ! device or host variable complex(8), device :: ss ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasZrot(n, x, incx, y, incy, sc, ss) integer :: n real(8), device :: sc ! device or host variable complex(8), device :: ss ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasZrot_v2(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(8), device :: sc ! device or host variable complex(8), device :: ss ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.5.10. zsrot
ZSROT applies a plane rotation, where the cos and sin (c and s) are real and the vectors cx and cy are complex.
subroutine zsrot(n, x, incx, y, incy, sc, ss) integer :: n real(8), device :: sc, ss ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasZsrot(n, x, incx, y, incy, sc, ss) integer :: n real(8), device :: sc, ss ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasZsrot_v2(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(8), device :: sc, ss ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.5.11. zrotg
ZROTG determines a double complex Givens rotation.
subroutine zrotg(sa, sb, sc, ss) complex(8), device :: sa, sb, ss ! device or host variable real(8), device :: sc ! device or host variable
subroutine cublasZrotg(sa, sb, sc, ss) complex(8), device :: sa, sb, ss ! device or host variable real(8), device :: sc ! device or host variable
integer(4) function cublasZrotg_v2(h, sa, sb, sc, ss) type(cublasHandle) :: h complex(8), device :: sa, sb, ss ! device or host variable real(8), device :: sc ! device or host variable
2.5.12. zscal
ZSCAL scales a vector by a constant.
subroutine zscal(n, a, x, incx) integer :: n complex(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x ! device or host variable integer :: incx
subroutine cublasZscal(n, a, x, incx) integer :: n complex(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasZscal_v2(h, n, a, x, incx) type(cublasHandle) :: h integer :: n complex(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x integer :: incx
2.5.13. zdscal
ZDSCAL scales a vector by a constant.
subroutine zdscal(n, a, x, incx) integer :: n real(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x ! device or host variable integer :: incx
subroutine cublasZdscal(n, a, x, incx) integer :: n real(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x integer :: incx
integer(4) function cublasZdscal_v2(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x integer :: incx
2.5.14. zswap
ZSWAP interchanges two vectors.
subroutine zswap(n, x, incx, y, incy) integer :: n complex(8), device, dimension(*) :: x, y ! device or host variable integer :: incx, incy
subroutine cublasZswap(n, x, incx, y, incy) integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
integer(4) function cublasZswap_v2(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.5.15. zgbmv
ZGBMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n band matrix, with kl sub-diagonals and ku super-diagonals.
subroutine zgbmv(t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, kl, ku, lda, incx, incy complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZgbmv(t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, kl, ku, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZgbmv_v2(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.5.16. zgemv
ZGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix.
subroutine zgemv(t, m, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZgemv(t, m, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: t integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZgemv_v2(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.5.17. zgerc
ZGERC performs the rank 1 operation A := alpha*x*y**H + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix.
subroutine zgerc(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable complex(8), device :: alpha ! device or host variable
subroutine cublasZgerc(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
integer(4) function cublasZgerc_v2(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
2.5.18. zgeru
ZGERU performs the rank 1 operation A := alpha*x*y**T + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix.
subroutine zgeru(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable complex(8), device :: alpha ! device or host variable
subroutine cublasZgeru(m, n, alpha, x, incx, y, incy, a, lda) integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
integer(4) function cublasZgeru_v2(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
2.5.19. zsymv
ZSYMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix.
subroutine zsymv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZsymv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZsymv_v2(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.5.20. zsyr
ZSYR performs the symmetric rank 1 operation A := alpha*x*x**H + A, where alpha is a complex scalar, x is an n element vector and A is an n by n symmetric matrix.
subroutine zsyr(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x ! device or host variable complex(8), device :: alpha ! device or host variable
subroutine cublasZsyr(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x complex(8), device :: alpha ! device or host variable
integer(4) function cublasZsyr_v2(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x complex(8), device :: alpha ! device or host variable
2.5.21. zsyr2
ZSYR2 performs the symmetric rank 2 operation A := alpha*x*y' + alpha*y*x' + A, where alpha is a complex scalar, x and y are n element vectors and A is an n by n SY matrix.
subroutine zsyr2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable complex(8), device :: alpha ! device or host variable
subroutine cublasZsyr2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
integer(4) function cublasZsyr2_v2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
2.5.22. ztbmv
ZTBMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals.
subroutine ztbmv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x ! device or host variable
subroutine cublasZtbmv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
integer(4) function cublasZtbmv_v2(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.5.23. ztbsv
ZTBSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine ztbsv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x ! device or host variable
subroutine cublasZtbsv(u, t, d, n, k, a, lda, x, incx) character*1 :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
integer(4) function cublasZtbsv_v2(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.5.24. ztpmv
ZTPMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form.
subroutine ztpmv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x ! device or host variable
subroutine cublasZtpmv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x
integer(4) function cublasZtpmv_v2(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x
2.5.25. ztpsv
ZTPSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine ztpsv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x ! device or host variable
subroutine cublasZtpsv(u, t, d, n, a, x, incx) character*1 :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x
integer(4) function cublasZtpsv_v2(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x
2.5.26. ztrmv
ZTRMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix.
subroutine ztrmv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x ! device or host variable
subroutine cublasZtrmv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
integer(4) function cublasZtrmv_v2(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.5.27. ztrsv
ZTRSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.
subroutine ztrsv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x ! device or host variable
subroutine cublasZtrsv(u, t, d, n, a, lda, x, incx) character*1 :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
integer(4) function cublasZtrsv_v2(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.5.28. zhbmv
ZHBMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian band matrix, with k super-diagonals.
subroutine zhbmv(uplo, n, k, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: k, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZhbmv(uplo, n, k, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: k, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZhbmv_v2(h, uplo, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: k, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.5.29. zhemv
ZHEMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian matrix.
subroutine zhemv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(*) :: x, y ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZhemv(uplo, n, alpha, a, lda, x, incx, beta, y, incy) character*1 :: uplo integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZhemv_v2(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.5.30. zhpmv
ZHPMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian matrix, supplied in packed form.
subroutine zhpmv(uplo, n, alpha, a, x, incx, beta, y, incy) character*1 :: uplo integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZhpmv(uplo, n, alpha, a, x, incx, beta, y, incy) character*1 :: uplo integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZhpmv_v2(h, uplo, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha, beta ! device or host variable
2.5.31. zher
ZHER performs the hermitian rank 1 operation A := alpha*x*x**H + A, where alpha is a real scalar, x is an n element vector and A is an n by n hermitian matrix.
subroutine zher(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda complex(8), device, dimension(*) :: a, x ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasZher(t, n, alpha, x, incx, a, lda) character*1 :: t integer :: n, incx, lda complex(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
integer(4) function cublasZher_v2(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
2.5.32. zher2
ZHER2 performs the hermitian rank 2 operation A := alpha*x*y**H + conjg( alpha )*y*x**H + A, where alpha is a scalar, x and y are n element vectors and A is an n by n hermitian matrix.
subroutine zher2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y ! device or host variable complex(8), device :: alpha ! device or host variable
subroutine cublasZher2(t, n, alpha, x, incx, y, incy, a, lda) character*1 :: t integer :: n, incx, incy, lda complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha ! device or host variable
integer(4) function cublasZher2_v2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha ! device or host variable
2.5.33. zhpr
ZHPR performs the hermitian rank 1 operation A := alpha*x*x**H + A, where alpha is a real scalar, x is an n element vector and A is an n by n hermitian matrix, supplied in packed form.
subroutine zhpr(t, n, alpha, x, incx, a) character*1 :: t integer :: n, incx complex(8), device, dimension(*) :: a, x ! device or host variable real(8), device :: alpha ! device or host variable
subroutine cublasZhpr(t, n, alpha, x, incx, a) character*1 :: t integer :: n, incx complex(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
integer(4) function cublasZhpr_v2(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx complex(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
2.5.34. zhpr2
ZHPR2 performs the hermitian rank 2 operation A := alpha*x*y**H + conjg( alpha )*y*x**H + A, where alpha is a scalar, x and y are n element vectors and A is an n by n hermitian matrix, supplied in packed form.
subroutine zhpr2(t, n, alpha, x, incx, y, incy, a) character*1 :: t integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y ! device or host variable complex(8), device :: alpha ! device or host variable
subroutine cublasZhpr2(t, n, alpha, x, incx, y, incy, a) character*1 :: t integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha ! device or host variable
integer(4) function cublasZhpr2_v2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha ! device or host variable
2.5.35. zgemm
ZGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
subroutine zgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZgemm_v2(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.5.36. zsymm
ZSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices.
subroutine zsymm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZsymm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZsymm_v2(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.5.37. zsyrk
ZSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
subroutine zsyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZsyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZsyrk_v2(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.5.38. zsyr2k
ZSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
subroutine zsyr2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZsyr2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZsyr2k_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.5.39. zsyrkx
ZSYRKX performs a variation of the symmetric rank k update C := alpha*A*B**T + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix stored in lower or upper mode, and A and B are n by k matrices. This routine can be used when B is in such a way that the result is guaranteed to be symmetric. See the CUBLAS documentation for more details.
subroutine zsyrkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZsyrkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZsyrkx_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.5.40. ztrmm
ZTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H.
subroutine ztrmm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device :: alpha ! device or host variable
subroutine cublasZtrmm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device :: alpha ! device or host variable
integer(4) function cublasZtrmm_v2(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha ! device or host variable
2.5.41. ztrsm
ZTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B.
subroutine ztrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device :: alpha ! device or host variable
subroutine cublasZtrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) character*1 :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device :: alpha ! device or host variable
integer(4) function cublasZtrsm_v2(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device :: alpha ! device or host variable
2.5.42. zhemm
ZHEMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is an hermitian matrix and B and C are m by n matrices.
subroutine zhemm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable complex(8), device :: alpha, beta ! device or host variable
subroutine cublasZhemm(side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZhemm_v2(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.5.43. zherk
ZHERK performs one of the hermitian rank k operations C := alpha*A*A**H + beta*C, or C := alpha*A**H*A + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
subroutine zherk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable real(8), device :: alpha, beta ! device or host variable
subroutine cublasZherk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
integer(4) function cublasZherk_v2(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.5.44. zher2k
ZHER2K performs one of the hermitian rank 2k operations C := alpha*A*B**H + conjg( alpha )*B*A**H + beta*C, or C := alpha*A**H*B + conjg( alpha )*B**H*A + beta*C, where alpha and beta are scalars with beta real, C is an n by n hermitian matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
subroutine zher2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable complex(8), device :: alpha ! device or host variable real(8), device :: beta ! device or host variable
subroutine cublasZher2k(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha ! device or host variable real(8), device :: beta ! device or host variable
integer(4) function cublasZher2k_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha ! device or host variable real(8), device :: beta ! device or host variable
2.5.45. zherkx
ZHERKX performs a variation of the hermitian rank k operations C := alpha*A*B**H + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix stored in lower or upper mode, and A and B are n by k matrices. See the CUBLAS documentation for more details.
subroutine zherkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a ! device or host variable complex(8), device, dimension(ldb, *) :: b ! device or host variable complex(8), device, dimension(ldc, *) :: c ! device or host variable complex(8), device :: alpha ! device or host variable real(8), device :: beta ! device or host variable
subroutine cublasZherkx(uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) character*1 :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha ! device or host variable real(8), device :: beta ! device or host variable
integer(4) function cublasZherkx_v2(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha ! device or host variable real(8), device :: beta ! device or host variable
2.5.46. cublasZgetrfBatched
ZGETRF computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges. The factorization has the form A = P * L * U where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n). This is the right-looking Level 3 BLAS version of the algorithm.
integer(4) function cublasZgetrfBatched(h, n, Aarray, lda, ipvt, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) integer, device :: info(*) integer :: batchCount
2.5.47. cublasZgetriBatched
ZGETRI computes the inverse of a matrix using the LU factorization computed by ZGETRF. This method inverts U and then computes inv(A) by solving the system inv(A)*L = inv(U) for inv(A).
integer(4) function cublasZgetriBatched(h, n, Aarray, lda, ipvt, Carray, ldc, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Carray(*) integer :: ldc integer, device :: info(*) integer :: batchCount
2.5.48. cublasZgetrsBatched
ZGETRS solves a system of linear equations A * X = B, A**T * X = B, or A**H * X = B with a general N-by-N matrix A using the LU factorization computed by ZGETRF.
integer(4) function cublasZgetrsBatched(h, trans, n, nrhs, Aarray, lda, ipvt, Barray, ldb, info, batchCount) type(cublasHandle) :: h integer :: trans ! integer or character(1) variable integer :: n, nrhs type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Barray(*) integer :: ldb integer :: info(*) integer :: batchCount
2.5.49. cublasZgemmBatched
ZGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
integer(4) function cublasZgemmBatched(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa ! integer or character(1) variable integer :: transb ! integer or character(1) variable integer :: m, n, k complex(8), device :: alpha ! device or host variable type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb complex(8), device :: beta ! device or host variable type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
integer(4) function cublasZgemmBatched_v2(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa integer :: transb integer :: m, n, k complex(8), device :: alpha ! device or host variable type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb complex(8), device :: beta ! device or host variable type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
2.5.50. cublasZtrsmBatched
ZTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B.
integer(4) function cublasZtrsmBatched( h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side ! integer or character(1) variable integer :: uplo ! integer or character(1) variable integer :: trans ! integer or character(1) variable integer :: diag ! integer or character(1) variable integer :: m, n complex(8), device :: alpha ! device or host variable type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
integer(4) function cublasZtrsmBatched_v2( h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side integer :: uplo integer :: trans integer :: diag integer :: m, n complex(8), device :: alpha ! device or host variable type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
2.5.51. cublasZmatinvBatched
cublasZmatinvBatched is a short cut of cublasZgetrfBatched plus cublasZgetriBatched. However it only works if n is less than 32. If not, the user has to go through cublasZgetrfBatched and cublasZgetriBatched.
integer(4) function cublasZmatinvBatched(h, n, Aarray, lda, Ainv, lda_inv, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Ainv(*) integer :: lda_inv integer, device :: info(*) integer :: batchCount
2.5.52. cublasZgeqrfBatched
ZGEQRF computes a QR factorization of a complex M-by-N matrix A: A = Q * R.
integer(4) function cublasZgeqrfBatched(h, m, n, Aarray, lda, Tau, info, batchCount) type(cublasHandle) :: h integer :: m, n type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Tau(*) integer :: info(*) integer :: batchCount
2.5.53. cublasZgelsBatched
ZGELS solves overdetermined or underdetermined complex linear systems involving an M-by-N matrix A, or its conjugate-transpose, using a QR or LQ factorization of A. It is assumed that A has full rank. The following options are provided: 1. If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A*X ||. 2. If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B. 3. If TRANS = 'C' and m >= n: find the minimum norm solution of an undetermined system A**H * X = B. 4. If TRANS = 'C' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A**H * X ||. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X.
integer(4) function cublasZgelsBatched(h, trans, m, n, nrhs, Aarray, lda, Carray, ldc, info, devinfo, batchCount) type(cublasHandle) :: h integer :: trans ! integer or character(1) variable integer :: m, n, nrhs type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Carray(*) integer :: ldc integer :: info(*) integer, device :: devinfo(*) integer :: batchCount
2.6. CUBLAS V2 Module Functions
This section contains interfaces to the cuBLAS V2 Module Functions. Users can access this module by inserting the line use cublas_v2 into the program unit. One major difference in the cublas_v2 versus the cublas module is the cublas entry points, such as cublasIsamax are changed to take the handle as the first argument. The second difference in the cublas_v2 module is the v2 entry points, such as cublasIsamax_v2 do not implicitly handle the pointer modes for the user. It is up to the programmer to make calls to cublasSetPointerMode to tell the library if scalar arguments reside on the host or device. The actual interfaces to the v2 entry points do not change, and are not listed in this section.
2.6.1. Single Precision Functions and Subroutines
This section contains the V2 interfaces to the single precision BLAS and cuBLAS functions and subroutines.
2.6.1.1. isamax
If you use the cublas_v2 module, the interface for cublasIsamax is changed to the following:
integer(4) function cublasIsamax(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.6.1.2. isamin
If you use the cublas_v2 module, the interface for cublasIsamin is changed to the following:
integer(4) function cublasIsamin(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.6.1.3. sasum
If you use the cublas_v2 module, the interface for cublasSasum is changed to the following:
integer(4) function cublasSasum(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx real(4), device :: res ! device or host variable
2.6.1.4. saxpy
If you use the cublas_v2 module, the interface for cublasSaxpy is changed to the following:
integer(4) function cublasSaxpy(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4), device :: a ! device or host variable real(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.1.5. scopy
If you use the cublas_v2 module, the interface for cublasScopy is changed to the following:
integer(4) function cublasScopy(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.1.6. sdot
If you use the cublas_v2 module, the interface for cublasSdot is changed to the following:
integer(4) function cublasSdot(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy real(4), device :: res ! device or host variable
2.6.1.7. snrm2
If you use the cublas_v2 module, the interface for cublasSnrm2 is changed to the following:
integer(4) function cublasSnrm2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx real(4), device :: res ! device or host variable
2.6.1.8. srot
If you use the cublas_v2 module, the interface for cublasSrot is changed to the following:
integer(4) function cublasSrot(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(4), device :: sc, ss ! device or host variable real(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.1.9. srotg
If you use the cublas_v2 module, the interface for cublasSrotg is changed to the following:
integer(4) function cublasSrotg(h, sa, sb, sc, ss) type(cublasHandle) :: h real(4), device :: sa, sb, sc, ss ! device or host variable
2.6.1.10. srotm
If you use the cublas_v2 module, the interface for cublasSrotm is changed to the following:
integer(4) function cublasSrotm(h, n, x, incx, y, incy, param) type(cublasHandle) :: h integer :: n real(4), device :: param(*) ! device or host variable real(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.1.11. srotmg
If you use the cublas_v2 module, the interface for cublasSrotmg is changed to the following:
integer(4) function cublasSrotmg(h, d1, d2, x1, y1, param) type(cublasHandle) :: h real(4), device :: d1, d2, x1, y1, param(*) ! device or host variable
2.6.1.12. sscal
If you use the cublas_v2 module, the interface for cublasSscal is changed to the following:
integer(4) function cublasSscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(4), device :: a ! device or host variable real(4), device, dimension(*) :: x integer :: incx
2.6.1.13. sswap
If you use the cublas_v2 module, the interface for cublasSswap is changed to the following:
integer(4) function cublasSswap(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.1.14. sgbmv
If you use the cublas_v2 module, the interface for cublasSgbmv is changed to the following:
integer(4) function cublasSgbmv(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
2.6.1.15. sgemv
If you use the cublas_v2 module, the interface for cublasSgemv is changed to the following:
integer(4) function cublasSgemv(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
2.6.1.16. sger
If you use the cublas_v2 module, the interface for cublasSger is changed to the following:
integer(4) function cublasSger(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha ! device or host variable
2.6.1.17. ssbmv
If you use the cublas_v2 module, the interface for cublasSsbmv is changed to the following:
integer(4) function cublasSsbmv(h, t, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: k, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
2.6.1.18. sspmv
If you use the cublas_v2 module, the interface for cublasSspmv is changed to the following:
integer(4) function cublasSspmv(h, t, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y real(4), device :: alpha, beta ! device or host variable
2.6.1.19. sspr
If you use the cublas_v2 module, the interface for cublasSspr is changed to the following:
integer(4) function cublasSspr(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx real(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
2.6.1.20. sspr2
If you use the cublas_v2 module, the interface for cublasSspr2 is changed to the following:
integer(4) function cublasSspr2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y real(4), device :: alpha ! device or host variable
2.6.1.21. ssymv
If you use the cublas_v2 module, the interface for cublasSsymv is changed to the following:
integer(4) function cublasSsymv(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha, beta ! device or host variable
2.6.1.22. ssyr
If you use the cublas_v2 module, the interface for cublasSsyr is changed to the following:
integer(4) function cublasSsyr(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x real(4), device :: alpha ! device or host variable
2.6.1.23. ssyr2
If you use the cublas_v2 module, the interface for cublasSsyr2 is changed to the following:
integer(4) function cublasSsyr2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4), device :: alpha ! device or host variable
2.6.1.24. stbmv
If you use the cublas_v2 module, the interface for cublasStbmv is changed to the following:
integer(4) function cublasStbmv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.6.1.25. stbsv
If you use the cublas_v2 module, the interface for cublasStbsv is changed to the following:
integer(4) function cublasStbsv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.6.1.26. stpmv
If you use the cublas_v2 module, the interface for cublasStpmv is changed to the following:
integer(4) function cublasStpmv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x
2.6.1.27. stpsv
If you use the cublas_v2 module, the interface for cublasStpsv is changed to the following:
integer(4) function cublasStpsv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x
2.6.1.28. strmv
If you use the cublas_v2 module, the interface for cublasStrmv is changed to the following:
integer(4) function cublasStrmv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.6.1.29. strsv
If you use the cublas_v2 module, the interface for cublasStrsv is changed to the following:
integer(4) function cublasStrsv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.6.1.30. sgemm
If you use the cublas_v2 module, the interface for cublasSgemm is changed to the following:
integer(4) function cublasSgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.6.1.31. ssymm
If you use the cublas_v2 module, the interface for cublasSsymm is changed to the following:
integer(4) function cublasSsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.6.1.32. ssyrk
If you use the cublas_v2 module, the interface for cublasSsyrk is changed to the following:
integer(4) function cublasSsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.6.1.33. ssyr2k
If you use the cublas_v2 module, the interface for cublasSsyr2k is changed to the following:
integer(4) function cublasSsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.6.1.34. ssyrkx
If you use the cublas_v2 module, the interface for cublasSsyrkx is changed to the following:
integer(4) function cublasSsyrkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.6.1.35. strmm
If you use the cublas_v2 module, the interface for cublasStrmm is changed to the following:
integer(4) function cublasStrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4), device :: alpha ! device or host variable
2.6.1.36. strsm
If you use the cublas_v2 module, the interface for cublasStrsm is changed to the following:
integer(4) function cublasStrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device :: alpha ! device or host variable
2.6.2. Double Precision Functions and Subroutines
This section contains the V2 interfaces to the double precision BLAS and cuBLAS functions and subroutines.
2.6.2.1. idamax
If you use the cublas_v2 module, the interface for cublasIdamax is changed to the following:
integer(4) function cublasIdamax(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.6.2.2. idamin
If you use the cublas_v2 module, the interface for cublasIdamin is changed to the following:
integer(4) function cublasIdamin(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.6.2.3. dasum
If you use the cublas_v2 module, the interface for cublasDasum is changed to the following:
integer(4) function cublasDasum(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx real(8), device :: res ! device or host variable
2.6.2.4. daxpy
If you use the cublas_v2 module, the interface for cublasDaxpy is changed to the following:
integer(4) function cublasDaxpy(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8), device :: a ! device or host variable real(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.2.5. dcopy
If you use the cublas_v2 module, the interface for cublasDcopy is changed to the following:
integer(4) function cublasDcopy(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.2.6. ddot
If you use the cublas_v2 module, the interface for cublasDdot is changed to the following:
integer(4) function cublasDdot(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy real(8), device :: res ! device or host variable
2.6.2.7. dnrm2
If you use the cublas_v2 module, the interface for cublasDnrm2 is changed to the following:
integer(4) function cublasDnrm2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx real(8), device :: res ! device or host variable
2.6.2.8. drot
If you use the cublas_v2 module, the interface for cublasDrot is changed to the following:
integer(4) function cublasDrot(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(8), device :: sc, ss ! device or host variable real(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.2.9. drotg
If you use the cublas_v2 module, the interface for cublasDrotg is changed to the following:
integer(4) function cublasDrotg(h, sa, sb, sc, ss) type(cublasHandle) :: h real(8), device :: sa, sb, sc, ss ! device or host variable
2.6.2.10. drotm
If you use the cublas_v2 module, the interface for cublasDrotm is changed to the following:
integer(4) function cublasDrotm(h, n, x, incx, y, incy, param) type(cublasHandle) :: h integer :: n real(8), device :: param(*) ! device or host variable real(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.2.11. drotmg
If you use the cublas_v2 module, the interface for cublasDrotmg is changed to the following:
integer(4) function cublasDrotmg(h, d1, d2, x1, y1, param) type(cublasHandle) :: h real(8), device :: d1, d2, x1, y1, param(*) ! device or host variable
2.6.2.12. dscal
If you use the cublas_v2 module, the interface for cublasDscal is changed to the following:
integer(4) function cublasDscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(8), device :: a ! device or host variable real(8), device, dimension(*) :: x integer :: incx
2.6.2.13. dswap
If you use the cublas_v2 module, the interface for cublasDswap is changed to the following:
integer(4) function cublasDswap(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.2.14. dgbmv
If you use the cublas_v2 module, the interface for cublasDgbmv is changed to the following:
integer(4) function cublasDgbmv(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
2.6.2.15. dgemv
If you use the cublas_v2 module, the interface for cublasDgemv is changed to the following:
integer(4) function cublasDgemv(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
2.6.2.16. dger
If you use the cublas_v2 module, the interface for cublasDger is changed to the following:
integer(4) function cublasDger(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha ! device or host variable
2.6.2.17. dsbmv
If you use the cublas_v2 module, the interface for cublasDsbmv is changed to the following:
integer(4) function cublasDsbmv(h, t, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: k, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
2.6.2.18. dspmv
If you use the cublas_v2 module, the interface for cublasDspmv is changed to the following:
integer(4) function cublasDspmv(h, t, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y real(8), device :: alpha, beta ! device or host variable
2.6.2.19. dspr
If you use the cublas_v2 module, the interface for cublasDspr is changed to the following:
integer(4) function cublasDspr(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx real(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
2.6.2.20. dspr2
If you use the cublas_v2 module, the interface for cublasDspr2 is changed to the following:
integer(4) function cublasDspr2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y real(8), device :: alpha ! device or host variable
2.6.2.21. dsymv
If you use the cublas_v2 module, the interface for cublasDsymv is changed to the following:
integer(4) function cublasDsymv(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha, beta ! device or host variable
2.6.2.22. dsyr
If you use the cublas_v2 module, the interface for cublasDsyr is changed to the following:
integer(4) function cublasDsyr(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x real(8), device :: alpha ! device or host variable
2.6.2.23. dsyr2
If you use the cublas_v2 module, the interface for cublasDsyr2 is changed to the following:
integer(4) function cublasDsyr2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8), device :: alpha ! device or host variable
2.6.2.24. dtbmv
If you use the cublas_v2 module, the interface for cublasDtbmv is changed to the following:
integer(4) function cublasDtbmv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.6.2.25. dtbsv
If you use the cublas_v2 module, the interface for cublasDtbsv is changed to the following:
integer(4) function cublasDtbsv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.6.2.26. dtpmv
If you use the cublas_v2 module, the interface for cublasDtpmv is changed to the following:
integer(4) function cublasDtpmv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x
2.6.2.27. dtpsv
If you use the cublas_v2 module, the interface for cublasDtpsv is changed to the following:
integer(4) function cublasDtpsv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x
2.6.2.28. dtrmv
If you use the cublas_v2 module, the interface for cublasDtrmv is changed to the following:
integer(4) function cublasDtrmv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.6.2.29. dtrsv
If you use the cublas_v2 module, the interface for cublasDtrsv is changed to the following:
integer(4) function cublasDtrsv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.6.2.30. dgemm
If you use the cublas_v2 module, the interface for cublasDgemm is changed to the following:
integer(4) function cublasDgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.6.2.31. dsymm
If you use the cublas_v2 module, the interface for cublasDsymm is changed to the following:
integer(4) function cublasDsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.6.2.32. dsyrk
If you use the cublas_v2 module, the interface for cublasDsyrk is changed to the following:
integer(4) function cublasDsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.6.2.33. dsyr2k
If you use the cublas_v2 module, the interface for cublasDsyr2k is changed to the following:
integer(4) function cublasDsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.6.2.34. dsyrkx
If you use the cublas_v2 module, the interface for cublasDsyrkx is changed to the following:
integer(4) function cublasDsyrkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.6.2.35. dtrmm
If you use the cublas_v2 module, the interface for cublasDtrmm is changed to the following:
integer(4) function cublasDtrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8), device :: alpha ! device or host variable
2.6.2.36. dtrsm
If you use the cublas_v2 module, the interface for cublasDtrsm is changed to the following:
integer(4) function cublasDtrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device :: alpha ! device or host variable
2.6.3. Single Precision Complex Functions and Subroutines
This section contains the V2 interfaces to the single precision complex BLAS and cuBLAS functions and subroutines.
2.6.3.1. icamax
If you use the cublas_v2 module, the interface for cublasIcamax is changed to the following:
integer(4) function cublasIcamax(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.6.3.2. icamin
If you use the cublas_v2 module, the interface for cublasIcamin is changed to the following:
integer(4) function cublasIcamin(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.6.3.3. scasum
If you use the cublas_v2 module, the interface for cublasScasum is changed to the following:
integer(4) function cublasScasum(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx real(4), device :: res ! device or host variable
2.6.3.4. caxpy
If you use the cublas_v2 module, the interface for cublasCaxpy is changed to the following:
integer(4) function cublasCaxpy(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.3.5. ccopy
If you use the cublas_v2 module, the interface for cublasCcopy is changed to the following:
integer(4) function cublasCcopy(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.3.6. cdotc
If you use the cublas_v2 module, the interface for cublasCdotc is changed to the following:
integer(4) function cublasCdotc(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy complex(4), device :: res ! device or host variable
2.6.3.7. cdotu
If you use the cublas_v2 module, the interface for cublasCdotu is changed to the following:
integer(4) function cublasCdotu(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy complex(4), device :: res ! device or host variable
2.6.3.8. scnrm2
If you use the cublas_v2 module, the interface for cublasScnrm2 is changed to the following:
integer(4) function cublasScnrm2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx real(4), device :: res ! device or host variable
2.6.3.9. crot
If you use the cublas_v2 module, the interface for cublasCrot is changed to the following:
integer(4) function cublasCrot(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(4), device :: sc ! device or host variable complex(4), device :: ss ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.3.10. csrot
If you use the cublas_v2 module, the interface for cublasCsrot is changed to the following:
integer(4) function cublasCsrot(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(4), device :: sc, ss ! device or host variable complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.3.11. crotg
If you use the cublas_v2 module, the interface for cublasCrotg is changed to the following:
integer(4) function cublasCrotg(h, sa, sb, sc, ss) type(cublasHandle) :: h complex(4), device :: sa, sb, ss ! device or host variable real(4), device :: sc ! device or host variable
2.6.3.12. cscal
If you use the cublas_v2 module, the interface for cublasCscal is changed to the following:
integer(4) function cublasCscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n complex(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x integer :: incx
2.6.3.13. csscal
If you use the cublas_v2 module, the interface for cublasCsscal is changed to the following:
integer(4) function cublasCsscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(4), device :: a ! device or host variable complex(4), device, dimension(*) :: x integer :: incx
2.6.3.14. cswap
If you use the cublas_v2 module, the interface for cublasCswap is changed to the following:
integer(4) function cublasCswap(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.6.3.15. cgbmv
If you use the cublas_v2 module, the interface for cublasCgbmv is changed to the following:
integer(4) function cublasCgbmv(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.6.3.16. cgemv
If you use the cublas_v2 module, the interface for cublasCgemv is changed to the following:
integer(4) function cublasCgemv(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.6.3.17. cgerc
If you use the cublas_v2 module, the interface for cublasCgerc is changed to the following:
integer(4) function cublasCgerc(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
2.6.3.18. cgeru
If you use the cublas_v2 module, the interface for cublasCgeru is changed to the following:
integer(4) function cublasCgeru(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
2.6.3.19. csymv
If you use the cublas_v2 module, the interface for cublasCsymv is changed to the following:
integer(4) function cublasCsymv(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.6.3.20. csyr
If you use the cublas_v2 module, the interface for cublasCsyr is changed to the following:
integer(4) function cublasCsyr(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x complex(4), device :: alpha ! device or host variable
2.6.3.21. csyr2
If you use the cublas_v2 module, the interface for cublasCsyr2 is changed to the following:
integer(4) function cublasCsyr2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha ! device or host variable
2.6.3.22. ctbmv
If you use the cublas_v2 module, the interface for cublasCtbmv is changed to the following:
integer(4) function cublasCtbmv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.6.3.23. ctbsv
If you use the cublas_v2 module, the interface for cublasCtbsv is changed to the following:
integer(4) function cublasCtbsv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.6.3.24. ctpmv
If you use the cublas_v2 module, the interface for cublasCtpmv is changed to the following:
integer(4) function cublasCtpmv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x
2.6.3.25. ctpsv
If you use the cublas_v2 module, the interface for cublasCtpsv is changed to the following:
integer(4) function cublasCtpsv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x
2.6.3.26. ctrmv
If you use the cublas_v2 module, the interface for cublasCtrmv is changed to the following:
integer(4) function cublasCtrmv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.6.3.27. ctrsv
If you use the cublas_v2 module, the interface for cublasCtrsv is changed to the following:
integer(4) function cublasCtrsv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.6.3.28. chbmv
If you use the cublas_v2 module, the interface for cublasChbmv is changed to the following:
integer(4) function cublasChbmv(h, uplo, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: k, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.6.3.29. chemv
If you use the cublas_v2 module, the interface for cublasChemv is changed to the following:
integer(4) function cublasChemv(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4), device :: alpha, beta ! device or host variable
2.6.3.30. chpmv
If you use the cublas_v2 module, the interface for cublasChpmv is changed to the following:
integer(4) function cublasChpmv(h, uplo, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha, beta ! device or host variable
2.6.3.31. cher
If you use the cublas_v2 module, the interface for cublasCher is changed to the following:
integer(4) function cublasCher(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
2.6.3.32. cher2
If you use the cublas_v2 module, the interface for cublasCher2 is changed to the following:
integer(4) function cublasCher2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha ! device or host variable
2.6.3.33. chpr
If you use the cublas_v2 module, the interface for cublasChpr is changed to the following:
integer(4) function cublasChpr(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx complex(4), device, dimension(*) :: a, x real(4), device :: alpha ! device or host variable
2.6.3.34. chpr2
If you use the cublas_v2 module, the interface for cublasChpr2 is changed to the following:
integer(4) function cublasChpr2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y complex(4), device :: alpha ! device or host variable
2.6.3.35. cgemm
If you use the cublas_v2 module, the interface for cublasCgemm is changed to the following:
integer(4) function cublasCgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.6.3.36. csymm
If you use the cublas_v2 module, the interface for cublasCsymm is changed to the following:
integer(4) function cublasCsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.6.3.37. csyrk
If you use the cublas_v2 module, the interface for cublasCsyrk is changed to the following:
integer(4) function cublasCsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.6.3.38. csyr2k
If you use the cublas_v2 module, the interface for cublasCsyr2k is changed to the following:
integer(4) function cublasCsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.6.3.39. csyrkx
If you use the cublas_v2 module, the interface for cublasCsyrkx is changed to the following:
integer(4) function cublasCsyrkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.6.3.40. ctrmm
If you use the cublas_v2 module, the interface for cublasCtrmm is changed to the following:
integer(4) function cublasCtrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha ! device or host variable
2.6.3.41. ctrsm
If you use the cublas_v2 module, the interface for cublasCtrsm is changed to the following:
integer(4) function cublasCtrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device :: alpha ! device or host variable
2.6.3.42. chemm
If you use the cublas_v2 module, the interface for cublasChemm is changed to the following:
integer(4) function cublasChemm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha, beta ! device or host variable
2.6.3.43. cherk
If you use the cublas_v2 module, the interface for cublasCherk is changed to the following:
integer(4) function cublasCherk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldc, *) :: c real(4), device :: alpha, beta ! device or host variable
2.6.3.44. cher2k
If you use the cublas_v2 module, the interface for cublasCher2k is changed to the following:
integer(4) function cublasCher2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha ! device or host variable real(4), device :: beta ! device or host variable
2.6.3.45. cherkx
If you use the cublas_v2 module, the interface for cublasCherkx is changed to the following:
integer(4) function cublasCherkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4), device :: alpha ! device or host variable real(4), device :: beta ! device or host variable
2.6.4. Double Precision Complex Functions and Subroutines
This section contains the V2 interfaces to the double precision complex BLAS and cuBLAS functions and subroutines.
2.6.4.1. izamax
If you use the cublas_v2 module, the interface for cublasIzamax is changed to the following:
integer(4) function cublasIzamax(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.6.4.2. izamin
If you use the cublas_v2 module, the interface for cublasIzamin is changed to the following:
integer(4) function cublasIzamin(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx integer, device :: res ! device or host variable
2.6.4.3. dzasum
If you use the cublas_v2 module, the interface for cublasDzasum is changed to the following:
integer(4) function cublasDzasum(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx real(8), device :: res ! device or host variable
2.6.4.4. zaxpy
If you use the cublas_v2 module, the interface for cublasZaxpy is changed to the following:
integer(4) function cublasZaxpy(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.4.5. zcopy
If you use the cublas_v2 module, the interface for cublasZcopy is changed to the following:
integer(4) function cublasZcopy(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.4.6. zdotc
If you use the cublas_v2 module, the interface for cublasZdotc is changed to the following:
integer(4) function cublasZdotc(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy complex(8), device :: res ! device or host variable
2.6.4.7. zdotu
If you use the cublas_v2 module, the interface for cublasZdotu is changed to the following:
integer(4) function cublasZdotu(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy complex(8), device :: res ! device or host variable
2.6.4.8. dznrm2
If you use the cublas_v2 module, the interface for cublasDznrm2 is changed to the following:
integer(4) function cublasDznrm2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx real(8), device :: res ! device or host variable
2.6.4.9. zrot
If you use the cublas_v2 module, the interface for cublasZrot is changed to the following:
integer(4) function cublasZrot(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(8), device :: sc ! device or host variable complex(8), device :: ss ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.4.10. zsrot
If you use the cublas_v2 module, the interface for cublasZsrot is changed to the following:
integer(4) function cublasZsrot(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(8), device :: sc, ss ! device or host variable complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.4.11. zrotg
If you use the cublas_v2 module, the interface for cublasZrotg is changed to the following:
integer(4) function cublasZrotg(h, sa, sb, sc, ss) type(cublasHandle) :: h complex(8), device :: sa, sb, ss ! device or host variable real(8), device :: sc ! device or host variable
2.6.4.12. zscal
If you use the cublas_v2 module, the interface for cublasZscal is changed to the following:
integer(4) function cublasZscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n complex(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x integer :: incx
2.6.4.13. zdscal
If you use the cublas_v2 module, the interface for cublasZdscal is changed to the following:
integer(4) function cublasZdscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(8), device :: a ! device or host variable complex(8), device, dimension(*) :: x integer :: incx
2.6.4.14. zswap
If you use the cublas_v2 module, the interface for cublasZswap is changed to the following:
integer(4) function cublasZswap(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.6.4.15. zgbmv
If you use the cublas_v2 module, the interface for cublasZgbmv is changed to the following:
integer(4) function cublasZgbmv(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.6.4.16. zgemv
If you use the cublas_v2 module, the interface for cublasZgemv is changed to the following:
integer(4) function cublasZgemv(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.6.4.17. zgerc
If you use the cublas_v2 module, the interface for cublasZgerc is changed to the following:
integer(4) function cublasZgerc(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
2.6.4.18. zgeru
If you use the cublas_v2 module, the interface for cublasZgeru is changed to the following:
integer(4) function cublasZgeru(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
2.6.4.19. zsymv
If you use the cublas_v2 module, the interface for cublasZsymv is changed to the following:
integer(4) function cublasZsymv(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.6.4.20. zsyr
If you use the cublas_v2 module, the interface for cublasZsyr is changed to the following:
integer(4) function cublasZsyr(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x complex(8), device :: alpha ! device or host variable
2.6.4.21. zsyr2
If you use the cublas_v2 module, the interface for cublasZsyr2 is changed to the following:
integer(4) function cublasZsyr2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha ! device or host variable
2.6.4.22. ztbmv
If you use the cublas_v2 module, the interface for cublasZtbmv is changed to the following:
integer(4) function cublasZtbmv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.6.4.23. ztbsv
If you use the cublas_v2 module, the interface for cublasZtbsv is changed to the following:
integer(4) function cublasZtbsv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.6.4.24. ztpmv
If you use the cublas_v2 module, the interface for cublasZtpmv is changed to the following:
integer(4) function cublasZtpmv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x
2.6.4.25. ztpsv
If you use the cublas_v2 module, the interface for cublasZtpsv is changed to the following:
integer(4) function cublasZtpsv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x
2.6.4.26. ztrmv
If you use the cublas_v2 module, the interface for cublasZtrmv is changed to the following:
integer(4) function cublasZtrmv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.6.4.27. ztrsv
If you use the cublas_v2 module, the interface for cublasZtrsv is changed to the following:
integer(4) function cublasZtrsv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.6.4.28. zhbmv
If you use the cublas_v2 module, the interface for cublasZhbmv is changed to the following:
integer(4) function cublasZhbmv(h, uplo, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: k, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.6.4.29. zhemv
If you use the cublas_v2 module, the interface for cublasZhemv is changed to the following:
integer(4) function cublasZhemv(h, uplo, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8), device :: alpha, beta ! device or host variable
2.6.4.30. zhpmv
If you use the cublas_v2 module, the interface for cublasZhpmv is changed to the following:
integer(4) function cublasZhpmv(h, uplo, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: uplo integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha, beta ! device or host variable
2.6.4.31. zher
If you use the cublas_v2 module, the interface for cublasZher is changed to the following:
integer(4) function cublasZher(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
2.6.4.32. zher2
If you use the cublas_v2 module, the interface for cublasZher2 is changed to the following:
integer(4) function cublasZher2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha ! device or host variable
2.6.4.33. zhpr
If you use the cublas_v2 module, the interface for cublasZhpr is changed to the following:
integer(4) function cublasZhpr(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx complex(8), device, dimension(*) :: a, x real(8), device :: alpha ! device or host variable
2.6.4.34. zhpr2
If you use the cublas_v2 module, the interface for cublasZhpr2 is changed to the following:
integer(4) function cublasZhpr2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y complex(8), device :: alpha ! device or host variable
2.6.4.35. zgemm
If you use the cublas_v2 module, the interface for cublasZgemm is changed to the following:
integer(4) function cublasZgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.6.4.36. zsymm
If you use the cublas_v2 module, the interface for cublasZsymm is changed to the following:
integer(4) function cublasZsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.6.4.37. zsyrk
If you use the cublas_v2 module, the interface for cublasZsyrk is changed to the following:
integer(4) function cublasZsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.6.4.38. zsyr2k
If you use the cublas_v2 module, the interface for cublasZsyr2k is changed to the following:
integer(4) function cublasZsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.6.4.39. zsyrkx
If you use the cublas_v2 module, the interface for cublasZsyrkx is changed to the following:
integer(4) function cublasZsyrkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.6.4.40. ztrmm
If you use the cublas_v2 module, the interface for cublasZtrmm is changed to the following:
integer(4) function cublasZtrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha ! device or host variable
2.6.4.41. ztrsm
If you use the cublas_v2 module, the interface for cublasZtrsm is changed to the following:
integer(4) function cublasZtrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device :: alpha ! device or host variable
2.6.4.42. zhemm
If you use the cublas_v2 module, the interface for cublasZhemm is changed to the following:
integer(4) function cublasZhemm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha, beta ! device or host variable
2.6.4.43. zherk
If you use the cublas_v2 module, the interface for cublasZherk is changed to the following:
integer(4) function cublasZherk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldc, *) :: c real(8), device :: alpha, beta ! device or host variable
2.6.4.44. zher2k
If you use the cublas_v2 module, the interface for cublasZher2k is changed to the following:
integer(4) function cublasZher2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha ! device or host variable real(8), device :: beta ! device or host variable
2.6.4.45. zherkx
If you use the cublas_v2 module, the interface for cublasZherkx is changed to the following:
integer(4) function cublasZherkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8), device :: alpha ! device or host variable real(8), device :: beta ! device or host variable
2.7. CUBLAS XT Module Functions
This section contains interfaces to the cuBLAS XT Module Functions. Users can access this module by inserting the line use cublasXt into the program unit. The cublasXt library is a host-side library, which supports multiple GPUs. Here is an example:
subroutine testxt(n) use cublasXt complex*16 :: a(n,n), b(n,n), c(n,n), alpha, beta type(cublasXtHandle) :: h integer ndevices(1) a = cmplx(1.0d0,0.0d0) b = cmplx(2.0d0,0.0d0) c = cmplx(-1.0d0,0.0d0) alpha = cmplx(1.0d0,0.0d0) beta = cmplx(0.0d0,0.0d0) istat = cublasXtCreate(h) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat ndevices(1) = 0 istat = cublasXtDeviceSelect(h, 1, ndevices) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat istat = cublasXtZgemm(h, CUBLAS_OP_N, CUBLAS_OP_N, & n, n, n, & alpha, A, n, B, n, beta, C, n) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat istat = cublasXtDestroy(h) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat if (all(dble(c).eq.2.0d0*n)) then print *,"Test PASSED" else print *,"Test FAILED" endif end
The cublasXt module contains all the types and definitions from the cublas module, and these additional types and enumerations:
TYPE cublasXtHandle TYPE(C_PTR) :: handle END TYPE
! Pinned memory mode enum, bind(c) enumerator :: CUBLASXT_PINNING_DISABLED=0 enumerator :: CUBLASXT_PINNING_ENABLED=1 end enum
! cublasXtOpType enum, bind(c) enumerator :: CUBLASXT_FLOAT=0 enumerator :: CUBLASXT_DOUBLE=1 enumerator :: CUBLASXT_COMPLEX=2 enumerator :: CUBLASXT_DOUBLECOMPLEX=3 end enum
! cublasXtBlasOp enum, bind(c) enumerator :: CUBLASXT_GEMM=0 enumerator :: CUBLASXT_SYRK=1 enumerator :: CUBLASXT_HERK=2 enumerator :: CUBLASXT_SYMM=3 enumerator :: CUBLASXT_HEMM=4 enumerator :: CUBLASXT_TRSM=5 enumerator :: CUBLASXT_SYR2K=6 enumerator :: CUBLASXT_HER2K=7 enumerator :: CUBLASXT_SPMM=8 enumerator :: CUBLASXT_SYRKX=9 enumerator :: CUBLASXT_HERKX=10 enumerator :: CUBLASXT_TRMM=11 enumerator :: CUBLASXT_ROUTINE_MAX=12 end enum
2.7.1. cublasXtCreate
This function initializes the cublasXt API and creates a handle to an opaque structure holding the cublasXT library context. It allocates hardware resources on the host and device and must be called prior to making any other cublasXt API library calls.
integer(4) function cublasXtcreate(h) type(cublasXtHandle) :: h
2.7.2. cublasXtDestroy
This function releases hardware resources used by the cublasXt API context. This function is usually the last call with a particular handle to the cublasXt API.
integer(4) function cublasXtdestroy(h) type(cublasXtHandle) :: h
2.7.3. cublasXtDeviceSelect
This function allows the user to provide the number of GPU devices and their respective Ids that will participate to the subsequent cublasXt API math function calls. This function will create a cuBLAS context for every GPU provided in that list. Currently the device configuration is static and cannot be changed between math function calls. In that regard, this function should be called only once after cublasXtCreate. To be able to run multiple configurations, multiple cublasXt API contexts should be created.
integer(4) function cublasXtdeviceselect(h, ndevices, deviceid) type(cublasXtHandle) :: h integer :: ndevices integer, dimension(*) :: deviceid
2.7.4. cublasXtSetBlockDim
This function allows the user to set the block dimension used for the tiling of the matrices for the subsequent Math function calls. Matrices are split in square tiles of blockDim x blockDim dimension. This function can be called anytime and will take effect for the following math function calls. The block dimension should be chosen in a way to optimize the math operation and to make sure that the PCI transfers are well overlapped with the computation.
integer(4) function cublasXtsetblockdim(h, blockdim) type(cublasXtHandle) :: h integer :: blockdim
2.7.5. cublasXtGetBlockDim
This function allows the user to query the block dimension used for the tiling of the matrices.
integer(4) function cublasXtgetblockdim(h, blockdim) type(cublasXtHandle) :: h integer :: blockdim
2.7.6. cublasXtSetCpuRoutine
This function allows the user to provide a CPU implementation of the corresponding BLAS routine. This function can be used with the function cublasXtSetCpuRatio() to define an hybrid computation between the CPU and the GPUs. Currently the hybrid feature is only supported for the xGEMM routines.
integer(4) function cublasXtsetcpuroutine(h, blasop, blastype) type(cublasXtHandle) :: h integer :: blasop, blastype
2.7.7. cublasXtSetCpuRatio
This function allows the user to define the percentage of workload that should be done on a CPU in the context of an hybrid computation. This function can be used with the function cublasXtSetCpuRoutine() to define an hybrid computation between the CPU and the GPUs. Currently the hybrid feature is only supported for the xGEMM routines.
integer(4) function cublasXtsetcpuratio(h, blasop, blastype, ratio) type(cublasXtHandle) :: h integer :: blasop, blastype real(4) :: ratio
2.7.8. cublasXtSetPinningMemMode
This function allows the user to enable or disable the Pinning Memory mode. When enabled, the matrices passed in subsequent cublasXt API calls will be pinned/unpinned using the CUDART routine cudaHostRegister and cudaHostUnregister respectively if the matrices are not already pinned. If a matrix happened to be pinned partially, it will also not be pinned. Pinning the memory improve PCI transfer performace and allows to overlap PCI memory transfer with computation. However pinning/unpinning the memory takes some time which might not be amortized. It is advised that the user pins the memory on its own using cudaMallocHost or cudaHostRegister and unpins it when the computation sequence is completed. By default, the Pinning Memory mode is disabled.
integer(4) function cublasXtsetpinningmemmode(h, mode) type(cublasXtHandle) :: h integer :: mode
2.7.9. cublasXtGetPinningMemMode
This function allows the user to query the Pinning Memory mode. By default, the Pinning Memory mode is disabled.
integer(4) function cublasXtgetpinningmemmode(h, mode) type(cublasXtHandle) :: h integer :: mode
2.7.10. cublasXtSgemm
SGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
integer(4) function cublasXtsgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: transa, transb integer(kind=c_intptr_t) :: m, n, k, lda, ldb, ldc real(4), dimension(lda, *) :: a real(4), dimension(ldb, *) :: b real(4), dimension(ldc, *) :: c real(4) :: alpha, beta
2.7.11. cublasXtSsymm
SSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices.
integer(4) function cublasXtssymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc real(4), dimension(lda, *) :: a real(4), dimension(ldb, *) :: b real(4), dimension(ldc, *) :: c real(4) :: alpha, beta
2.7.12. cublasXtSsyrk
SSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
integer(4) function cublasXtssyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldc real(4), dimension(lda, *) :: a real(4), dimension(ldc, *) :: c real(4) :: alpha, beta
2.7.13. cublasXtSsyr2k
SSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
integer(4) function cublasXtssyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc real(4), dimension(lda, *) :: a real(4), dimension(ldb, *) :: b real(4), dimension(ldc, *) :: c real(4) :: alpha, beta
2.7.14. cublasXtSsyrkx
SSYRKX performs a variation of the symmetric rank k update C := alpha*A*B**T + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix stored in lower or upper mode, and A and B are n by k matrices. This routine can be used when B is in such a way that the result is guaranteed to be symmetric. See the CUBLAS documentation for more details.
integer(4) function cublasXtssyrkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc real(4), dimension(lda, *) :: a real(4), dimension(ldb, *) :: b real(4), dimension(ldc, *) :: c real(4) :: alpha, beta
2.7.15. cublasXtStrmm
STRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ), where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T.
integer(4) function cublasXtstrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo, transa, diag integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc real(4), dimension(lda, *) :: a real(4), dimension(ldb, *) :: b real(4), dimension(ldc, *) :: c real(4) :: alpha
2.7.16. cublasXtStrsm
STRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B.
integer(4) function cublasXtstrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasXtHandle) :: h integer :: side, uplo, transa, diag integer(kind=c_intptr_t) :: m, n, lda, ldb real(4), dimension(lda, *) :: a real(4), dimension(ldb, *) :: b real(4) :: alpha
2.7.17. cublasXtSspmm
SSPMM performs one of the symmetric packed matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a n by n symmetric matrix stored in packed format, and B and C are m by n matrices.
integer(4) function cublasXtsspmm(h, side, uplo, m, n, alpha, ap, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, ldb, ldc real(4), dimension(*) :: ap real(4), dimension(ldb, *) :: b real(4), dimension(ldc, *) :: c real(4) :: alpha, beta
2.7.18. cublasXtCgemm
CGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
integer(4) function cublasXtcgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: transa, transb integer(kind=c_intptr_t) :: m, n, k, lda, ldb, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha, beta
2.7.19. cublasXtChemm
CHEMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is an hermitian matrix and B and C are m by n matrices.
integer(4) function cublasXtchemm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha, beta
2.7.20. cublasXtCherk
CHERK performs one of the hermitian rank k operations C := alpha*A*A**H + beta*C, or C := alpha*A**H*A + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
integer(4) function cublasXtcherk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldc, *) :: c real(4) :: alpha, beta
2.7.21. cublasXtCher2k
CHER2K performs one of the hermitian rank 2k operations C := alpha*A*B**H + conjg( alpha )*B*A**H + beta*C, or C := alpha*A**H*B + conjg( alpha )*B**H*A + beta*C, where alpha and beta are scalars with beta real, C is an n by n hermitian matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
integer(4) function cublasXtcher2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha real(4) :: beta
2.7.22. cublasXtCherkx
CHERKX performs a variation of the hermitian rank k operations C := alpha*A*B**H + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix stored in lower or upper mode, and A and B are n by k matrices. See the CUBLAS documentation for more details.
integer(4) function cublasXtcherkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha real(4) :: beta
2.7.23. cublasXtCsymm
CSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices.
integer(4) function cublasXtcsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha, beta
2.7.24. cublasXtCsyrk
CSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
integer(4) function cublasXtcsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldc, *) :: c complex(4) :: alpha, beta
2.7.25. cublasXtCsyr2k
CSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
integer(4) function cublasXtcsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha, beta
2.7.26. cublasXtCsyrkx
CSYRKX performs a variation of the symmetric rank k update C := alpha*A*B**T + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix stored in lower or upper mode, and A and B are n by k matrices. This routine can be used when B is in such a way that the result is guaranteed to be symmetric. See the CUBLAS documentation for more details.
integer(4) function cublasXtcsyrkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha, beta
2.7.27. cublasXtCtrmm
CTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H.
integer(4) function cublasXtctrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo, transa, diag integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha
2.7.28. cublasXtCtrsm
CTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B.
integer(4) function cublasXtctrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasXtHandle) :: h integer :: side, uplo, transa, diag integer(kind=c_intptr_t) :: m, n, lda, ldb complex(4), dimension(lda, *) :: a complex(4), dimension(ldb, *) :: b complex(4) :: alpha
2.7.29. cublasXtCspmm
CSPMM performs one of the symmetric packed matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a n by n symmetric matrix stored in packed format, and B and C are m by n matrices.
integer(4) function cublasXtcspmm(h, side, uplo, m, n, alpha, ap, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, ldb, ldc complex(4), dimension(*) :: ap complex(4), dimension(ldb, *) :: b complex(4), dimension(ldc, *) :: c complex(4) :: alpha, beta
2.7.30. cublasXtDgemm
DGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
integer(4) function cublasXtdgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: transa, transb integer(kind=c_intptr_t) :: m, n, k, lda, ldb, ldc real(8), dimension(lda, *) :: a real(8), dimension(ldb, *) :: b real(8), dimension(ldc, *) :: c real(8) :: alpha, beta
2.7.31. cublasXtDsymm
DSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices.
integer(4) function cublasXtdsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc real(8), dimension(lda, *) :: a real(8), dimension(ldb, *) :: b real(8), dimension(ldc, *) :: c real(8) :: alpha, beta
2.7.32. cublasXtDsyrk
DSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
integer(4) function cublasXtdsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldc real(8), dimension(lda, *) :: a real(8), dimension(ldc, *) :: c real(8) :: alpha, beta
2.7.33. cublasXtDsyr2k
DSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
integer(4) function cublasXtdsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc real(8), dimension(lda, *) :: a real(8), dimension(ldb, *) :: b real(8), dimension(ldc, *) :: c real(8) :: alpha, beta
2.7.34. cublasXtDsyrkx
DSYRKX performs a variation of the symmetric rank k update C := alpha*A*B**T + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix stored in lower or upper mode, and A and B are n by k matrices. This routine can be used when B is in such a way that the result is guaranteed to be symmetric. See the CUBLAS documentation for more details.
integer(4) function cublasXtdsyrkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc real(8), dimension(lda, *) :: a real(8), dimension(ldb, *) :: b real(8), dimension(ldc, *) :: c real(8) :: alpha, beta
2.7.35. cublasXtDtrmm
DTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ), where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T.
integer(4) function cublasXtdtrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo, transa, diag integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc real(8), dimension(lda, *) :: a real(8), dimension(ldb, *) :: b real(8), dimension(ldc, *) :: c real(8) :: alpha
2.7.36. cublasXtDtrsm
DTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B.
integer(4) function cublasXtdtrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasXtHandle) :: h integer :: side, uplo, transa, diag integer(kind=c_intptr_t) :: m, n, lda, ldb real(8), dimension(lda, *) :: a real(8), dimension(ldb, *) :: b real(8) :: alpha
2.7.37. cublasXtDspmm
DSPMM performs one of the symmetric packed matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a n by n symmetric matrix stored in packed format, and B and C are m by n matrices.
integer(4) function cublasXtdspmm(h, side, uplo, m, n, alpha, ap, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, ldb, ldc real(8), dimension(*) :: ap real(8), dimension(ldb, *) :: b real(8), dimension(ldc, *) :: c real(8) :: alpha, beta
2.7.38. cublasXtZgemm
ZGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
integer(4) function cublasXtzgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: transa, transb integer(kind=c_intptr_t) :: m, n, k, lda, ldb, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha, beta
2.7.39. cublasXtZhemm
ZHEMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is an hermitian matrix and B and C are m by n matrices.
integer(4) function cublasXtzhemm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha, beta
2.7.40. cublasXtZherk
ZHERK performs one of the hermitian rank k operations C := alpha*A*A**H + beta*C, or C := alpha*A**H*A + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
integer(4) function cublasXtzherk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldc, *) :: c real(8) :: alpha, beta
2.7.41. cublasXtZher2k
ZHER2K performs one of the hermitian rank 2k operations C := alpha*A*B**H + conjg( alpha )*B*A**H + beta*C, or C := alpha*A**H*B + conjg( alpha )*B**H*A + beta*C, where alpha and beta are scalars with beta real, C is an n by n hermitian matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
integer(4) function cublasXtzher2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha real(8) :: beta
2.7.42. cublasXtZherkx
ZHERKX performs a variation of the hermitian rank k operations C := alpha*A*B**H + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix stored in lower or upper mode, and A and B are n by k matrices. See the CUBLAS documentation for more details.
integer(4) function cublasXtzherkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha real(8) :: beta
2.7.43. cublasXtZsymm
ZSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices.
integer(4) function cublasXtzsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha, beta
2.7.44. cublasXtZsyrk
ZSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case.
integer(4) function cublasXtzsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldc, *) :: c complex(8) :: alpha, beta
2.7.45. cublasXtZsyr2k
ZSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case.
integer(4) function cublasXtzsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha, beta
2.7.46. cublasXtZsyrkx
ZSYRKX performs a variation of the symmetric rank k update C := alpha*A*B**T + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix stored in lower or upper mode, and A and B are n by k matrices. This routine can be used when B is in such a way that the result is guaranteed to be symmetric. See the CUBLAS documentation for more details.
integer(4) function cublasXtzsyrkx(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: uplo, trans integer(kind=c_intptr_t) :: n, k, lda, ldb, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha, beta
2.7.47. cublasXtZtrmm
ZTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H.
integer(4) function cublasXtztrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo, transa, diag integer(kind=c_intptr_t) :: m, n, lda, ldb, ldc complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha
2.7.48. cublasXtZtrsm
ZTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B.
integer(4) function cublasXtztrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasXtHandle) :: h integer :: side, uplo, transa, diag integer(kind=c_intptr_t) :: m, n, lda, ldb complex(8), dimension(lda, *) :: a complex(8), dimension(ldb, *) :: b complex(8) :: alpha
2.7.49. cublasXtZspmm
ZSPMM performs one of the symmetric packed matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a n by n symmetric matrix stored in packed format, and B and C are m by n matrices.
integer(4) function cublasXtzspmm(h, side, uplo, m, n, alpha, ap, b, ldb, beta, c, ldc) type(cublasXtHandle) :: h integer :: side, uplo integer(kind=c_intptr_t) :: m, n, ldb, ldc complex(8), dimension(*) :: ap complex(8), dimension(ldb, *) :: b complex(8), dimension(ldc, *) :: c complex(8) :: alpha, beta
2.8. CUBLAS DEVICE Module Functions
This section contains interfaces to the cuBLAS functions accessible from device code. CUDA Fortran users can access this module by inserting the line use cublas_device into the program unit. OpenACC users can access this module by inserting the line use openacc_cublas into the program unit. Examples for making cuBLAS calls from device code are included in Chapter 6.
The cublas_device module and the openacc_cublas module contain all the types and definitions from the cublas module:
TYPE cublasHandle TYPE(C_PTR) :: handle END TYPE
Each device module contains the following enumerations:
enum, bind(c) enumerator :: CUBLAS_STATUS_SUCCESS =0 enumerator :: CUBLAS_STATUS_NOT_INITIALIZED =1 enumerator :: CUBLAS_STATUS_ALLOC_FAILED =3 enumerator :: CUBLAS_STATUS_INVALID_VALUE =7 enumerator :: CUBLAS_STATUS_ARCH_MISMATCH =8 enumerator :: CUBLAS_STATUS_MAPPING_ERROR =11 enumerator :: CUBLAS_STATUS_EXECUTION_FAILED=13 enumerator :: CUBLAS_STATUS_INTERNAL_ERROR =14 end enum
enum, bind(c) enumerator :: CUBLAS_FILL_MODE_LOWER=0 enumerator :: CUBLAS_FILL_MODE_UPPER=1 end enum
enum, bind(c) enumerator :: CUBLAS_DIAG_NON_UNIT=0 enumerator :: CUBLAS_DIAG_UNIT=1 end enum
enum, bind(c) enumerator :: CUBLAS_SIDE_LEFT =0 enumerator :: CUBLAS_SIDE_RIGHT=1 end enum
enum, bind(c) enumerator :: CUBLAS_OP_N=0 enumerator :: CUBLAS_OP_T=1 enumerator :: CUBLAS_OP_C=2 end enum
enum, bind(c) enumerator :: CUBLAS_POINTER_MODE_HOST = 0 enumerator :: CUBLAS_POINTER_MODE_DEVICE = 1 end enum
2.8.1. Device Library Helper Functions
This section contains the cuBLAS interfaces to the device-side single precision BLAS and cuBLAS functions and subroutines.
2.8.1.1. cublasCreate
This function initializes the CUBLAS library and creates a handle to an opaque structure holding the CUBLAS library context. It allocates hardware resources on the host and device and must be called prior to making any other CUBLAS library calls. The CUBLAS library context is tied to the current CUDA device. To use the library on multiple devices, one CUBLAS handle needs to be created for each device. Furthermore, for a given device, multiple CUBLAS handles with different configuration can be created. Because cublasCreate allocates some internal resources and the release of those resources by calling cublasDestroy will implicitly call cublasDeviceSynchronize, it is recommended to minimize the number of cublasCreate/cublasDestroy occurences. For multi-threaded applications that use the same device from different threads, the recommended programming model is to create one CUBLAS handle per thread and use that CUBLAS handle for the entire life of the thread. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCreate(handle) type(cublasHandle) :: handle
2.8.1.2. cublasDestroy
This function releases hardware resources used by the CUBLAS library. This function is usually the last call with a particular handle to the CUBLAS library. Because cublasCreate allocates some internal resources and the release of those resources by calling cublasDestroy will implicitly call cublasDeviceSynchronize, it is recommended to minimize the number of cublasCreate/cublasDestroy occurences. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDestroy(handle) type(cublasHandle) :: handle
2.8.1.3. cublasGetVersion
This function returns the version number of the cuBLAS library. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasGetVersion(handle, version) type(cublasHandle) :: handle integer(4) :: version
2.8.1.4. cublasSetStream
This function sets the cuBLAS library stream, which will be used to execute all subsequent calls to the cuBLAS library functions. If the cuBLAS library stream is not set, all kernels use the default NULL stream. In particular, this routine can be used to change the stream between kernel launches and then to reset the cuBLAS library stream back to NULL. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasSetStream(handle, stream) type(cublasHandle) :: handle integer(kind=cuda_stream_kind()) :: stream
2.8.1.5. cublasGetStream
This function gets the cuBLAS library stream, which is being used to execute all calls to the cuBLAS library functions. If the cuBLAS library stream is not set, all kernels use the default NULL stream. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasGetStream(handle, stream) type(cublasHandle) :: handle integer(kind=cuda_stream_kind()) :: stream
2.8.2. Single Precision Functions and Subroutines
This section contains the cuBLAS interfaces to the device-side single precision BLAS and cuBLAS functions and subroutines.
2.8.2.1. cublasIsamax
ISAMAX finds the index of the first element having maximum absolute value. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasisamax(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx integer :: res
2.8.2.2. cublasIsamin
ISAMIN finds the index of the first element having minimum absolute value. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasisamin(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx integer :: res
2.8.2.3. cublasSasum
SASUM takes the sum of the absolute values. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassasum(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx real(4) :: res
2.8.2.4. cublasSaxpy
SAXPY constant times a vector plus a vector. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassaxpy(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4) :: a real(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.2.5. cublasScopy
SCOPY copies a vector, x, to a vector, y. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasscopy(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.2.6. cublasSdot
SDOT forms the dot product of two vectors. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassdot(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy real(4) :: res
2.8.2.7. cublasSnrm2
SNRM2 returns the euclidean norm of a vector via the function name, so that SNRM2 := sqrt( x'*x ). Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassnrm2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x integer :: incx real(4) :: res
2.8.2.8. cublasSrot
SROT applies a plane rotation. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassrot(h, n, x, incx, y, incy, sc, ss) type(cublasHandle) :: h integer :: n real(4) :: sc, ss real(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.2.9. cublasSrotg
SROTG constructs a Givens plane rotation. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassrotg(h, sa, sb, sc, ss) type(cublasHandle) :: h real(4) :: sa, sb, sc, ss
2.8.2.10. cublasSrotm
SROTM applies the modified Givens transformation, H, to the 2 by N matrix (SX**T) , where **T indicates transpose. The elements of SX are in (SX**T) SX(LX+I*INCX), I = 0 to N-1, where LX = 1 if INCX .GE. 0, ELSE LX = (-INCX)*N, and similarly for SY using LY and INCY. With SPARAM(1)=SFLAG, H has one of the following forms.. SFLAG=-1.E0 SFLAG=0.E0 SFLAG=1.E0 SFLAG=-2.E0 (SH11 SH12) (1.E0 SH12) (SH11 1.E0) (1.E0 0.E0) H=( ) ( ) ( ) ( ) (SH21 SH22), (SH21 1.E0), (-1.E0 SH22), (0.E0 1.E0). See SROTMG for a description of data storage in SPARAM. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassrotm(h, n, x, incx, y, incy, param) type(cublasHandle) :: h integer :: n real(4) :: param(*) real(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.2.11. cublasSrotmg
SFLAG=-1.E0 SFLAG=0.E0 SFLAG=1.E0 SFLAG=-2.E0 (SH11 SH12) (1.E0 SH12) (SH11 1.E0) (1.E0 0.E0) H=( ) ( ) ( ) ( ) (SH21 SH22), (SH21 1.E0), (-1.E0 SH22), (0.E0 1.E0).Locations 2-4 of SPARAM contain SH11,SH21,SH12, and SH22 respectively. (Values of 1.E0, -1.E0, or 0.E0 implied by the value of SPARAM(1) are not stored in SPARAM.) Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassrotmg(h, d1, d2, x1, y1, param) type(cublasHandle) :: h real(4) :: d1, d2, x1, y1, param(*)
2.8.2.12. cublasSscal
SSCAL scales a vector by a constant. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(4) :: a real(4), device, dimension(*) :: x integer :: incx
2.8.2.13. cublasSswap
SSWAP interchanges two vectors. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassswap(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.2.14. cublasSgbmv
SGBMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n band matrix, with kl sub-diagonals and ku super-diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassgbmv(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4) :: alpha, beta
2.8.2.15. cublasSgemv
SGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassgemv(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4) :: alpha, beta
2.8.2.16. cublasSger
SGER performs the rank 1 operation A := alpha*x*y**T + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassger(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4) :: alpha
2.8.2.17. cublasSsbmv
SSBMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric band matrix, with k super-diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasssbmv(h, t, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: k, n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4) :: alpha, beta
2.8.2.18. cublasSspmv
SSPMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassspmv(h, t, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y real(4) :: alpha, beta
2.8.2.19. cublasSspr
SSPR performs the symmetric rank 1 operation A := alpha*x*x**T + A, where alpha is a real scalar, x is an n element vector and A is an n by n symmetric matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassspr(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx real(4), device, dimension(*) :: a, x real(4) :: alpha
2.8.2.20. cublasSspr2
SSPR2 performs the symmetric rank 2 operation A := alpha*x*y**T + alpha*y*x**T + A, where alpha is a scalar, x and y are n element vectors and A is an n by n symmetric matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassspr2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(4), device, dimension(*) :: a, x, y real(4) :: alpha
2.8.2.21. cublasSsymv
SSYMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasssymv(h, t, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, lda, incx, incy real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4) :: alpha, beta
2.8.2.22. cublasSsyr
SSYR performs the symmetric rank 1 operation A := alpha*x*x**T + A, where alpha is a real scalar, x is an n element vector and A is an n by n symmetric matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasssyr(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x real(4) :: alpha
2.8.2.23. cublasSsyr2
SSYR2 performs the symmetric rank 2 operation A := alpha*x*y**T + alpha*y*x**T + A, where alpha is a scalar, x and y are n element vectors and A is an n by n symmetric matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasssyr2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x, y real(4) :: alpha
2.8.2.24. cublasStbmv
STBMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasstbmv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.8.2.25. cublasStbsv
STBSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasstbsv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.8.2.26. cublasStpmv
STPMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasstpmv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x
2.8.2.27. cublasStpsv
STPSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasstpsv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(4), device, dimension(*) :: a, x
2.8.2.28. cublasStrmv
STRMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasstrmv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.8.2.29. cublasStrsv
STRSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasstrsv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(4), device, dimension(lda, *) :: a real(4), device, dimension(*) :: x
2.8.2.30. cublasSgemm
SGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublassgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4) :: alpha, beta
2.8.2.31. cublasSsymm
SSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasssymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4) :: alpha, beta
2.8.2.32. cublasSsyrk
SSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasssyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldc, *) :: c real(4) :: alpha, beta
2.8.2.33. cublasSsyr2k
SSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasssyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4) :: alpha, beta
2.8.2.34. cublasStrmm
STRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ), where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasstrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4), device, dimension(ldc, *) :: c real(4) :: alpha
2.8.2.35. cublasStrsm
STRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasstrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb real(4), device, dimension(lda, *) :: a real(4), device, dimension(ldb, *) :: b real(4) :: alpha
2.8.2.36. cublasSgemmBatched
SGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasSgemmBatched(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa integer :: transb integer :: m, n, k real(4) :: alpha type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb real(4) :: beta type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
2.8.2.37. cublasSgetrfBatched
SGETRF computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges. The factorization has the form A = P * L * U where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n). This is the right-looking Level 3 BLAS version of the algorithm. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasSgetrfBatched(h, n, Aarray, lda, ipvt, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) integer, device :: info(*) integer :: batchCount
2.8.2.38. cublasSgetriBatched
SGETRI computes the inverse of a matrix using the LU factorization computed by SGETRF. This method inverts U and then computes inv(A) by solving the system inv(A)*L = inv(U) for inv(A). Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasSgetriBatched(h, n, Aarray, lda, ipvt, Carray, ldc, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Carray(*) integer :: ldc integer, device :: info(*) integer :: batchCount
2.8.2.39. cublasSgetrsBatched
SGETRS solves a system of linear equations A * X = B or A**T * X = B with a general N-by-N matrix A using the LU factorization computed by SGETRF. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasSgetrsBatched(h, trans, n, nrhs, A, lda, ipvt, B, ldb, info, batchCount) type(cublasHandle) :: h integer :: trans integer :: n, nrhs type(c_devptr), device :: A(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: B(*) integer :: ldb integer, device :: info(*) integer :: batchCount
2.8.2.40. cublasStrsmBatched
STRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasStrsmBatched(h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side integer :: uplo integer :: trans integer :: diag integer :: m, n real(4) :: alpha type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
2.8.2.41. cublasSmatinvBatched
cublasSmatinvBatched is a short cut of cublasSgetrfBatched plus cublasSgetriBatched. However it only works if n is less than 32. If not, the user has to go through cublasSgetrfBatched and cublasSgetriBatched. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasSmatinvBatched(h, n, A, lda, Ainv, lda_inv, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: Ainv(*) integer :: lda_inv integer, device :: info(*) integer :: batchCount
2.8.2.42. cublasSgeqrfBatched
SGEQRF computes a QR factorization of a real M-by-N matrix A: A = Q * R. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasSgeqrfBatched(h, m, n, A, lda, Tau, info, batchCount) type(cublasHandle) :: h integer :: m, n type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: Tau(*) integer, device :: info(*) integer :: batchCount
2.8.2.43. cublasSgelsBatched
SGELS solves overdetermined or underdetermined real linear systems involving an M-by-N matrix A, or its transpose, using a QR or LQ factorization of A. It is assumed that A has full rank. The following options are provided: 1. If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A*X ||. 2. If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B. 3. If TRANS = 'T' and m >= n: find the minimum norm solution of an undetermined system A**T * X = B. 4. If TRANS = 'T' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A**T * X ||. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasSgelsBatched(h, trans, m, n, nrhs, A, lda, C, ldc, info, dinfo, batchCount) type(cublasHandle) :: h integer :: trans integer :: m, n, nrhs type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: C(*) integer :: ldc integer, device :: info(*) integer, device :: dinfo(*) integer :: batchCount
2.8.3. Single Precision Complex Functions and Subroutines
This section contains the cuBLAS interfaces to the device-side single precision complex BLAS and cuBLAS functions and subroutines.
2.8.3.1. cublasCaxpy
CAXPY constant times a vector plus a vector. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascaxpy(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4) :: a complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.3.2. cublasCcopy
CCOPY copies a vector x to a vector y. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasccopy(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.3.3. cublasCdotc
forms the dot product of two vectors, conjugating the first vector. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascdotc(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy complex(4) :: res
2.8.3.4. cublasCdotu
CDOTU forms the dot product of two vectors. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascdotu(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy complex(4) :: res
2.8.3.5. cublasCrot
CROT applies a plane rotation, where the cos (C) is real and the sin (S) is complex, and the vectors CX and CY are complex. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascrot(h, n, x, incx, y, incy, sc, cs) type(cublasHandle) :: h integer :: n real(4) :: sc complex(4) :: cs complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.3.6. cublasCscal
CSCAL scales a vector by a constant. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n complex(4) :: a complex(4), device, dimension(*) :: x integer :: incx
2.8.3.7. cublasCsscal
CSSCAL scales a complex vector by a real constant. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascsscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(4) :: a complex(4), device, dimension(*) :: x integer :: incx
2.8.3.8. cublasCswap
CSWAP interchanges two vectors. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascswap(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x, y integer :: incx, incy
2.8.3.9. cublasIcamax
ICAMAX finds the index of the first element having maximum absolute value. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasicamax(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx integer :: res
2.8.3.10. cublasIcamin
ICAMIN finds the index of the first element having minimum absolute value. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasicamin(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx integer :: res
2.8.3.11. cublasScasum
SCASUM takes the sum of the absolute values of a complex vector and returns a single precision result. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasscasum(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx real(4) :: res
2.8.3.12. cublasScnrm2
SCNRM2 returns the euclidean norm of a vector via the function name, so that SCNRM2 := sqrt( x**H*x ) Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasscnrm2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(4), device, dimension(*) :: x integer :: incx real(4) :: res
2.8.3.13. cublasCgbmv
CGBMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n band matrix, with kl sub-diagonals and ku super-diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascgbmv(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4) :: alpha, beta
2.8.3.14. cublasCgemv
CGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascgemv(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4) :: alpha, beta
2.8.3.15. cublasCgerc
CGERC performs the rank 1 operation A := alpha*x*y**H + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascgerc(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4) :: alpha
2.8.3.16. cublasCgeru
CGERU performs the rank 1 operation A := alpha*x*y**T + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascgeru(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4) :: alpha
2.8.3.17. cublasChbmv
CHBMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian band matrix, with k super-diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaschbmv(h, t, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: k, n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4) :: alpha, beta
2.8.3.18. cublasChemv
CHEMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaschemv(h, t, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, lda, incx, incy complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4) :: alpha, beta
2.8.3.19. cublasCher
CHER performs the hermitian rank 1 operation A := alpha*x*x**H + A, where alpha is a real scalar, x is an n element vector and A is an n by n hermitian matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascher(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x real(4) :: alpha
2.8.3.20. cublasCher2
CHER2 performs the hermitian rank 2 operation A := alpha*x*y**H + conjg( alpha )*y*x**H + A, where alpha is a scalar, x and y are n element vectors and A is an n by n hermitian matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascher2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x, y complex(4) :: alpha
2.8.3.21. cublasChpmv
CHPMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaschpmv(h, t, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y complex(4) :: alpha, beta
2.8.3.22. cublasChpr
CHPR performs the hermitian rank 1 operation A := alpha*x*x**H + A, where alpha is a real scalar, x is an n element vector and A is an n by n hermitian matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaschpr(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx complex(4), device, dimension(*) :: a, x real(4) :: alpha
2.8.3.23. cublasChpr2
CHPR2 performs the hermitian rank 2 operation A := alpha*x*y**H + conjg( alpha )*y*x**H + A, where alpha is a scalar, x and y are n element vectors and A is an n by n hermitian matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaschpr2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy complex(4), device, dimension(*) :: a, x, y complex(4) :: alpha
2.8.3.24. cublasCtbmv
CTBMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasctbmv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.8.3.25. cublasCtbsv
CTBSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasctbsv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.8.3.26. cublasCtpmv
CTPMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasctpmv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x
2.8.3.27. cublasCtpsv
CTPSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasctpsv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(4), device, dimension(*) :: a, x
2.8.3.28. cublasCtrmv
CTRMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasctrmv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.8.3.29. cublasCtrsv
CTRSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasctrsv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(*) :: x
2.8.3.30. cublasCgemm
CGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4) :: alpha, beta
2.8.3.31. cublasChemm
CHEMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is an hermitian matrix and B and C are m by n matrices. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaschemm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4) :: alpha, beta
2.8.3.32. cublasCherk
CHERK performs one of the hermitian rank k operations C := alpha*A*A**H + beta*C, or C := alpha*A**H*A + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascherk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldc, *) :: c real(4) :: alpha, beta
2.8.3.33. cublasCher2k
CHER2K performs one of the hermitian rank 2k operations C := alpha*A*B**H + conjg( alpha )*B*A**H + beta*C, or C := alpha*A**H*B + conjg( alpha )*B**H*A + beta*C, where alpha and beta are scalars with beta real, C is an n by n hermitian matrix and A and B are n by k matrices in the first case and k by n matrices in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascher2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4) :: alpha real(4) :: beta
2.8.3.34. cublasCsymm
CSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4) :: alpha, beta
2.8.3.35. cublasCsyrk
CSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldc, *) :: c complex(4) :: alpha, beta
2.8.3.36. cublasCsyr2k
CSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublascsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4) :: alpha, beta
2.8.3.37. cublasCtrmm
CTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasctrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4), device, dimension(ldc, *) :: c complex(4) :: alpha
2.8.3.38. cublasCtrsm
CTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasctrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(4), device, dimension(lda, *) :: a complex(4), device, dimension(ldb, *) :: b complex(4) :: alpha
2.8.3.39. cublasCgemmBatched
CGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCgemmBatched(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa integer :: transb integer :: m, n, k complex(4) :: alpha type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb complex(4) :: beta type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
2.8.3.40. cublasCgetrfBatched
CGETRF computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges. The factorization has the form A = P * L * U where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n). This is the right-looking Level 3 BLAS version of the algorithm. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCgetrfBatched(h, n, Aarray, lda, ipvt, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) integer, device :: info(*) integer :: batchCount
2.8.3.41. cublasCgetriBatched
CGETRI computes the inverse of a matrix using the LU factorization computed by CGETRF. This method inverts U and then computes inv(A) by solving the system inv(A)*L = inv(U) for inv(A). Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCgetriBatched(h, n, Aarray, lda, ipvt, Carray, ldc, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Carray(*) integer :: ldc integer, device :: info(*) integer :: batchCount
2.8.3.42. cublasCgetrsBatched
CGETRS solves a system of linear equations A * X = B, A**T * X = B, or A**H * X = B with a general N-by-N matrix A using the LU factorization computed by CGETRF. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCgetrsBatched(h, trans, n, nrhs, A, lda, ipvt, B, ldb, info, batchCount) type(cublasHandle) :: h integer :: trans integer :: n, nrhs type(c_devptr), device :: A(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: B(*) integer :: ldb integer, device :: info(*) integer :: batchCount
2.8.3.43. cublasCtrsmBatched
CTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCtrsmBatched(h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side integer :: uplo integer :: trans integer :: diag integer :: m, n complex(4) :: alpha type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
2.8.3.44. cublasCmatinvBatched
cublasCmatinvBatched is a short cut of cublasCgetrfBatched plus cublasCgetriBatched. However it only works if n is less than 32. If not, the user has to go through cublasCgetrfBatched and cublasCgetriBatched. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCmatinvBatched(h, n, A, lda, Ainv, lda_inv, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: Ainv(*) integer :: lda_inv integer, device :: info(*) integer :: batchCount
2.8.3.45. cublasCgeqrfBatched
CGEQRF computes a QR factorization of a complex M-by-N matrix A: A = Q * R. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCgeqrfBatched(h, m, n, A, lda, Tau, info, batchCount) type(cublasHandle) :: h integer :: m, n type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: Tau(*) integer, device :: info(*) integer :: batchCount
2.8.3.46. cublasCgelsBatched
CGELS solves overdetermined or underdetermined complex linear systems involving an M-by-N matrix A, or its conjugate-transpose, using a QR or LQ factorization of A. It is assumed that A has full rank. The following options are provided: 1. If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A*X ||. 2. If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B. 3. If TRANS = 'C' and m >= n: find the minimum norm solution of an undetermined system A**H * X = B. 4. If TRANS = 'C' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A**H * X ||. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasCgelsBatched(h, trans, m, n, nrhs, A, lda, C, ldc, info, dinfo, batchCount) type(cublasHandle) :: h integer :: trans integer :: m, n, nrhs type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: C(*) integer :: ldc integer, device :: info(*) integer, device :: dinfo(*) integer :: batchCount
2.8.4. Double Precision Functions and Subroutines
This section contains the cuBLAS interfaces to the device-side double precision BLAS and cuBLAS functions and subroutines.
2.8.4.1. cublasIdamax
IDAMAX finds the the index of the first element having maximum absolute value. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasidamax(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx integer :: res
2.8.4.2. cublasIdamin
IDAMIN finds the index of the first element having minimum absolute value. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasidamin(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx integer :: res
2.8.4.3. cublasDasum
DASUM takes the sum of the absolute values. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdasum(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx real(8) :: res
2.8.4.4. cublasDaxpy
DAXPY constant times a vector plus a vector. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdaxpy(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8) :: a real(8), device, dimension(*) :: x, y integer :: incx, incy
2.8.4.5. cublasDcopy
DCOPY copies a vector, x, to a vector, y. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdcopy(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
2.8.4.6. cublasDdot
DDOT forms the dot product of two vectors. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasddot(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy real(8) :: res
2.8.4.7. cublasDnrm2
DNRM2 returns the euclidean norm of a vector via the function name, so that DNRM2 := sqrt( x'*x ) Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdnrm2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x integer :: incx real(8) :: res
2.8.4.8. cublasDrot
DROT applies a plane rotation. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdrot(h, n, x, incx, y, incy, dc, ds) type(cublasHandle) :: h integer :: n real(8) :: dc, ds real(8), device, dimension(*) :: x, y integer :: incx, incy
2.8.4.9. cublasDrotg
DROTG constructs a Givens plane rotation. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdrotg(h, sa, sb, sc, ss) type(cublasHandle) :: h real(8) :: sa, sb, sc, ss
2.8.4.10. cublasDrotm
DROTM applies the modified Givens transformation, H, to the 2 by N matrix (DX**T) , where **T indicates transpose. The elements of DX are in (DX**T) DX(LX+I*INCX), I = 0 to N-1, where LX = 1 if INCX .GE. 0, ELSE LX = (-INCX)*N, and similarly for DY using LY and INCY. With DPARAM(1)=DFLAG, H has one of the following forms.. DFLAG=-1.D0 DFLAG=0.D0 DFLAG=1.D0 DFLAG=-2.D0 (DH11 DH12) (1.D0 DH12) (DH11 1.D0) (1.D0 0.D0) H=( ) ( ) ( ) ( ) (DH21 DH22), (DH21 1.D0), (-1.D0 DH22), (0.D0 1.D0). See DROTMG for a description of data storage in DPARAM. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdrotm(h, n, x, incx, y, incy, param) type(cublasHandle) :: h integer :: n real(8) :: param(*) real(8), device, dimension(*) :: x, y integer :: incx, incy
2.8.4.11. cublasDrotmg
DFLAG=-1.D0 DFLAG=0.D0 DFLAG=1.D0 DFLAG=-2.D0 (DH11 DH12) (1.D0 DH12) (DH11 1.D0) (1.D0 0.D0) H=( ) ( ) ( ) ( ) (DH21 DH22), (DH21 1.D0), (-1.D0 DH22), (0.D0 1.D0).Locations 2-4 of DPARAM contain DH11, DH21, DH12, and DH22 respectively. (Values of 1.D0, -1.D0, of 0.D0 implied by the value of DPARAM(1) are not stored in DPARAM.) Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdrotmg(h, d1, d2, x1, y1, param) type(cublasHandle) :: h real(8) :: d1, d2, x1, y1, param(*)
2.8.4.12. cublasDscal
DSCAL scales a vector by a constant. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(8) :: a real(8), device, dimension(*) :: x integer :: incx
2.8.4.13. cublasDswap
interchanges two vectors. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdswap(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n real(8), device, dimension(*) :: x, y integer :: incx, incy
2.8.4.14. cublasDgbmv
DGBMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n band matrix, with kl sub-diagonals and ku super-diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdgbmv(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8) :: alpha, beta
2.8.4.15. cublasDgemv
DGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdgemv(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8) :: alpha, beta
2.8.4.16. cublasDger
DGER performs the rank 1 operation A := alpha*x*y**T + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdger(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8) :: alpha
2.8.4.17. cublasDsbmv
DSBMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric band matrix, with k super-diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdsbmv(h, t, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: k, n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8) :: alpha, beta
2.8.4.18. cublasDspmv
DSPMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdspmv(h, t, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y real(8) :: alpha, beta
2.8.4.19. cublasDspr
DSPR performs the symmetric rank 1 operation A := alpha*x*x**T + A, where alpha is a real scalar, x is an n element vector and A is an n by n symmetric matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdspr(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx real(8), device, dimension(*) :: a, x real(8) :: alpha
2.8.4.20. cublasDspr2
DSPR2 performs the symmetric rank 2 operation A := alpha*x*y**T + alpha*y*x**T + A, where alpha is a scalar, x and y are n element vectors and A is an n by n symmetric matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdspr2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy real(8), device, dimension(*) :: a, x, y real(8) :: alpha
2.8.4.21. cublasDsymv
DSYMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n symmetric matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdsymv(h, t, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, lda, incx, incy real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8) :: alpha, beta
2.8.4.22. cublasDsyr
DSYR performs the symmetric rank 1 operation A := alpha*x*x**T + A, where alpha is a real scalar, x is an n element vector and A is an n by n symmetric matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdsyr(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x real(8) :: alpha
2.8.4.23. cublasDsyr2
DSYR2 performs the symmetric rank 2 operation A := alpha*x*y**T + alpha*y*x**T + A, where alpha is a scalar, x and y are n element vectors and A is an n by n symmetric matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdsyr2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x, y real(8) :: alpha
2.8.4.24. cublasDtbmv
DTBMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdtbmv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.8.4.25. cublasDtbsv
DTBSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdtbsv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.8.4.26. cublasDtpmv
DTPMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdtpmv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x
2.8.4.27. cublasDtpsv
DTPSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdtpsv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx real(8), device, dimension(*) :: a, x
2.8.4.28. cublasDtrmv
DTRMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdtrmv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.8.4.29. cublasDtrsv
DTRSV solves one of the systems of equations A*x = b, or A**T*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdtrsv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda real(8), device, dimension(lda, *) :: a real(8), device, dimension(*) :: x
2.8.4.30. cublasDgemm
DGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8) :: alpha, beta
2.8.4.31. cublasDsymm
DSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8) :: alpha, beta
2.8.4.32. cublasDsyrk
DSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldc, *) :: c real(8) :: alpha, beta
2.8.4.33. cublasDsyr2k
DSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8) :: alpha, beta
2.8.4.34. cublasDtrmm
DTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ), where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdtrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8), device, dimension(ldc, *) :: c real(8) :: alpha
2.8.4.35. cublasDtrsm
DTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdtrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb real(8), device, dimension(lda, *) :: a real(8), device, dimension(ldb, *) :: b real(8) :: alpha
2.8.4.36. cublasDgemmBatched
DGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDgemmBatched(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa integer :: transb integer :: m, n, k real(8) :: alpha type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb real(8) :: beta type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
2.8.4.37. cublasDgetrfBatched
DGETRF computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges. The factorization has the form A = P * L * U where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n). This is the right-looking Level 3 BLAS version of the algorithm. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDgetrfBatched(h, n, Aarray, lda, ipvt, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) integer, device :: info(*) integer :: batchCount
2.8.4.38. cublasDgetriBatched
DGETRI computes the inverse of a matrix using the LU factorization computed by DGETRF. This method inverts U and then computes inv(A) by solving the system inv(A)*L = inv(U) for inv(A). Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDgetriBatched(h, n, Aarray, lda, ipvt, Carray, ldc, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Carray(*) integer :: ldc integer, device :: info(*) integer :: batchCount
2.8.4.39. cublasDgetrsBatched
DGETRS solves a system of linear equations A * X = B or A**T * X = B with a general N-by-N matrix A using the LU factorization computed by DGETRF. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDgetrsBatched(h, trans, n, nrhs, A, lda, ipvt, B, ldb, info, batchCount) type(cublasHandle) :: h integer :: trans integer :: n, nrhs type(c_devptr), device :: A(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: B(*) integer :: ldb integer, device :: info(*) integer :: batchCount
2.8.4.40. cublasDtrsmBatched
DTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDtrsmBatched(h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side integer :: uplo integer :: trans integer :: diag integer :: m, n real(8) :: alpha type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
2.8.4.41. cublasDmatinvBatched
cublasDmatinvBatched is a short cut of cublasDgetrfBatched plus cublasDgetriBatched. However it only works if n is less than 32. If not, the user has to go through cublasDgetrfBatched and cublasDgetriBatched. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDmatinvBatched(h, n, A, lda, Ainv, lda_inv, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: Ainv(*) integer :: lda_inv integer, device :: info(*) integer :: batchCount
2.8.4.42. cublasDgeqrfBatched
DGEQRF computes a QR factorization of a real M-by-N matrix A: A = Q * R. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDgeqrfBatched(h, m, n, A, lda, Tau, info, batchCount) type(cublasHandle) :: h integer :: m, n type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: Tau(*) integer, device :: info(*) integer :: batchCount
2.8.4.43. cublasDgelsBatched
DGELS solves overdetermined or underdetermined real linear systems involving an M-by-N matrix A, or its transpose, using a QR or LQ factorization of A. It is assumed that A has full rank. The following options are provided: 1. If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A*X ||. 2. If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B. 3. If TRANS = 'T' and m >= n: find the minimum norm solution of an undetermined system A**T * X = B. 4. If TRANS = 'T' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A**T * X ||. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasDgelsBatched(h, trans, m, n, nrhs, A, lda, C, ldc, info, dinfo, batchCount) type(cublasHandle) :: h integer :: trans integer :: m, n, nrhs type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: C(*) integer :: ldc integer, device :: info(*) integer, device :: dinfo(*) integer :: batchCount
2.8.5. Double Precision Complex Functions and Subroutines
This section contains the cuBLAS interfaces to the device-side double precision complex BLAS and cuBLAS functions and subroutines.
2.8.5.1. cublasDzasum
DZASUM takes the sum of the absolute values. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdzasum(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx real(8) :: res
2.8.5.2. cublasDznrm2
DZNRM2 returns the euclidean norm of a vector via the function name, so that DZNRM2 := sqrt( x**H*x ) Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasdznrm2(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx real(8) :: res
2.8.5.3. cublasIzamax
IZAMAX finds the index of the first element having maximum absolute value. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasizamax(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx integer :: res
2.8.5.4. cublasIzamin
IZAMIN finds the index of the first element having minimum absolute value. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasizamin(h, n, x, incx, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x integer :: incx integer :: res
2.8.5.5. cublasZaxpy
ZAXPY constant times a vector plus a vector. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszaxpy(h, n, a, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8) :: a complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.8.5.6. cublasZcopy
ZCOPY copies a vector, x, to a vector, y. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszcopy(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.8.5.7. cublasZdotc
ZDOTC forms the dot product of a vector. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszdotc(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy complex(8) :: res
2.8.5.8. cublasZdotu
ZDOTU forms the dot product of two vectors. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszdotu(h, n, x, incx, y, incy, res) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy complex(8) :: res
2.8.5.9. cublasZdrot
Applies a plane rotation, where the cos and sin (c and s) are real and the vectors cx and cy are complex. jack dongarra, linpack, 3/11/78. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszdrot(h, n, x, incx, y, incy, sc, cs) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy real(8) :: sc, cs
2.8.5.10. cublasZrot
ZROT applies a plane rotation, where the cos (C) is real and the sin (S) is complex, and the vectors CX and CY are complex. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszrot(h, n, x, incx, y, incy, sc, cs) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy real(8) :: sc complex(4) :: cs
2.8.5.11. cublasZscal
ZSCAL scales a vector by a constant. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n complex(8) :: a complex(8), device, dimension(*) :: x integer :: incx
2.8.5.12. cublasZdscal
ZDSCAL scales a vector by a constant. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszdscal(h, n, a, x, incx) type(cublasHandle) :: h integer :: n real(8) :: a complex(8), device, dimension(*) :: x integer :: incx
2.8.5.13. cublasZswap
ZSWAP interchanges two vectors. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszswap(h, n, x, incx, y, incy) type(cublasHandle) :: h integer :: n complex(8), device, dimension(*) :: x, y integer :: incx, incy
2.8.5.14. cublasZgbmv
ZGBMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n band matrix, with kl sub-diagonals and ku super-diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszgbmv(h, t, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, kl, ku, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8) :: alpha, beta
2.8.5.15. cublasZgemv
ZGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszgemv(h, t, m, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8) :: alpha, beta
2.8.5.16. cublasZgerc
ZGERC performs the rank 1 operation A := alpha*x*y**H + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszgerc(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8) :: alpha
2.8.5.17. cublasZgeru
ZGERU performs the rank 1 operation A := alpha*x*y**T + A, where alpha is a scalar, x is an m element vector, y is an n element vector and A is an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszgeru(h, m, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: m, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8) :: alpha
2.8.5.18. cublasZhbmv
ZHBMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian band matrix, with k super-diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszhbmv(h, t, n, k, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: k, n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8) :: alpha, beta
2.8.5.19. cublasZhemv
ZHEMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszhemv(h, t, n, alpha, a, lda, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, lda, incx, incy complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8) :: alpha, beta
2.8.5.20. cublasZher
ZHER performs the hermitian rank 1 operation A := alpha*x*x**H + A, where alpha is a real scalar, x is an n element vector and A is an n by n hermitian matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszher(h, t, n, alpha, x, incx, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x real(8) :: alpha
2.8.5.21. cublasZher2
ZHER2 performs the hermitian rank 2 operation A := alpha*x*y**H + conjg( alpha )*y*x**H + A, where alpha is a scalar, x and y are n element vectors and A is an n by n hermitian matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszher2(h, t, n, alpha, x, incx, y, incy, a, lda) type(cublasHandle) :: h integer :: t integer :: n, incx, incy, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x, y complex(8) :: alpha
2.8.5.22. cublasZhpmv
ZHPMV performs the matrix-vector operation y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n hermitian matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszhpmv(h, t, n, alpha, a, x, incx, beta, y, incy) type(cublasHandle) :: h integer :: t integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y complex(8) :: alpha, beta
2.8.5.23. cublasZhpr
ZHPR performs the hermitian rank 1 operation A := alpha*x*x**H + A, where alpha is a real scalar, x is an n element vector and A is an n by n hermitian matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszhpr(h, t, n, alpha, x, incx, a) type(cublasHandle) :: h integer :: t integer :: n, incx complex(8), device, dimension(*) :: a, x real(8) :: alpha
2.8.5.24. cublasZhpr2
ZHPR2 performs the hermitian rank 2 operation A := alpha*x*y**H + conjg( alpha )*y*x**H + A, where alpha is a scalar, x and y are n element vectors and A is an n by n hermitian matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszhpr2(h, t, n, alpha, x, incx, y, incy, a) type(cublasHandle) :: h integer :: t integer :: n, incx, incy complex(8), device, dimension(*) :: a, x, y complex(8) :: alpha
2.8.5.25. cublasZtbmv
ZTBMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasztbmv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.8.5.26. cublasZtbsv
ZTBSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular band matrix, with ( k + 1 ) diagonals. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasztbsv(h, u, t, d, n, k, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, k, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.8.5.27. cublasZtpmv
ZTPMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasztpmv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x
2.8.5.28. cublasZtpsv
ZTPSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in packed form. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasztpsv(h, u, t, d, n, a, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx complex(8), device, dimension(*) :: a, x
2.8.5.29. cublasZtrmv
ZTRMV performs one of the matrix-vector operations x := A*x, or x := A**T*x, or x := A**H*x, where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasztrmv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.8.5.30. cublasZtrsv
ZTRSV solves one of the systems of equations A*x = b, or A**T*x = b, or A**H*x = b, where b and x are n element vectors and A is an n by n unit, or non-unit, upper or lower triangular matrix. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasztrsv(h, u, t, d, n, a, lda, x, incx) type(cublasHandle) :: h integer :: u, t, d integer :: n, incx, lda complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(*) :: x
2.8.5.31. cublasZgemm
ZGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszgemm(h, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: transa, transb integer :: m, n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8) :: alpha, beta
2.8.5.32. cublasZhemm
ZHEMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is an hermitian matrix and B and C are m by n matrices. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszhemm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8) :: alpha, beta
2.8.5.33. cublasZherk
ZHERK performs one of the hermitian rank k operations C := alpha*A*A**H + beta*C, or C := alpha*A**H*A + beta*C, where alpha and beta are real scalars, C is an n by n hermitian matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszherk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldc, *) :: c real(8) :: alpha, beta
2.8.5.34. cublasZher2k
ZHER2K performs one of the hermitian rank 2k operations C := alpha*A*B**H + conjg( alpha )*B*A**H + beta*C, or C := alpha*A**H*B + conjg( alpha )*B**H*A + beta*C, where alpha and beta are scalars with beta real, C is an n by n hermitian matrix and A and B are n by k matrices in the first case and k by n matrices in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszher2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8) :: alpha real(8) :: beta
2.8.5.35. cublasZsymm
ZSYMM performs one of the matrix-matrix operations C := alpha*A*B + beta*C, or C := alpha*B*A + beta*C, where alpha and beta are scalars, A is a symmetric matrix and B and C are m by n matrices. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszsymm(h, side, uplo, m, n, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: side, uplo integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8) :: alpha, beta
2.8.5.36. cublasZsyrk
ZSYRK performs one of the symmetric rank k operations C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszsyrk(h, uplo, trans, n, k, alpha, a, lda, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldc, *) :: c complex(8) :: alpha, beta
2.8.5.37. cublasZsyr2k
ZSYR2K performs one of the symmetric rank 2k operations C := alpha*A*B**T + alpha*B*A**T + beta*C, or C := alpha*A**T*B + alpha*B**T*A + beta*C, where alpha and beta are scalars, C is an n by n symmetric matrix and A and B are n by k matrices in the first case and k by n matrices in the second case. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublaszsyr2k(h, uplo, trans, n, k, alpha, a, lda, b, ldb, beta, c, ldc) type(cublasHandle) :: h integer :: uplo, trans integer :: n, k, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8) :: alpha, beta
2.8.5.38. cublasZtrmm
ZTRMM performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasztrmm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb, c, ldc) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb, ldc complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8), device, dimension(ldc, *) :: c complex(8) :: alpha
2.8.5.39. cublasZtrsm
ZTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasztrsm(h, side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) type(cublasHandle) :: h integer :: side, uplo, transa, diag integer :: m, n, lda, ldb complex(8), device, dimension(lda, *) :: a complex(8), device, dimension(ldb, *) :: b complex(8) :: alpha
2.8.5.40. cublasZgemmBatched
ZGEMM performs one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasZgemmBatched(h, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount) type(cublasHandle) :: h integer :: transa integer :: transb integer :: m, n, k complex(8) :: alpha type(c_devptr), device :: Aarray(*) integer :: lda type(c_devptr), device :: Barray(*) integer :: ldb complex(8) :: beta type(c_devptr), device :: Carray(*) integer :: ldc integer :: batchCount
2.8.5.41. cublasZgetrfBatched
ZGETRF computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges. The factorization has the form A = P * L * U where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n). This is the right-looking Level 3 BLAS version of the algorithm. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasZgetrfBatched(h, n, Aarray, lda, ipvt, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) integer, device :: info(*) integer :: batchCount
2.8.5.42. cublasZgetriBatched
ZGETRI computes the inverse of a matrix using the LU factorization computed by ZGETRF. This method inverts U and then computes inv(A) by solving the system inv(A)*L = inv(U) for inv(A). Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasZgetriBatched(h, n, Aarray, lda, ipvt, Carray, ldc, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: Aarray(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: Carray(*) integer :: ldc integer, device :: info(*) integer :: batchCount
2.8.5.43. cublasZgetrsBatched
ZGETRS solves a system of linear equations A * X = B, A**T * X = B, or A**H * X = B with a general N-by-N matrix A using the LU factorization computed by ZGETRF. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasZgetrsBatched(h, trans, n, nrhs, A, lda, ipvt, B, ldb, info, batchCount) type(cublasHandle) :: h integer :: trans integer :: n, nrhs type(c_devptr), device :: A(*) integer :: lda integer, device :: ipvt(*) type(c_devptr), device :: B(*) integer :: ldb integer, device :: info(*) integer :: batchCount
2.8.5.44. cublasZtrsmBatched
ZTRSM solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T or op( A ) = A**H. The matrix X is overwritten on B. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasZtrsmBatched(h, side, uplo, trans, diag, m, n, alpha, A, lda, B, ldb, batchCount) type(cublasHandle) :: h integer :: side integer :: uplo integer :: trans integer :: diag integer :: m, n complex(8) :: alpha type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: B(*) integer :: ldb integer :: batchCount
2.8.5.45. cublasZmatinvBatched
cublasZmatinvBatched is a short cut of cublasZgetrfBatched plus cublasZgetriBatched. However it only works if n is less than 32. If not, the user has to go through cublasZgetrfBatched and cublasZgetriBatched. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasZmatinvBatched(h, n, A, lda, Ainv, lda_inv, info, batchCount) type(cublasHandle) :: h integer :: n type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: Ainv(*) integer :: lda_inv integer, device :: info(*) integer :: batchCount
2.8.5.46. cublasZgeqrfBatched
ZGEQRF computes a QR factorization of a complex M-by-N matrix A: A = Q * R. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasZgeqrfBatched(h, m, n, A, lda, Tau, info, batchCount) type(cublasHandle) :: h integer :: m, n type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: Tau(*) integer, device :: info(*) integer :: batchCount
2.8.5.47. cublasZgelsBatched
ZGELS solves overdetermined or underdetermined complex linear systems involving an M-by-N matrix A, or its conjugate-transpose, using a QR or LQ factorization of A. It is assumed that A has full rank. The following options are provided: 1. If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A*X ||. 2. If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B. 3. If TRANS = 'C' and m >= n: find the minimum norm solution of an undetermined system A**H * X = B. 4. If TRANS = 'C' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem minimize || B - A**H * X ||. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function cublasZgelsBatched(h, trans, m, n, nrhs, A, lda, C, ldc, info, dinfo, batchCount) type(cublasHandle) :: h integer :: trans integer :: m, n, nrhs type(c_devptr), device :: A(*) integer :: lda type(c_devptr), device :: C(*) integer :: ldc integer, device :: info(*) integer, device :: dinfo(*) integer :: batchCount
3. FFT Runtime Library APIs
This section describes the Fortran interfaces to the cuFFT library. The FFT functions are only accessible from host code. All of the runtime API routines are integer functions that return an error code; they return a value of CUFFT_SUCCESS if the call was successful, or another cuFFT status return value if there was an error.
Unless a specific kind is provided in the following interfaces, the plain integer type implies integer(4) and the plain real type implies real(4).
3.1. CUFFT Definitions and Helper Functions
This section contains definitions and data types used in the cuFFT library and interfaces to the cuFFT helper functions.
The cuFFT module contains the following constants and enumerations:
integer, parameter :: CUFFT_FORWARD = -1 integer, parameter :: CUFFT_INVERSE = 1
! CUFFT Status enum, bind(C) enumerator :: CUFFT_SUCCESS = 0 enumerator :: CUFFT_INVALID_PLAN = 1 enumerator :: CUFFT_ALLOC_FAILED = 2 enumerator :: CUFFT_INVALID_TYPE = 3 enumerator :: CUFFT_INVALID_VALUE = 4 enumerator :: CUFFT_INTERNAL_ERROR = 5 enumerator :: CUFFT_EXEC_FAILED = 6 enumerator :: CUFFT_SETUP_FAILED = 7 enumerator :: CUFFT_INVALID_SIZE = 8 enumerator :: CUFFT_UNALIGNED_DATA = 9 end enum
! CUFFT Transform Types enum, bind(C) enumerator :: CUFFT_R2C = z'2a' ! Real to Complex (interleaved) enumerator :: CUFFT_C2R = z'2c' ! Complex (interleaved) to Real enumerator :: CUFFT_C2C = z'29' ! Complex to Complex, interleaved enumerator :: CUFFT_D2Z = z'6a' ! Double to Double-Complex enumerator :: CUFFT_Z2D = z'6c' ! Double-Complex to Double enumerator :: CUFFT_Z2Z = z'69' ! Double-Complex to Double-Complex end enum
! CUFFT Data Layouts enum, bind(C) enumerator :: CUFFT_COMPATIBILITY_NATIVE = 0 enumerator :: CUFFT_COMPATIBILITY_FFTW_PADDING = 1 enumerator :: CUFFT_COMPATIBILITY_FFTW_ASYMMETRIC = 2 enumerator :: CUFFT_COMPATIBILITY_FFTW_ALL = 3 end enum
integer, parameter :: CUFFT_COMPATIBILITY_DEFAULT = CUFFT_COMPATIBILITY_FFTW_PADDING
3.1.1. cufftSetCompatibilityMode
This function configures the layout of cuFFT output in FFTW-compatible modes.
integer(4) function cufftSetCompatibilityMode( plan, mode ) integer :: plan integer :: mode
3.1.2. cufftSetStream
This function sets the stream to be used by the cuFFT library to execute its routines.
integer(4) function cufftSetStream(plan, stream) integer :: plan integer(kind=cuda_stream_kind()) :: stream
3.1.3. cufftGetVersion
This function returns the version number of cuFFT.
integer(4) function cufftGetVersion( version ) integer :: version
3.1.4. cufftSetAutoAllocation
This function indicates that the caller intends to allocate and manage work areas for plans that have been generated. cuFFT default behavior is to allocate the work area at plan generation time. If cufftSetAutoAllocation() has been called with autoAllocate set to 0 prior to one of the cufftMakePlan*() calls, cuFFT does not allocate the work area. This is the preferred sequence for callers wishing to manage work area allocation.
integer(4) function cufftSetAutoAllocation(plan, autoAllocate) integer(4) :: plan, autoallocate
3.1.5. cufftSetWorkArea
This function overrides the work area pointer associated with a plan. If the work area was auto-allocated, cuFFT frees the auto-allocated space. The cufftExecute*() calls assume that the work area pointer is valid and that it points to a contiguous region in device memory that does not overlap with any other work area. If this is not the case, results are indeterminate.
integer(4) function cufftSetWorkArea(plan, workArea) integer(4) :: plan integer, device :: workArea(*)
3.1.6. cufftDestroy
This function frees all GPU resources associated with a cuFFT plan and destroys the internal plan data structure.
integer(4) function cufftDestroy( plan ) integer :: plan
3.2. CUFFT Plans and Estimated Size Functions
This section contains functions from the cuFFT library used to create plans and estimate work buffer size.
3.2.1. cufftPlan1d
This function creates a 1D FFT plan configuration for a specified signal size and data type. Nx is the size of the transform; batch is the number of transforms of size nx.
integer(4) function cufftPlan1d(plan, nx, ffttype, batch) integer :: plan integer :: nx integer :: ffttype integer :: batch
3.2.2. cufftPlan2d
This function creates a 2D FFT plan configuration according to a specified signal size and data type. Nx is the size of the of the 1st dimension in the transform; ny is the size of the 2nd dimension.
integer(4) function cufftPlan2d( plan, nx, ny, ffttype ) integer :: plan integer :: nx, ny integer :: ffttype
3.2.3. cufftPlan3d
This function creates a 3D FFT plan configuration according to a specified signal size and data type. Nx is the size of the of the 1st dimension in the transform; ny is the size of the 2nd dimension; nz is the size of the 3rd dimension.
integer(4) function cufftPlan3d( plan, nx, ny, nz, ffttype ) integer :: plan integer :: nx, ny, nz integer :: ffttype
3.2.4. cufftPlanMany
This function creates an FFT plan configuration of dimension rank, with sizes specified in the array n. Batch is the number of transforms to configure. This function supports more complicated input and output data layouts using the arguments inembed, istride, idist, onembed, ostride, and odist. In the C function, if inembed and onembed are set to NULL, all other stride information is ignored. Fortran programmers can pass NULL when using the PGI cufft module by setting an F90 pointer to null(), either through direct assignment, using c_f_pointer() with c_null_ptr as the first argument, or the nullify statement, then passing the nullified F90 pointer as the actual argument for the inembed and onembed dummies.
integer(4) function cufftPlanMany(plan, rank, n, inembed, istride, idist, onembed, ostride, odist, ffttype, batch ) integer :: plan integer :: rank integer :: n integer :: inembed, onembed integer :: istride, idist, ostride, odist integer :: ffttype, batch
3.2.5. cufftCreate
This function creates an opaque handle for further cuFFT calls and allocates some small data structures on the host. In C, the handle type is currently typedef'ed to an int, so in Fortran we use an integer*4 to hold the plan.
integer(4) function cufftCreate(plan) integer(4) :: plan
3.2.6. cufftMakePlan1d
Following a call to cufftCreate(), this function creates a 1D FFT plan configuration for a specified signal size and data type. Nx is the size of the transform; batch is the number of transforms of size nx.
integer(4) function cufftMakePlan1d(plan, nx, ffttype, batch, worksize) integer(4) :: plan integer(4) :: nx integer(4) :: ffttype integer(4) :: batch integer(kind=int_ptr_kind()) :: workSize
3.2.7. cufftMakePlan2d
Following a call to cufftCreate(), this function creates a 2D FFT plan configuration according to a specified signal size and data type. Nx is the size of the of the 1st dimension in the transform; ny is the size of the 2nd dimension.
integer(4) function cufftMakePlan2d(plan, nx, ny, ffttype, workSize) integer(4) :: plan integer(4) :: nx, ny integer(4) :: ffttype integer(kind=int_ptr_kind()) :: workSize
3.2.8. cufftMakePlan3d
Following a call to cufftCreate(), this function creates a 3D FFT plan configuration according to a specified signal size and data type. Nx is the size of the of the 1st dimension in the transform; ny is the size of the 2nd dimension; nz is the size of the 3rd dimension.
integer(4) function cufftMakePlan3d(plan, nx, ny, nz, ffttype, workSize) integer(4) :: plan integer(4) :: nx, ny, nz integer(4) :: ffttype integer(kind=int_ptr_kind()) :: workSize
3.2.9. cufftMakePlanMany
Following a call to cufftCreate(), this function creates an FFT plan configuration of dimension rank, with sizes specified in the array n. Batch is the number of transforms to configure. This function supports more complicated input and output data layouts using the arguments inembed, istride, idist, onembed, ostride, and odist. In the C function, if inembed and onembed are set to NULL, all other stride information is ignored. Fortran programmers can pass NULL when using the PGI cufft module by setting an F90 pointer to null(), either through direct assignment, using c_f_pointer() with c_null_ptr as the first argument, or the nullify statement, then passing the nullified F90 pointer as the actual argument for the inembed and onembed dummies.
integer(4) function cufftMakePlanMany(plan, rank, n, inembed, istride, idist, onembed, ostride, odist, ffttype, batch, workSize) integer(4) :: plan integer(4) :: rank integer :: n integer :: inembed, onembed integer(4) :: istride, idist, ostride, odist integer(4) :: ffttype, batch integer(kind=int_ptr_kind()) :: workSize
3.2.10. cufftEstimate1d
This function returns an estimate for the size of the work area required, in bytes, given the specified size and data type, and assuming default plan settings.
integer(4) function cufftEstimate1d(nx, ffttype, batch, workSize) integer(4) :: nx integer(4) :: ffttype integer(4) :: batch integer(kind=int_ptr_kind()) :: workSize
3.2.11. cufftEstimate2d
This function returns an estimate for the size of the work area required, in bytes, given the specified size and data type, and assuming default plan settings.
integer(4) function cufftEstimate2d(nx, ny, ffttype, workSize) integer(4) :: nx, ny integer(4) :: ffttype integer(kind=int_ptr_kind()) :: workSize
3.2.12. cufftEstimate3d
This function returns an estimate for the size of the work area required, in bytes, given the specified size and data type, and assuming default plan settings.
integer(4) function cufftEstimate3d(nx, ny, nz, ffttype, workSize) integer(4) :: nx, ny, nz integer(4) :: ffttype integer(kind=int_ptr_kind()) :: workSize
3.2.13. cufftEstimateMany
This function returns an estimate for the size of the work area required, in bytes, given the specified size and data type, and assuming default plan settings.
integer(4) function cufftEstimateMany(rank, n, inembed, istride, idist, onembed, ostride, odist, ffttype, batch, workSize) integer(4) :: rank, istride, idist, ostride, odist integer :: n, inembed, onembed integer(4) :: ffttype integer(4) :: batch integer(kind=int_ptr_kind()) :: workSize
3.2.14. cufftGetSize1d
This function gives a more accurate estimate than cufftEstimate1d() of the size of the work area required, in bytes, given the specified plan parameters and taking into account any plan settings which may have been made.
integer(4) function cufftGetSize1d(plan, nx, ffttype, batch, workSize) integer(4) :: plan, nx, ffttype, batch integer(kind=int_ptr_kind()) :: workSize
3.2.15. cufftGetSize2d
This function gives a more accurate estimate than cufftEstimate2d() of the size of the work area required, in bytes, given the specified plan parameters and taking into account any plan settings which may have been made.
integer(4) function cufftGetSize2d(plan, nx, ny, ffttype, workSize) integer(4) :: plan, nx, ny, ffttype integer(kind=int_ptr_kind()) :: workSize
3.2.16. cufftGetSize3d
This function gives a more accurate estimate than cufftEstimate3d() of the size of the work area required, in bytes, given the specified plan parameters and taking into account any plan settings which may have been made.
integer(4) function cufftGetSize3d(plan, nx, ny, nz, ffttype, workSize) integer(4) :: plan, nx, ny, nz, ffttype integer(kind=int_ptr_kind()) :: workSize
3.2.17. cufftGetSizeMany
This function gives a more accurate estimate than cufftEstimateMany() of the size of the work area required, in bytes, given the specified plan parameters and taking into account any plan settings which may have been made.
integer(4) function cufftGetSizeMany(plan, rank, n, inembed, istride, idist, onembed, ostride, odist, ffttype, batch, workSize) integer(4) :: plan, rank, istride, idist, ostride, odist integer :: n, inembed, onembed integer(4) :: ffttype integer(4) :: batch integer(kind=int_ptr_kind()) :: workSize
3.2.18. cufftGetSize
Once plan generation has been done, either with the original API or the extensible API, this call returns the actual size of the work area required, in bytes, to support the plan. Callers who choose to manage work area allocation within their application must use this call after plan generation, and after any cufftSet*() calls subsequent to plan generation, if those calls might alter the required work space size.
integer(4) function cufftGetSize(plan, workSize) integer(4) :: plan integer(kind=int_ptr_kind()) :: workSize
3.3. CUFFT Execution Functions
This section contains definitions and data types used in the cuFFT library and interfaces to the cuFFT helper functions.
3.3.1. cufftExecC2C
This function executes a single precision complex-to-complex transform plan in the transform direction as specified by the direction parameter. If idata and odata are the same, this function does an in-place transform.
integer(4) function cufftExecC2C( plan, idata, odata, direction ) integer :: plan complex(4), device, dimension(*) :: idata, odata integer :: direction
3.3.2. cufftExecR2C
This function executes a single precision real-to-complex, implicity forward, cuFFT transform plan. If idata and odata are the same, this function does an in-place transform, but note there are data layout differences between in-place and out-of-place transforms for real-to- complex FFTs in cuFFT.
integer(4) function cufftExecR2C( plan, idata, odata ) integer :: plan real(4), device, dimension(*) :: idata complex(4), device, dimension(*) :: odata
3.3.3. cufftExecC2R
This function executes a single precision complex-to-real, implicity inverse, cuFFT transform plan. If idata and odata are the same, this function does an in-place transform.
integer(4) function cufftExecC2R( plan, idata, odata ) integer :: plan complex(4), device, dimension(*) :: idata real(4), device, dimension(*) :: odata
3.3.4. cufftExecZ2Z
This function executes a double precision complex-to-complex transform plan in the transform direction as specified by the direction parameter. If idata and odata are the same, this function does an in-place transform.
integer(4) function cufftExecZ2Z( plan, idata, odata, direction ) integer :: plan complex(8), device, dimension(*) :: idata, odata integer :: direction
3.3.5. cufftExecD2Z
This function executes a double precision real-to-complex, implicity forward, cuFFT transform plan. If idata and odata are the same, this function does an in-place transform, but note there are data layout differences between in-place and out-of-place transforms for real-to- complex FFTs in cuFFT.
integer(4) function cufftExecD2Z( plan, idata, odata ) integer :: plan real(8), device, dimension(*) :: idata complex(8), device, dimension(*) :: odata
3.3.6. cufftExecZ2D
This function executes a double precision complex-to-real, implicity inverse, cuFFT transform plan. If idata and odata are the same, this function does an in-place transform.
integer(4) function cufftExecZ2D( plan, idata, odata ) integer :: plan complex(8), device, dimension(*) :: idata real(8), device, dimension(*) :: odata
4. Random Number Runtime Library APIs
This section describes the Fortran interfaces to the CUDA cuRAND library. The cuRAND functionality is accessible from both host and device code. In the host library, all of the runtime API routines are integer functions that return an error code; they return a value of CURAND_STATUS_SUCCESS if the call was successful, or other cuRAND return status value if there was an error. The host library routines are meant to produce a series or array of random numbers. In the device library, the init routines are subroutines and the generator functions return the type of the value being generated. The device library routines are meant for producing a single value per thread per call.
Unless a specific kind is provided, the plain integer type implies integer(4) and the plain real type implies real(4).
4.1. CURAND Definitions and Helper Functions
This section contains definitions and data types used in the cuRAND library and interfaces to the cuRAND helper functions.
The curand module contains the following derived type definitions:
TYPE curandGenerator TYPE(C_PTR) :: handle END TYPE
The curand module contains the following enumerations:
! CURAND Status enum, bind(c) enumerator :: CURAND_STATUS_SUCCESS = 0 enumerator :: CURAND_STATUS_VERSION_MISMATCH = 100 enumerator :: CURAND_STATUS_NOT_INITIALIZED = 101 enumerator :: CURAND_STATUS_ALLOCATION_FAILED = 102 enumerator :: CURAND_STATUS_TYPE_ERROR = 103 enumerator :: CURAND_STATUS_OUT_OF_RANGE = 104 enumerator :: CURAND_STATUS_LENGTH_NOT_MULTIPLE = 105 enumerator :: CURAND_STATUS_DOUBLE_PRECISION_REQUIRED = 106 enumerator :: CURAND_STATUS_LAUNCH_FAILURE = 201 enumerator :: CURAND_STATUS_PREEXISTING_FAILURE = 202 enumerator :: CURAND_STATUS_INITIALIZATION_FAILED = 203 enumerator :: CURAND_STATUS_ARCH_MISMATCH = 204 enumerator :: CURAND_STATUS_INTERNAL_ERROR = 999 end enum
! CURAND Generator Types enum, bind(c) enumerator :: CURAND_RNG_TEST = 0 enumerator :: CURAND_RNG_PSEUDO_DEFAULT = 100 enumerator :: CURAND_RNG_PSEUDO_XORWOW = 101 enumerator :: CURAND_RNG_PSEUDO_MRG32K3A = 121 enumerator :: CURAND_RNG_PSEUDO_MTGP32 = 141 enumerator :: CURAND_RNG_PSEUDO_MT19937 = 142 enumerator :: CURAND_RNG_PSEUDO_PHILOX4_32_10 = 161 enumerator :: CURAND_RNG_QUASI_DEFAULT = 200 enumerator :: CURAND_RNG_QUASI_SOBOL32 = 201 enumerator :: CURAND_RNG_QUASI_SCRAMBLED_SOBOL32 = 202 enumerator :: CURAND_RNG_QUASI_SOBOL64 = 203 enumerator :: CURAND_RNG_QUASI_SCRAMBLED_SOBOL64 = 204 end enum
! CURAND Memory Ordering enum, bind(c) enumerator :: CURAND_ORDERING_PSEUDO_BEST = 100 enumerator :: CURAND_ORDERING_PSEUDO_DEFAULT = 101 enumerator :: CURAND_ORDERING_PSEUDO_SEEDED = 102 enumerator :: CURAND_ORDERING_QUASI_DEFAULT = 201 end enum
! CURAND Direction Vectors enum, bind(c) enumerator :: CURAND_DIRECTION_VECTORS_32_JOEKUO6 = 101 enumerator :: CURAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6 = 102 enumerator :: CURAND_DIRECTION_VECTORS_64_JOEKUO6 = 103 enumerator :: CURAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6 = 104 end enum
! CURAND Methods enum, bind(c) enumerator :: CURAND_CHOOSE_BEST = 0 enumerator :: CURAND_ITR = 1 enumerator :: CURAND_KNUTH = 2 enumerator :: CURAND_HITR = 3 enumerator :: CURAND_M1 = 4 enumerator :: CURAND_M2 = 5 enumerator :: CURAND_BINARY_SEARCH = 6 enumerator :: CURAND_DISCRETE_GAUSS = 7 enumerator :: CURAND_REJECTION = 8 enumerator :: CURAND_DEVICE_API = 9 enumerator :: CURAND_FAST_REJECTION = 10 enumerator :: CURAND_3RD = 11 enumerator :: CURAND_DEFINITION = 12 enumerator :: CURAND_POISSON = 13 end enum
4.1.1. curandCreateGenerator
This function creates a new random number generator of type rng. See the beginning of this section for valid values of rng.
integer(4) function curandCreateGenerator(generator, rng) type(curandGenerator) :: generator integer :: rng
4.1.2. curandCreateGeneratorHost
This function creates a new host CPU random number generator of type rng. See the beginning of this section for valid values of rng.
integer(4) function curandCreateGeneratorHost(generator, rng) type(curandGenerator) :: generator integer :: rng
4.1.3. curandDestroyGenerator
This function destroys an existing random number generator.
integer(4) function curandDestroyGenerator(generator) type(curandGenerator) :: generator
4.1.4. curandGetVersion
This function returns the version number of the cuRAND library.
integer(4) function curandGetVersion(version) integer(4) :: version
4.1.5. curandSetStream
This function sets the current stream for the cuRAND kernel launches.
integer(4) function curandSetStream(generator, stream) type(curandGenerator) :: generator integer(kind=c_intptr_t) :: stream
4.1.6. curandSetPseudoRandomGeneratorSeed
This function sets the seed value of the pseudo-random number generator.
integer(4) function curandSetPseudoRandomGeneratorSeed(generator, seed) type(curandGenerator) :: generator integer(8) :: seed
4.1.7. curandSetGeneratorOffset
This function sets the absolute offset of the pseudo or quasirandom number generator.
integer(4) function curandSetGeneratorOffset(generator, offset) type(curandGenerator) :: generator integer(8) :: offset
4.1.8. curandSetGeneratorOrdering
This function sets the ordering of results of the pseudo or quasirandom number generator.
integer(4) function curandSetGeneratorOrdering(generator, order) type(curandGenerator) :: generator integer(4) :: order
4.1.9. curandSetQuasiRandomGeneratorDimensions
This function sets number of dimensions of the quasirandom number generator.
integer(4) function curandSetQuasiRandomGeneratorDimensions(generator, num) type(curandGenerator) :: generator integer(4) :: num
4.2. CURAND Generator Functions
This section contains interfaces for the cuRAND generator functions.
4.2.1. curandGenerate
This function generates 32-bit pseudo or quasirandom numbers.
integer(4) function curandGenerate(generator, array, num ) type(curandGenerator) :: generator integer(4), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num
4.2.2. curandGenerateLongLong
This function generates 64-bit integer quasirandom numbers. The function curandGenerate() has also been overloaded to accept these function arguments.
integer(4) function curandGenerateLongLong(generator, array, num ) type(curandGenerator) :: generator integer(8), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num
4.2.3. curandGenerateUniform
This function generates 32-bit floating point uniformly distributed random numbers. The function curandGenerate() has also been overloaded to accept these function arguments.
integer(4) function curandGenerateUniform(generator, array, num ) type(curandGenerator) :: generator real(4), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num
4.2.4. curandGenerateUniformDouble
This function generates 64-bit floating point uniformly distributed random numbers. The function curandGenerate() has also been overloaded to accept these function arguments.
integer(4) function curandGenerateUniformDouble(generator, array, num ) type(curandGenerator) :: generator real(8), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num
4.2.5. curandGenerateNormal
This function generates 32-bit floating point normally distributed random numbers. The function curandGenerate() has also been overloaded to accept these function arguments.
integer(4) function curandGenerateNormal(generator, array, num, mean, stddev ) type(curandGenerator) :: generator real(4), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num real(4) :: mean, stddev
4.2.6. curandGenerateNormalDouble
This function generates 64-bit floating point normally distributed random numbers. The function curandGenerate() has also been overloaded to accept these function arguments.
integer(4) function curandGenerateNormalDouble(generator, array, num, mean, stddev ) type(curandGenerator) :: generator real(8), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num real(8) :: mean, stddev
4.2.7. curandGeneratePoisson
This function generates Poisson-distributed random numbers. The function curandGenerate() has also been overloaded to accept these function arguments.
integer(4) function curandGeneratePoisson(generator, array, num, lambda ) type(curandGenerator) :: generator real(8), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num real(8) :: lambda
4.2.8. curandGenerateSeeds
This function sets the starting state of the generator.
integer(4) function curandGenerateSeeds(generator) type(curandGenerator) :: generator
4.2.9. curandGenerateLogNormal
This function generates 32-bit floating point log-normally distributed random numbers.
integer(4) function curandGenerateLogNormal(generator, array, num, mean, stddev ) type(curandGenerator) :: generator real(4), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num real(4) :: mean, stddev
4.2.10. curandGenerateLogNormalDouble
This function generates 64-bit floating point log-normally distributed random numbers.
integer(4) function curandGenerateLogNormalDouble(generator, array, num, mean, stddev ) type(curandGenerator) :: generator real(8), device :: array(*) ! Host or device depending on the generator integer(kind=c_intptr_t) :: num real(8) :: mean, stddev
4.3. CURAND Device Definitions and Functions
This section contains definitions and data types used in the cuRAND device library and interfaces to the cuRAND functions.
The curand device module contains the following derived type definitions:
TYPE curandStateXORWOW integer(4) :: d integer(4) :: v(5) integer(4) :: boxmuller_flag integer(4) :: boxmuller_flag_double real(4) :: boxmuller_extra real(8) :: boxmuller_extra_double END TYPE curandStateXORWOW
TYPE curandStateMRG32k3a real(8) :: s1(3) real(8) :: s2(3) integer(4) :: boxmuller_flag integer(4) :: boxmuller_flag_double real(4) :: boxmuller_extra real(8) :: boxmuller_extra_double END TYPE curandStateMRG32k3a
TYPE curandStateSobol32 integer(4) :: d integer(4) :: x integer(4) :: c integer(4) :: direction_vectors(32) END TYPE curandStateSobol32
TYPE curandStateScrambledSobol32 integer(4) :: d integer(4) :: x integer(4) :: c integer(4) :: direction_vectors(32) END TYPE curandStateScrambledSobol32
TYPE curandStateSobol64 integer(8) :: d integer(8) :: x integer(8) :: c integer(8) :: direction_vectors(32) END TYPE curandStateSobol64
TYPE curandStateScrambledSobol64 integer(8) :: d integer(8) :: x integer(8) :: c integer(8) :: direction_vectors(32) END TYPE curandStateScrambledSobol64
TYPE curandStateMtgp32 integer(4) :: s(MTGP32_STATE_SIZE) integer(4) :: offset integer(4) :: pIdx integer(kind=int_ptr_kind()) :: k integer(4) :: precise_double_flag END TYPE curandStateMtgp32
TYPE curandStatePhilox4_32_10 integer(4) :: ctr integer(4) :: output integer(2) :: key integer(4) :: state integer(4) :: boxmuller_flag integer(4) :: boxmuller_flag_double real(4) :: boxmuller_extra real(8) :: boxmuller_extra_double END TYPE curandStatePhilox4_32_10
4.3.1. curand_Init
This overloaded device subroutine initializes the state for the random number generator. These device subroutines are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
4.3.1.1. curandInitXORWOW
This function initializes the state for the XORWOW random number generator. The function curand_init() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
subroutine curandInitXORWOW(seed, sequence, offset, state) integer(8) :: seed integer(8) :: sequence integer(8) :: offset TYPE(curandStateXORWOW) :: state
4.3.1.2. curandInitMRG32k3a
This function initializes the state for the MRG32k3a random number generator. The function curand_init() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
subroutine curandInitMRG32k3a(seed, sequence, offset, state) integer(8) :: seed integer(8) :: sequence integer(8) :: offset TYPE(curandStateMRG32k3a) :: state
4.3.1.3. curandInitPhilox4_32_10
This function initializes the state for the Philox4_32_10 random number generator. The function curand_init() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
subroutine curandInitPhilox4_32_10(seed, sequence, offset, state) integer(8) :: seed integer(8) :: sequence integer(8) :: offset TYPE(curandStatePhilox4_32_10) :: state
4.3.1.4. curandInitSobol32
This function initializes the state for the Sobol32 random number generator. The function curand_init() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
subroutine curandInitSobol32(direction_vectors, offset, state) integer :: direction_vectors(*) integer(4) :: offset TYPE(curandStateSobol32) :: state
4.3.1.5. curandInitScrambledSobol32
This function initializes the state for the scrambled Sobol32 random number generator. The function curand_init() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
subroutine curandInitScrambledSobol32(direction_vectors, scramble, offset, state) integer :: direction_vectors(*) integer(4) :: scramble integer(4) :: offset TYPE(curandStateScrambledSobol32) :: state
4.3.1.6. curandInitSobol64
This function initializes the state for the Sobol64 random number generator. The function curand_init() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
subroutine curandInitSobol64(direction_vectors, offset, state) integer :: direction_vectors(*) integer(8) :: offset TYPE(curandStateSobol64) :: state
4.3.1.7. curandInitScrambledSobol64
This function initializes the state for the scrambled Sobol64 random number generator. The function curand_init() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
subroutine curandInitScrambledSobol64(direction_vectors, scramble, offset, state) integer :: direction_vectors(*) integer(8) :: scramble integer(8) :: offset TYPE(curandStateScrambledSobol64) :: state
4.3.2. curand
This overloaded device function returns 32 or 64 bits or random data based on the state argument. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
4.3.2.1. curandGetXORWOW
This function returns 32 bits of pseudorandomness from the XORWOW random number generator. The function curand() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function curandGetXORWOW(state) TYPE(curandStateXORWOW) :: state
4.3.2.2. curandGetMRG32k3a
This function returns 32 bits of pseudorandomness from the MRG32k3a random number generator. The function curand() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function curandGetMRG32k3a(state) TYPE(curandStateMRG32k3a) :: state
4.3.2.3. curandGetPhilox4_32_10
This function returns 32 bits of pseudorandomness from the Philox4_32_10 random number generator. The function curand() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function curandGetPhilox4_32_10(state) TYPE(curandStatePhilox4_32_10) :: state
4.3.2.4. curandGetSobol32
This function returns 32 bits of quasirandomness from the Sobol32 random number generator. The function curand() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function curandGetSobol32(state) TYPE(curandStateSobol32) :: state
4.3.2.5. curandGetScrambledSobol32
This function returns 32 bits of quasirandomness from the scrambled Sobol32 random number generator. The function curand() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function curandGetScrambledSobol32(state) TYPE(curandStateScrambledSobol32) :: state
4.3.2.6. curandGetSobol64
This function returns 64 bits of quasirandomness from the Sobol64 random number generator. The function curand() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function curandGetSobol64(state) TYPE(curandStateSobol64) :: state
4.3.2.7. curandGetScrambledSobol64
This function returns 64 bits of quasirandomness from the scrambled Sobol64 random number generator. The function curand() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
integer(4) function curandGetScrambledSobol64(state) TYPE(curandStateScrambledSobol64) :: state
4.3.3. Curand_Normal
This overloaded device function returns a 32-bit floating point normally distributed random number. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
4.3.3.1. curandNormalXORWOW
This function returns a 32-bit floating point normally distributed random number from an XORWOW generator. The function curand_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandNormalXORWOW(state) TYPE(curandStateXORWOW) :: state
4.3.3.2. curandNormalMRG32k3a
This function returns a 32-bit floating point normally distributed random number from an MRG32k3a generator. The function curand_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandNormalMRG32k3a(state) TYPE(curandStateMRG32k3a) :: state
4.3.3.3. curandNormalPhilox4_32_10
This function returns a 32-bit floating point normally distributed random number from a Philox4_32_10 generator. The function curand_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandNormalPhilox4_32_10(state) TYPE(curandStatePhilox4_32_10) :: state
4.3.3.4. curandNormalSobol32
This function returns a 32-bit floating point normally distributed random number from an Sobol32 generator. The function curand_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandNormalSobol32(state) TYPE(curandStateSobol32) :: state
4.3.3.5. curandNormalScrambledSobol32
This function returns a 32-bit floating point normally distributed random number from an scrambled Sobol32 generator. The function curand_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandNormalScrambledSobol32(state) TYPE(curandStateScrambledSobol32) :: state
4.3.3.6. curandNormalSobol64
This function returns a 32-bit floating point normally distributed random number from an Sobol64 generator. The function curand_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandNormalSobol64(state) TYPE(curandStateSobol64) :: state
4.3.3.7. curandNormalScrambledSobol64
This function returns a 32-bit floating point normally distributed random number from an scrambled Sobol64 generator. The function curand_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandNormalScrambledSobol64(state) TYPE(curandStateScrambledSobol64) :: state
4.3.4. Curand_Normal_Double
This overloaded device function returns a 64-bit floating point normally distributed random number. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
4.3.4.1. curandNormalDoubleXORWOW
This function returns a 64-bit floating point normally distributed random number from an XORWOW generator. The function curand_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandNormalDoubleXORWOW(state) TYPE(curandStateXORWOW) :: state
4.3.4.2. curandNormalDoubleMRG32k3a
This function returns a 64-bit floating point normally distributed random number from an MRG32k3a generator. The function curand_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandNormalDoubleMRG32k3a(state) TYPE(curandStateMRG32k3a) :: state
4.3.4.3. curandNormalDoublePhilox4_32_10
This function returns a 64-bit floating point normally distributed random number from a Philox4_32_10 generator. The function curand_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandNormalDoublePhilox4_32_10(state) TYPE(curandStatePhilox4_32_10) :: state
4.3.4.4. curandNormalDoubleSobol32
This function returns a 64-bit floating point normally distributed random number from an Sobol32 generator. The function curand_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandNormalDoubleSobol32(state) TYPE(curandStateSobol32) :: state
4.3.4.5. curandNormalDoubleScrambledSobol32
This function returns a 64-bit floating point normally distributed random number from an scrambled Sobol32 generator. The function curand_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandNormalDoubleScrambledSobol32(state) TYPE(curandStateScrambledSobol32) :: state
4.3.4.6. curandNormalDoubleSobol64
This function returns a 64-bit floating point normally distributed random number from an Sobol64 generator. The function curand_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandNormalDoubleSobol64(state) TYPE(curandStateSobol64) :: state
4.3.4.7. curandNormalDoubleScrambledSobol64
This function returns a 64-bit floating point normally distributed random number from an scrambled Sobol64 generator. The function curand_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandNormalDoubleScrambledSobol64(state) TYPE(curandStateScrambledSobol64) :: state
4.3.5. Curand_Log_Normal
This overloaded device function returns a 32-bit floating point log-normally distributed random number. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
4.3.5.1. curandLogNormalXORWOW
This function returns a 32-bit floating point log-normally distributed random number from an XORWOW generator. The function curand_log_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandLogNormalXORWOW(state) TYPE(curandStateXORWOW) :: state
4.3.5.2. curandLogNormalMRG32k3a
This function returns a 32-bit floating point log-normally distributed random number from an MRG32k3a generator. The function curand_log_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandLogNormalMRG32k3a(state) TYPE(curandStateMRG32k3a) :: state
4.3.5.3. curandLogNormalPhilox4_32_10
This function returns a 32-bit floating point log-normally distributed random number from a Philox4_32_10 generator. The function curand_log_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandLogNormalPhilox4_32_10(state) TYPE(curandStatePhilox4_32_10) :: state
4.3.5.4. curandLogNormalSobol32
This function returns a 32-bit floating point log-normally distributed random number from an Sobol32 generator. The function curand_log_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandLogNormalSobol32(state) TYPE(curandStateSobol32) :: state
4.3.5.5. curandLogNormalScrambledSobol32
This function returns a 32-bit floating point log-normally distributed random number from an scrambled Sobol32 generator. The function curand_log_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandLogNormalScrambledSobol32(state) TYPE(curandStateScrambledSobol32) :: state
4.3.5.6. curandLogNormalSobol64
This function returns a 32-bit floating point log-normally distributed random number from an Sobol64 generator. The function curand_log_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandLogNormalSobol64(state) TYPE(curandStateSobol64) :: state
4.3.5.7. curandLogNormalScrambledSobol64
This function returns a 32-bit floating point log-normally distributed random number from an scrambled Sobol64 generator. The function curand_log_normal() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandLogNormalScrambledSobol64(state) TYPE(curandStateScrambledSobol64) :: state
4.3.6. Curand_Log_Normal_Double
This overloaded device function returns a 64-bit floating point log-normally distributed random number. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
4.3.6.1. curandLogNormalDoubleXORWOW
This function returns a 64-bit floating point log-normally distributed random number from an XORWOW generator. The function curand_log_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandLogNormalDoubleXORWOW(state) TYPE(curandStateXORWOW) :: state
4.3.6.2. curandLogNormalDoubleMRG32k3a
This function returns a 64-bit floating point log-normally distributed random number from an MRG32k3a generator. The function curand_log_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandLogNormalDoubleMRG32k3a(state) TYPE(curandStateMRG32k3a) :: state
4.3.6.3. curandLogNormalDoublePhilox4_32_10
This function returns a 64-bit floating point log-normally distributed random number from a Philox4_32_10 generator. The function curand_log_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandLogNormalDoublePhilox4_32_10(state) TYPE(curandStatePhilox4_32_10) :: state
4.3.6.4. curandLogNormalDoubleSobol32
This function returns a 64-bit floating point log-normally distributed random number from an Sobol32 generator. The function curand_log_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandLogNormalDoubleSobol32(state) TYPE(curandStateSobol32) :: state
4.3.6.5. curandLogNormalDoubleScrambledSobol32
This function returns a 64-bit floating point log-normally distributed random number from an scrambled Sobol32 generator. The function curand_log_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandLogNormalDoubleScrambledSobol32(state) TYPE(curandStateScrambledSobol32) :: state
4.3.6.6. curandLogNormalDoubleSobol64
This function returns a 64-bit floating point log-normally distributed random number from an Sobol64 generator. The function curand_log_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandLogNormalDoubleSobol64(state) TYPE(curandStateSobol64) :: state
4.3.6.7. curandLogNormalDoubleScrambledSobol64
This function returns a 64-bit floating point log-normally distributed random number from an scrambled Sobol64 generator. The function curand_log_normal_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandLogNormalDoubleScrambledSobol64(state) TYPE(curandStateScrambledSobol64) :: state
4.3.7. Curand_Uniform
This overloaded device function returns a 32-bit floating point uniformly distributed random number. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
4.3.7.1. curandUniformXORWOW
This function returns a 32-bit floating point uniformly distributed random number from an XORWOW generator. The function curand_uniform() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandUniformXORWOW(state) TYPE(curandStateXORWOW) :: state
4.3.7.2. curandUniformMRG32k3a
This function returns a 32-bit floating point uniformly distributed random number from an MRG32k3a generator. The function curand_uniform() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandUniformMRG32k3a(state) TYPE(curandStateMRG32k3a) :: state
4.3.7.3. curandUniformPhilox4_32_10
This function returns a 32-bit floating point uniformly distributed random number from a Philox4_32_10 generator. The function curand_uniform() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandUniformPhilox4_32_10(state) TYPE(curandStatePhilox4_32_10) :: state
4.3.7.4. curandUniformSobol32
This function returns a 32-bit floating point uniformly distributed random number from an Sobol32 generator. The function curand_uniform() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandUniformSobol32(state) TYPE(curandStateSobol32) :: state
4.3.7.5. curandUniformScrambledSobol32
This function returns a 32-bit floating point uniformly distributed random number from an scrambled Sobol32 generator. The function curand_uniform() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandUniformScrambledSobol32(state) TYPE(curandStateScrambledSobol32) :: state
4.3.7.6. curandUniformSobol64
This function returns a 32-bit floating point uniformly distributed random number from an Sobol64 generator. The function curand_uniform() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandUniformSobol64(state) TYPE(curandStateSobol64) :: state
4.3.7.7. curandUniformScrambledSobol64
This function returns a 32-bit floating point uniformly distributed random number from an scrambled Sobol64 generator. The function curand_uniform() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(4) function curandUniformScrambledSobol64(state) TYPE(curandStateScrambledSobol64) :: state
4.3.8. Curand_Uniform_Double
This overloaded device function returns a 64-bit floating point uniformly distributed random number. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
4.3.8.1. curandUniformDoubleXORWOW
This function returns a 64-bit floating point uniformly distributed random number from an XORWOW generator. The function curand_uniform_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandUniformDoubleXORWOW(state) TYPE(curandStateXORWOW) :: state
4.3.8.2. curandUniformDoubleMRG32k3a
This function returns a 64-bit floating point uniformly distributed random number from an MRG32k3a generator. The function curand_uniform_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandUniformDoubleMRG32k3a(state) TYPE(curandStateMRG32k3a) :: state
4.3.8.3. curandUniformDoublePhilox4_32_10
This function returns a 64-bit floating point uniformly distributed random number from a Philox4_32_10 generator. The function curand_uniform_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandUniformDoublePhilox4_32_10(state) TYPE(curandStatePhilox4_32_10) :: state
4.3.8.4. curandUniformDoubleSobol32
This function returns a 64-bit floating point uniformly distributed random number from an Sobol32 generator. The function curand_uniform_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandUniformDoubleSobol32(state) TYPE(curandStateSobol32) :: state
4.3.8.5. curandUniformDoubleScrambledSobol32
This function returns a 64-bit floating point uniformly distributed random number from an scrambled Sobol32 generator. The function curand_uniform_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandUniformDoubleScrambledSobol32(state) TYPE(curandStateScrambledSobol32) :: state
4.3.8.6. curandUniformDoubleSobol64
This function returns a 64-bit floating point uniformly distributed random number from an Sobol64 generator. The function curand_uniform_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandUniformDoubleSobol64(state) TYPE(curandStateSobol64) :: state
4.3.8.7. curandUniformDoubleScrambledSobol64
This function returns a 64-bit floating point uniformly distributed random number from an scrambled Sobol64 generator. The function curand_uniform_double() has also been overloaded to accept these function arguments, as in CUDA C++. Device Functions are declared "attributes(device)" in CUDA Fortran and "!$acc routine() seq" in OpenACC.
real(8) function curandUniformDoubleScrambledSobol64(state) TYPE(curandStateScrambledSobol64) :: state
5. SPARSE Matrix Runtime Library APIs
This section describes the Fortran interfaces to the CUDA cuSPARSE library. The cuSPARSE functions are only accessible from host code. All of the runtime API routines are integer functions that return an error code; they return a value of CUSPARSE_STATUS_SUCCESS if the call was successful, or another cuSPARSE status return value if there was an error.
use cusparseto your program unit.
A number of the function interfaces listed in this chapter can take host or device scalar arguments. Those functions have an additional v2 interface, which does not implicitly manage the pointer mode for these calls. See section 1.6 for further discussion on the handling of pointer modes.
Unless a specific kind is provided, the plain integer type used in the interfaces implies integer(4) and the plain real type implies real(4).
5.1. CUSPARSE Definitions and Helper Functions
This section contains definitions and data types used in the cuSPARSE library and interfaces to the cuSPARSE helper functions.
The cuSPARSE module contains the following derived type definitions:
type cusparseHandle type(c_ptr) :: handle end type cusparseHandle
type :: cusparseMatDescr type(c_ptr) :: descr end type cusparseMatDescr
type cusparseSolveAnalysisInfo type(c_ptr) :: info end type cusparseSolveAnalysisInfo
type cusparseHybMat type(c_ptr) :: mat end type cusparseHybMat
type cusparseCsrsv2Info type(c_ptr) :: info end type cusparseCsrsv2Info
type cusparseCsric02Info type(c_ptr) :: info end type cusparseCsric02Info
type cusparseCsrilu02Info type(c_ptr) :: info end type cusparseCsrilu02Info
type cusparseBsrsv2Info type(c_ptr) :: info end type cusparseBsrsv2Info
type cusparseBsric02Info type(c_ptr) :: info end type cusparseBsric02Info
type cusparseBsrilu02Info type(c_ptr) :: info end type cusparseBsrilu02Info
type cusparseBsrsm2Info type(c_ptr) :: info end type cusparseBsrsm2Info
type cusparseCsrgemm2Info type(c_ptr) :: info end type cusparseCsrgemm2Info
type cusparseColorInfo type(c_ptr) :: info end type cusparseColorInfo
type cusparseCsru2csrInfo type(c_ptr) :: info end type cusparseCsru2csrInfo
The cuSPARSE module contains the following enumerations:
! cuSPARSE status return values enum, bind(C) ! cusparseStatus_t enumerator :: CUSPARSE_STATUS_SUCCESS=0 enumerator :: CUSPARSE_STATUS_NOT_INITIALIZED=1 enumerator :: CUSPARSE_STATUS_ALLOC_FAILED=2 enumerator :: CUSPARSE_STATUS_INVALID_VALUE=3 enumerator :: CUSPARSE_STATUS_ARCH_MISMATCH=4 enumerator :: CUSPARSE_STATUS_MAPPING_ERROR=5 enumerator :: CUSPARSE_STATUS_EXECUTION_FAILED=6 enumerator :: CUSPARSE_STATUS_INTERNAL_ERROR=7 enumerator :: CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED=8 end enum
enum, bind(c) ! cusparsePointerMode_t enumerator :: CUSPARSE_POINTER_MODE_HOST = 0 enumerator :: CUSPARSE_POINTER_MODE_DEVICE = 1 end enum
enum, bind(c) ! cusparseAction_t enumerator :: CUSPARSE_ACTION_SYMBOLIC = 0 enumerator :: CUSPARSE_ACTION_NUMERIC = 1 end enum
enum, bind(C) ! cusparseMatrixType_t enumerator :: CUSPARSE_MATRIX_TYPE_GENERAL = 0 enumerator :: CUSPARSE_MATRIX_TYPE_SYMMETRIC = 1 enumerator :: CUSPARSE_MATRIX_TYPE_HERMITIAN = 2 enumerator :: CUSPARSE_MATRIX_TYPE_TRIANGULAR = 3 end enum
enum, bind(C) ! cusparseFillMode_t enumerator :: CUSPARSE_FILL_MODE_LOWER = 0 enumerator :: CUSPARSE_FILL_MODE_UPPER = 1 end enum
enum, bind(C) ! cusparseDiagType_t enumerator :: CUSPARSE_DIAG_TYPE_NON_UNIT = 0 enumerator :: CUSPARSE_DIAG_TYPE_UNIT = 1 end enum
enum, bind(C) ! cusparseIndexBase_t enumerator :: CUSPARSE_INDEX_BASE_ZERO = 0 enumerator :: CUSPARSE_INDEX_BASE_ONE = 1 end enum
enum, bind(C) ! cusparseOperation_t enumerator :: CUSPARSE_OPERATION_NON_TRANSPOSE = 0 enumerator :: CUSPARSE_OPERATION_TRANSPOSE = 1 enumerator :: CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE = 2 end enum
enum, bind(C) ! cusparseDirection_t enumerator :: CUSPARSE_DIRECTION_ROW = 0 enumerator :: CUSPARSE_DIRECTION_COLUMN = 1 end enum
enum, bind(C) ! cusparseHybPartition_t enumerator :: CUSPARSE_HYB_PARTITION_AUTO = 0 enumerator :: CUSPARSE_HYB_PARTITION_USER = 1 enumerator :: CUSPARSE_HYB_PARTITION_MAX = 2 end enum
enum, bind(C) ! cusparseSolvePolicy_t enumerator :: CUSPARSE_SOLVE_POLICY_NO_LEVEL = 0 enumerator :: CUSPARSE_SOLVE_POLICY_USE_LEVEL = 1 end enum
5.1.1. cusparseCreate
This function initializes the cuSPARSE library and creates a handle on the cuSPARSE context. It must be called before any other cuSPARSE API function is invoked. It allocates hardware resources necessary for accessing the GPU.
integer(4) function cusparseCreate(handle) type(cusparseHandle) :: handle
5.1.2. cusparseDestroy
This function releases CPU-side resources used by the cuSPARSE library. The release of GPU-side resources may be deferred until the application shuts down.
integer(4) function cusparseDestroy(handle) type(cusparseHandle) :: handle
5.1.3. cusparseGetVersion
This function returns the version number of the cuSPARSE library.
integer(4) function cusparseGetVersion(handle, version) type(cusparseHandle) :: handle integer(c_int) :: version
5.1.4. cusparseSetStream
This function sets the stream to be used by the cuSPARSE library to execute its routines.
integer(4) function cusparseSetStream(handle, stream) type(cusparseHandle) :: handle integer(cuda_stream_kind) :: stream
5.1.5. cusparseGetPointerMode
This function obtains the pointer mode used by the cuSPARSE library. Please see section 1.6 for more details on pointer modes.
integer(4) function cusparseGetPointerMode(handle, mode) type(cusparseHandle) :: handle integer(c_int) :: mode
5.1.6. cusparseSetPointerMode
This function sets the pointer mode used by the cuSPARSE library. In these Fortran interfaces, this only has an effect when using the *_v2 interfaces. The default is for the values to be passed by reference on the host. Please see section 1.6 for more details on pointer modes.
integer(4) function cusparseSetPointerMode(handle, mode) type(cusparseHandle) :: handle integer(4) :: mode
5.1.7. cusparseCreateMatDescr
This function initializes the matrix descriptor. It sets the fields MatrixType and IndexBase to the default values CUSPARSE_MATRIX_TYPE_GENERAL and CUSPARSE_INDEX_BASE_ZERO , respectively, while leaving other fields uninitialized.
integer(4) function cusparseCreateMatDescr(descrA) type(cusparseMatDescr) :: descrA
5.1.8. cusparseDestroyMatDescr
This function releases the memory allocated for the matrix descriptor.
integer(4) function cusparseDestroyMatDescr(descrA) type(cusparseMatDescr) :: descrA
5.1.9. cusparseSetMatType
This function sets the MatrixType of the matrix descriptor descrA.
integer(4) function cusparseSetMatType(descrA, type) type(cusparseMatDescr) :: descrA integer(4) :: type
5.1.10. cusparseGetMatType
This function returns the MatrixType of the matrix descriptor descrA.
integer(4) function cusparseGetMatType(descrA) type(cusparseMatDescr) :: descrA
5.1.11. cusparseSetMatFillMode
This function sets the FillMode field of the matrix descriptor descrA.
integer(4) function cusparseSetMatFillMode(descrA, mode) type(cusparseMatDescr) :: descrA integer(4) :: mode
5.1.12. cusparseGetMatFillMode
This function returns the FillMode field of the matrix descriptor descrA.
integer(4) function cusparseGetMatFillMode(descrA) type(cusparseMatDescr) :: descrA
5.1.13. cusparseSetMatDiagType
This function sets the DiagType of the matrix descriptor descrA.
integer(4) function cusparseSetMatDiagType(descrA, type) type(cusparseMatDescr) :: descrA integer(4) :: type
5.1.14. cusparseGetMatDiagType
This function returns the DiagType of the matrix descriptor descrA.
integer(4) function cusparseGetMatDiagType(descrA) type(cusparseMatDescr) :: descrA
5.1.15. cusparseSetMatIndexBase
This function sets the IndexBase field of the matrix descriptor descrA.
integer(4) function cusparseSetMatIndexBase(descrA, base) type(cusparseMatDescr) :: descrA integer(4) :: base
5.1.16. cusparseGetMatIndexBase
This function returns the IndexBase field of the matrix descriptor descrA.
integer(4) function cusparseGetMatIndexBase(descrA) type(cusparseMatDescr) :: descrA
5.1.17. cusparseCreateSolveAnalysisInfo
This function creates and initializes the solve and analysis structure to default values.
integer(4) function cusparseCreateSolveAnalysisInfo(info) type(cusparseSolveAnalysisinfo) :: info
5.1.18. cusparseDestroySolveAnalysisInfo
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroySolveAnalysisInfo(info) type(cusparseSolveAnalysisinfo) :: info
5.1.19. cusparseGetLevelInfo
This function returns the number of levels and the assignment of rows into the levels computed by either the csrsv_analysis, csrsm_analysis or hybsv_analysis routines.
integer(4) function cusparseGetLevelInfo(handle, info, nlevels, levelPtr, levelInd) type(cusparseHandle) :: handle type(cusparseSolveAnalysisinfo) :: info integer(c_int) :: nlevels type(c_ptr) :: levelPtr type(c_ptr) :: levelInd
5.1.20. cusparseCreateHybMat
This function creates and initializes the hybA opaque data structure.
integer(4) function cusparseCreateHybMat(hybA) type(cusparseHybMat) :: hybA
5.1.21. cusparseDestroyHybMat
This function destroys and releases any memory required by the hybA structure.
integer(4) function cusparseDestroyHybMat(hybA) type(cusparseHybMat) :: hybA
5.1.22. cusparseCreateCsrsv2Info
This function creates and initializes the solve and analysis structure of csrsv2 to default values.
integer(4) function cusparseCreateCsrsv2Info(info) type(cusparseCsrsv2Info) :: info
5.1.23. cusparseDestroyCsrsv2Info
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroyCsrsv2Info(info) type(cusparseCsrsv2Info) :: info
5.1.24. cusparseCreateCsric02Info
This function creates and initializes the solve and analysis structure of incomplete Cholesky to default values.
integer(4) function cusparseCreateCsric02Info(info) type(cusparseCsric02Info) :: info
5.1.25. cusparseDestroyCsric02Info
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroyCsric02Info(info) type(cusparseCsric02Info) :: info
5.1.26. cusparseCreateCsrilu02Info
This function creates and initializes the solve and analysis structure of incomplete LU to default values.
integer(4) function cusparseCreateCsrilu02Info(info) type(cusparseCsrilu02Info) :: info
5.1.27. cusparseDestroyCsrilu02Info
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroyCsrilu02Info(info) type(cusparseCsrilu02Info) :: info
5.1.28. cusparseCreateBsrsv2Info
This function creates and initializes the solve and analysis structure of bsrsv2 to default values.
integer(4) function cusparseCreateBsrsv2Info(info) type(cusparseBsrsv2Info) :: info
5.1.29. cusparseDestroyBsrsv2Info
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroyBsrsv2Info(info) type(cusparseBsrsv2Info) :: info
5.1.30. cusparseCreateBsric02Info
This function creates and initializes the solve and analysis structure of block incomplete Cholesky to default values.
integer(4) function cusparseCreateBsric02Info(info) type(cusparseBsric02Info) :: info
5.1.31. cusparseDestroyBsric02Info
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroyBsric02Info(info) type(cusparseBsric02Info) :: info
5.1.32. cusparseCreateBsrilu02Info
This function creates and initializes the solve and analysis structure of block incomplete LU to default values.
integer(4) function cusparseCreateBsrilu02Info(info) type(cusparseBsrilu02Info) :: info
5.1.33. cusparseDestroyBsrilu02Info
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroyBsrilu02Info(info) type(cusparseBsrilu02Info) :: info
5.1.34. cusparseCreateBsrsm2Info
This function creates and initializes the solve and analysis structure of bsrsm2 to default values.
integer(4) function cusparseCreateBsrsm2Info(info) type(cusparseBsrsm2Info) :: info
5.1.35. cusparseDestroyBsrsm2Info
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroyBsrsm2Info(info) type(cusparseBsrsm2Info) :: info
5.1.36. cusparseCreateCsrgemm2Info
This function creates and initializes the analysis structure of general sparse matrix-matrix multiplication.
integer(4) function cusparseCreateCsrgemm2Info(info) type(cusparseCsrgemm2Info) :: info
5.1.37. cusparseDestroyCsrgemm2Info
This function destroys and releases any memory required by the structure.
integer(4) function cusparseDestroyCsrgemm2Info(info) type(cusparseCsrgemm2Info) :: info
5.1.38. cusparseCreateColorInfo
This function creates coloring information used in calls like CSRCOLOR.
integer(4) function cusparseCreateColorInfo(info) type(cusparseColorInfo) :: info
5.1.39. cusparseDestroyColorInfo
This function destroys coloring information used in calls like CSRCOLOR.
integer(4) function cusparseDestroyColorInfo(info) type(cusparseColorInfo) :: info
5.1.40. cusparseCreateCsru2csrInfo
This function creates sorting information used in calls like CSRU2CSR.
integer(4) function cusparseCreateCsru2csrInfo(info) type(cusparseCsru2csrInfo) :: info
5.1.41. cusparseDestroyCsru2csrInfo
This function creates sorting information used in calls like CSRU2CSR.
integer(4) function cusparseDestroyCsru2csrInfo(info) type(cusparseCsru2csrInfo) :: info
5.2. CUSPARSE Level 1 Functions
This section contains interfaces for the level 1 sparse linear algebra functions that perform operations between dense and sparse vectors.
5.2.1. cusparseSaxpyi
SAXPY performs constant times a vector plus a vector. This function multiplies the vector x in sparse format by the constant alpha and adds the result to the vector y in dense format, i.e. y = y + alpha * xVal(xInd)
integer(4) function cusparseSaxpyi(handle, nnz, alpha, xVal, xInd, y, idxBase) type(cusparseHandle) :: handle integer :: nnz real(4), device :: alpha ! device or host variable real(4), device :: xVal(*) integer(4), device :: xInd(*) real(4), device :: y(*) integer :: idxBase
5.2.2. cusparseDaxpyi
DAXPY performs constant times a vector plus a vector. This function multiplies the vector x in sparse format by the constant alpha and adds the result to the vector y in dense format, i.e. y = y + alpha * xVal(xInd)
integer(4) function cusparseDaxpyi(handle, nnz, alpha, xVal, xInd, y, idxBase) type(cusparseHandle) :: handle integer :: nnz real(8), device :: alpha ! device or host variable real(8), device :: xVal(*) integer(4), device :: xInd(*) real(8), device :: y(*) integer :: idxBase
5.2.3. cusparseCaxpyi
CAXPY performs constant times a vector plus a vector. This function multiplies the vector x in sparse format by the constant alpha and adds the result to the vector y in dense format, i.e. y = y + alpha * xVal(xInd)
integer(4) function cusparseCaxpyi(handle, nnz, alpha, xVal, xInd, y, idxBase) type(cusparseHandle) :: handle integer :: nnz complex(4), device :: alpha ! device or host variable complex(4), device :: xVal(*) integer(4), device :: xInd(*) complex(4), device :: y(*) integer :: idxBase
5.2.4. cusparseZaxpyi
ZAXPY performs constant times a vector plus a vector. This function multiplies the vector x in sparse format by the constant alpha and adds the result to the vector y in dense format, i.e. y = y + alpha * xVal(xInd)
integer(4) function cusparseZaxpyi(handle, nnz, alpha, xVal, xInd, y, idxBase) type(cusparseHandle) :: handle integer :: nnz complex(8), device :: alpha ! device or host variable complex(8), device :: xVal(*) integer(4), device :: xInd(*) complex(8), device :: y(*) integer :: idxBase
5.2.5. cusparseSdoti
SDOT forms the dot product of two vectors. This function returns the dot product of a vector x in sparse format and vector y in dense format, i.e. res = sum(y * xVal(xInd))
integer(4) function cusparseSdoti(handle, nnz, xVal, xInd, y, res, idxBase) type(cusparseHandle) :: handle integer :: nnz real(4), device :: xVal(*) integer(4), device :: xInd(*) real(4), device :: y(*) real(4), device :: res ! device or host variable integer :: idxBase
5.2.6. cusparseDdoti
DDOT forms the dot product of two vectors. This function returns the dot product of a vector x in sparse format and vector y in dense format, i.e. res = sum(y * xVal(xInd))
integer(4) function cusparseDdoti(handle, nnz, xVal, xInd, y, res, idxBase) type(cusparseHandle) :: handle integer :: nnz real(8), device :: xVal(*) integer(4), device :: xInd(*) real(8), device :: y(*) real(8), device :: res ! device or host variable integer :: idxBase
5.2.7. cusparseCdoti
CDOT forms the dot product of two vectors. This function returns the dot product of a vector x in sparse format and vector y in dense format, i.e. res = sum(y * xVal(xInd))
integer(4) function cusparseCdoti(handle, nnz, xVal, xInd, y, res, idxBase) type(cusparseHandle) :: handle integer :: nnz complex(4), device :: xVal(*) integer(4), device :: xInd(*) complex(4), device :: y(*) complex(4), device :: res ! device or host variable integer :: idxBase
5.2.8. cusparseZdoti
ZDOT forms the dot product of two vectors. This function returns the dot product of a vector x in sparse format and vector y in dense format, i.e. res = sum(y * xVal(xInd))
integer(4) function cusparseZdoti(handle, nnz, xVal, xInd, y, res, idxBase) type(cusparseHandle) :: handle integer :: nnz complex(8), device :: xVal(*) integer(4), device :: xInd(*) complex(8), device :: y(*) complex(8), device :: res ! device or host variable integer :: idxBase
5.2.9. cusparseCdotci
CDOTC forms the dot product of two vectors, conjugating the first vector. This function returns the dot product of a vector x in sparse format and vector y in dense format, i.e. res = sum(y * conjg(xVal(xInd))
integer(4) function cusparseCdotci(handle, nnz, xVal, xInd, y, res, idxBase) type(cusparseHandle) :: handle integer :: nnz complex(4), device :: xVal(*) integer(4), device :: xInd(*) complex(4), device :: y(*) complex(4), device :: res ! device or host variable integer :: idxBase
5.2.10. cusparseZdotci
ZDOTC forms the dot product of two vectors, conjugating the first vector. This function returns the dot product of a vector x in sparse format and vector y in dense format, i.e. res = sum(y * conjg(xVal(xInd))
integer(4) function cusparseZdotci(handle, nnz, xVal, xInd, y, res, idxBase) type(cusparseHandle) :: handle integer :: nnz complex(8), device :: xVal(*) integer(4), device :: xInd(*) complex(8), device :: y(*) complex(8), device :: res ! device or host variable integer :: idxBase
5.2.11. cusparseSgthr
This function gathers the elements of the vector y listed in the index array xInd into data array xVal, i.e. xVal = y(xInd) Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseSgthr(handle, nnz, y, xVal, xInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz real(4), device :: y(*) real(4), device :: xVal(*) integer(4), device :: xInd(*) integer(4) :: idxBase
5.2.12. cusparseDgthr
This function gathers the elements of the vector y listed in the index array xInd into data array xVal, i.e. xVal = y(xInd) Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseDgthr(handle, nnz, y, xVal, xInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz real(8), device :: y(*) real(8), device :: xVal(*) integer(4), device :: xInd(*) integer(4) :: idxBase
5.2.13. cusparseCgthr
This function gathers the elements of the vector y listed in the index array xInd into data array xVal, i.e. xVal = y(xInd) Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseCgthr(handle, nnz, y, xVal, xInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz complex(4), device :: y(*) complex(4), device :: xVal(*) integer(4), device :: xInd(*) integer(4) :: idxBase
5.2.14. cusparseZgthr
This function gathers the elements of the vector y listed in the index array xInd into data array xVal, i.e. xVal = y(xInd) Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseZgthr(handle, nnz, y, xVal, xInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz complex(8), device :: y(*) complex(8), device :: xVal(*) integer(4), device :: xInd(*) integer(4) :: idxBase
5.2.15. cusparseSgthrz
This function gathers the elements of the vector y listed in the index array xInd into data array xVal, i.e. xVal = y(xInd); y(xInd) = 0.0 Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseSgthrz(handle, nnz, y, xVal, xInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz real(4), device :: y(*) real(4), device :: xVal(*) integer(4), device :: xInd(*) integer(4) :: idxBase
5.2.16. cusparseDgthrz
This function gathers the elements of the vector y listed in the index array xInd into data array xVal, i.e. xVal = y(xInd); y(xInd) = 0.0 Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseDgthrz(handle, nnz, y, xVal, xInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz real(8), device :: y(*) real(8), device :: xVal(*) integer(4), device :: xInd(*) integer(4) :: idxBase
5.2.17. cusparseCgthrz
This function gathers the elements of the vector y listed in the index array xInd into data array xVal, i.e. xVal = y(xInd); y(xInd) = 0.0 Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseCgthrz(handle, nnz, y, xVal, xInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz complex(4), device :: y(*) complex(4), device :: xVal(*) integer(4), device :: xInd(*) integer(4) :: idxBase
5.2.18. cusparseZgthrz
This function gathers the elements of the vector y listed in the index array xInd into data array xVal, i.e. xVal = y(xInd); y(xInd) = 0.0 Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseZgthrz(handle, nnz, y, xVal, xInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz complex(8), device :: y(*) complex(8), device :: xVal(*) integer(4), device :: xInd(*) integer(4) :: idxBase
5.2.19. cusparseSsctr
This function scatters the elements of the dense format vector x into the vector y in sparse format, i.e. y(xInd) = x Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseSsctr(handle, nnz, xVal, xInd, y, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz real(4), device :: xVal(*) integer(4), device :: xInd(*) real(4), device :: y(*) integer(4) :: idxBase
5.2.20. cusparseDsctr
This function scatters the elements of the dense format vector x into the vector y in sparse format, i.e. y(xInd) = x Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseDsctr(handle, nnz, xVal, xInd, y, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz real(8), device :: xVal(*) integer(4), device :: xInd(*) real(8), device :: y(*) integer(4) :: idxBase
5.2.21. cusparseCsctr
This function scatters the elements of the dense format vector x into the vector y in sparse format, i.e. y(xInd) = x Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseCsctr(handle, nnz, xVal, xInd, y, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz complex(4), device :: xVal(*) integer(4), device :: xInd(*) complex(4), device :: y(*) integer(4) :: idxBase
5.2.22. cusparseZsctr
This function scatters the elements of the dense format vector x into the vector y in sparse format, i.e. y(xInd) = x Fortran programmers should normally use idxBase == 1.
integer(4) function cusparseZsctr(handle, nnz, xVal, xInd, y, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz complex(8), device :: xVal(*) integer(4), device :: xInd(*) complex(8), device :: y(*) integer(4) :: idxBase
5.2.23. cusparseSroti
SROT applies a plane rotation. X is a sparse vector and Y is dense.
integer(4) function cusparseSroti(handle, nnz, xVal, xInd, y, c, s, idxBase) type(cusparseHandle) :: handle integer :: nnz real(4), device :: xVal(*) integer(4), device :: xInd(*) real(4), device :: y(*) real(4), device :: c, s ! device or host variable integer :: idxBase
5.2.24. cusparseDroti
DROT applies a plane rotation. X is a sparse vector and Y is dense.
integer(4) function cusparseDroti(handle, nnz, xVal, xInd, y, c, s, idxBase) type(cusparseHandle) :: handle integer :: nnz real(8), device :: xVal(*) integer(4), device :: xInd(*) real(8), device :: y(*) real(8), device :: c, s ! device or host variable integer :: idxBase
5.3. CUSPARSE Level 2 Functions
This section contains interfaces for the level 2 sparse linear algebra functions that perform operations between sparse matrices and dense vectors.
5.3.1. cusparseSbsrmv
BSRMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an (mb*blockDim) x (nb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrVal, bsrRowPtr, and bsrColInd
integer(4) function cusparseSbsrmv(handle, dir, trans, mb, nb, nnzb, alpha, descr, bsrVal, bsrRowPtr, bsrColInd, blockDim, x, beta, y) type(cusparseHandle) :: handle integer :: dir integer :: trans integer :: mb, nb, nnzb real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr real(4), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*) integer(4), device :: bsrColInd(*) integer :: blockDim real(4), device :: x(*) real(4), device :: beta ! device or host variable real(4), device :: y(*)
5.3.2. cusparseDbsrmv
BSRMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an (mb*blockDim) x (nb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrVal, bsrRowPtr, and bsrColInd
integer(4) function cusparseDbsrmv(handle, dir, trans, mb, nb, nnzb, alpha, descr, bsrVal, bsrRowPtr, bsrColInd, blockDim, x, beta, y) type(cusparseHandle) :: handle integer :: dir integer :: trans integer :: mb, nb, nnzb real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr real(8), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*) integer(4), device :: bsrColInd(*) integer :: blockDim real(8), device :: x(*) real(8), device :: beta ! device or host variable real(8), device :: y(*)
5.3.3. cusparseCbsrmv
BSRMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an (mb*blockDim) x (nb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrVal, bsrRowPtr, and bsrColInd
integer(4) function cusparseCbsrmv(handle, dir, trans, mb, nb, nnzb, alpha, descr, bsrVal, bsrRowPtr, bsrColInd, blockDim, x, beta, y) type(cusparseHandle) :: handle integer :: dir integer :: trans integer :: mb, nb, nnzb complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr complex(4), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*) integer(4), device :: bsrColInd(*) integer :: blockDim complex(4), device :: x(*) complex(4), device :: beta ! device or host variable complex(4), device :: y(*)
5.3.4. cusparseZbsrmv
BSRMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an (mb*blockDim) x (nb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrVal, bsrRowPtr, and bsrColInd
integer(4) function cusparseZbsrmv(handle, dir, trans, mb, nb, nnzb, alpha, descr, bsrVal, bsrRowPtr, bsrColInd, blockDim, x, beta, y) type(cusparseHandle) :: handle integer :: dir integer :: trans integer :: mb, nb, nnzb complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr complex(8), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*) integer(4), device :: bsrColInd(*) integer :: blockDim complex(8), device :: x(*) complex(8), device :: beta ! device or host variable complex(8), device :: y(*)
5.3.5. cusparseSbsrxmv
BSRXMV performs a BSRMV and a mask operation.
integer(4) function cusparseSbsrxmv(handle, dir, trans, sizeOfMask, mb, nb, nnzb, alpha, descr, bsrVal, bsrMaskPtr, bsrRowPtr, bsrEndPtr, bsrColInd, blockDim, x, beta, y) type(cusparseHandle) :: handle integer :: dir integer :: trans integer :: sizeOfMask integer :: mb, nb, nnzb real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr real(4), device :: bsrVal(*) integer(4), device :: bsrMaskPtr(*), bsrRowPtr(*), bsrEndPtr(*) integer(4), device :: bsrColInd(*) integer :: blockDim real(4), device :: x(*) real(4), device :: beta ! device or host variable real(4), device :: y(*)
5.3.6. cusparseDbsrxmv
BSRXMV performs a BSRMV and a mask operation.
integer(4) function cusparseDbsrxmv(handle, dir, trans, sizeOfMask, mb, nb, nnzb, alpha, descr, bsrVal, bsrMaskPtr, bsrRowPtr, bsrEndPtr, bsrColInd, blockDim, x, beta, y) type(cusparseHandle) :: handle integer :: dir integer :: trans integer :: sizeOfMask integer :: mb, nb, nnzb real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr real(8), device :: bsrVal(*) integer(4), device :: bsrMaskPtr(*), bsrRowPtr(*), bsrEndPtr(*) integer(4), device :: bsrColInd(*) integer :: blockDim real(8), device :: x(*) real(8), device :: beta ! device or host variable real(8), device :: y(*)
5.3.7. cusparseCbsrxmv
BSRXMV performs a BSRMV and a mask operation.
integer(4) function cusparseCbsrxmv(handle, dir, trans, sizeOfMask, mb, nb, nnzb, alpha, descr, bsrVal, bsrMaskPtr, bsrRowPtr, bsrEndPtr, bsrColInd, blockDim, x, beta, y) type(cusparseHandle) :: handle integer :: dir integer :: trans integer :: sizeOfMask integer :: mb, nb, nnzb complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr complex(4), device :: bsrVal(*) integer(4), device :: bsrMaskPtr(*), bsrRowPtr(*), bsrEndPtr(*) integer(4), device :: bsrColInd(*) integer :: blockDim complex(4), device :: x(*) complex(4), device :: beta ! device or host variable complex(4), device :: y(*)
5.3.8. cusparseZbsrxmv
BSRXMV performs a BSRMV and a mask operation.
integer(4) function cusparseZbsrxmv(handle, dir, trans, sizeOfMask, mb, nb, nnzb, alpha, descr, bsrVal, bsrMaskPtr, bsrRowPtr, bsrEndPtr, bsrColInd, blockDim, x, beta, y) type(cusparseHandle) :: handle integer :: dir integer :: trans integer :: sizeOfMask integer :: mb, nb, nnzb complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr complex(8), device :: bsrVal(*) integer(4), device :: bsrMaskPtr(*), bsrRowPtr(*), bsrEndPtr(*) integer(4), device :: bsrColInd(*) integer :: blockDim complex(8), device :: x(*) complex(8), device :: beta ! device or host variable complex(8), device :: y(*)
5.3.9. cusparseScsrmv
CSRMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m x n sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA
integer(4) function cusparseScsrmv(handle, trans, m, n, nnz, alpha, descr, csrVal, csrRowPtr, csrColInd, x, beta, y) type(cusparseHandle) :: handle integer :: trans integer :: m, n, nnz real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr real(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) real(4), device :: x(*) real(4), device :: beta ! device or host variable real(4), device :: y(*)
5.3.10. cusparseDcsrmv
CSRMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m x n sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA
integer(4) function cusparseDcsrmv(handle, trans, m, n, nnz, alpha, descr, csrVal, csrRowPtr, csrColInd, x, beta, y) type(cusparseHandle) :: handle integer :: trans integer :: m, n, nnz real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr real(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) real(8), device :: x(*) real(8), device :: beta ! device or host variable real(8), device :: y(*)
5.3.11. cusparseCcsrmv
CSRMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m x n sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA
integer(4) function cusparseCcsrmv(handle, trans, m, n, nnz, alpha, descr, csrVal, csrRowPtr, csrColInd, x, beta, y) type(cusparseHandle) :: handle integer :: trans integer :: m, n, nnz complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr complex(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) complex(4), device :: x(*) complex(4), device :: beta ! device or host variable complex(4), device :: y(*)
5.3.12. cusparseZcsrmv
CSRMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m x n sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA
integer(4) function cusparseZcsrmv(handle, trans, m, n, nnz, alpha, descr, csrVal, csrRowPtr, csrColInd, x, beta, y) type(cusparseHandle) :: handle integer :: trans integer :: m, n, nnz complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr complex(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) complex(8), device :: x(*) complex(8), device :: beta ! device or host variable complex(8), device :: y(*)
5.3.13. cusparseScsrsv_analysis
This function performs the analysis phase of csrsv.
integer(4) function cusparseScsrsv_analysis(handle, trans, m, nnz, descr, csrVal, csrRowPtr, csrColInd, info) type(cusparseHandle) :: handle integer(4) :: trans integer(4) :: m, nnz type(cusparseMatDescr) :: descr real(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) type(cusparseSolveAnalysisInfo) :: info
5.3.14. cusparseDcsrsv_analysis
This function performs the analysis phase of csrsv.
integer(4) function cusparseDcsrsv_analysis(handle, trans, m, nnz, descr, csrVal, csrRowPtr, csrColInd, info) type(cusparseHandle) :: handle integer(4) :: trans integer(4) :: m, nnz type(cusparseMatDescr) :: descr real(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) type(cusparseSolveAnalysisInfo) :: info
5.3.15. cusparseCcsrsv_analysis
This function performs the analysis phase of csrsv.
integer(4) function cusparseCcsrsv_analysis(handle, trans, m, nnz, descr, csrVal, csrRowPtr, csrColInd, info) type(cusparseHandle) :: handle integer(4) :: trans integer(4) :: m, nnz type(cusparseMatDescr) :: descr complex(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) type(cusparseSolveAnalysisInfo) :: info
5.3.16. cusparseZcsrsv_analysis
This function performs the analysis phase of csrsv.
integer(4) function cusparseZcsrsv_analysis(handle, trans, m, nnz, descr, csrVal, csrRowPtr, csrColInd, info) type(cusparseHandle) :: handle integer(4) :: trans integer(4) :: m, nnz type(cusparseMatDescr) :: descr complex(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) type(cusparseSolveAnalysisInfo) :: info
5.3.17. cusparseScsrsv_solve
This function performs the solve phase of csrsv.
integer(4) function cusparseScsrsv_solve(handle, trans, m, alpha, descr, csrVal, csrRowPtr, csrColInd, info, x, y) type(cusparseHandle) :: handle integer :: trans integer :: m real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr real(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) type(cusparseSolveAnalysisInfo) :: info real(4), device :: x(*) real(4), device :: y(*)
5.3.18. cusparseDcsrsv_solve
This function performs the solve phase of csrsv.
integer(4) function cusparseDcsrsv_solve(handle, trans, m, alpha, descr, csrVal, csrRowPtr, csrColInd, info, x, y) type(cusparseHandle) :: handle integer :: trans integer :: m real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr real(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) type(cusparseSolveAnalysisInfo) :: info real(8), device :: x(*) real(8), device :: y(*)
5.3.19. cusparseCcsrsv_solve
This function performs the solve phase of csrsv.
integer(4) function cusparseCcsrsv_solve(handle, trans, m, alpha, descr, csrVal, csrRowPtr, csrColInd, info, x, y) type(cusparseHandle) :: handle integer :: trans integer :: m complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr complex(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) type(cusparseSolveAnalysisInfo) :: info complex(4), device :: x(*) complex(4), device :: y(*)
5.3.20. cusparseZcsrsv_solve
This function performs the solve phase of csrsv.
integer(4) function cusparseZcsrsv_solve(handle, trans, m, alpha, descr, csrVal, csrRowPtr, csrColInd, info, x, y) type(cusparseHandle) :: handle integer :: trans integer :: m complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr complex(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*) integer(4), device :: csrColInd(*) type(cusparseSolveAnalysisInfo) :: info complex(8), device :: x(*) complex(8), device :: y(*)
5.3.21. cusparseShybmv
HYBMV performs the matrix-vector operations y := alpha*A*x + beta*y where alpha and beta are scalars, x and y are vectors and A is an m x n sparse matrix that is defined in the HYB storage format.
integer(4) function cusparseShybmv(handle, trans, alpha, descr, hyb, x, beta, y) type(cusparseHandle) :: handle integer :: trans real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb real(4), device :: x(*) real(4), device :: beta ! device or host variable real(4), device :: y(*)
5.3.22. cusparseDhybmv
HYBMV performs the matrix-vector operations y := alpha*A*x + beta*y where alpha and beta are scalars, x and y are vectors and A is an m x n sparse matrix that is defined in the HYB storage format.
integer(4) function cusparseDhybmv(handle, trans, alpha, descr, hyb, x, beta, y) type(cusparseHandle) :: handle integer :: trans real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb real(8), device :: x(*) real(8), device :: beta ! device or host variable real(8), device :: y(*)
5.3.23. cusparseChybmv
HYBMV performs the matrix-vector operations y := alpha*A*x + beta*y where alpha and beta are scalars, x and y are vectors and A is an m x n sparse matrix that is defined in the HYB storage format.
integer(4) function cusparseChybmv(handle, trans, alpha, descr, hyb, x, beta, y) type(cusparseHandle) :: handle integer :: trans complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb complex(4), device :: x(*) complex(4), device :: beta ! device or host variable complex(4), device :: y(*)
5.3.24. cusparseZhybmv
HYBMV performs the matrix-vector operations y := alpha*A*x + beta*y where alpha and beta are scalars, x and y are vectors and A is an m x n sparse matrix that is defined in the HYB storage format.
integer(4) function cusparseZhybmv(handle, trans, alpha, descr, hyb, x, beta, y) type(cusparseHandle) :: handle integer :: trans complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb complex(8), device :: x(*) complex(8), device :: beta ! device or host variable complex(8), device :: y(*)
5.3.25. cusparseShybsv_analysis
This function performs the analysis phase of hybsv.
integer(4) function cusparseShybsv_analysis(handle, trans, descr, hyb, info) type(cusparseHandle) :: handle integer(4) :: trans type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb type(cusparseSolveAnalysisInfo) :: info
5.3.26. cusparseDhybsv_analysis
This function performs the analysis phase of hybsv.
integer(4) function cusparseDhybsv_analysis(handle, trans, descr, hyb, info) type(cusparseHandle) :: handle integer(4) :: trans type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb type(cusparseSolveAnalysisInfo) :: info
5.3.27. cusparseChybsv_analysis
This function performs the analysis phase of hybsv.
integer(4) function cusparseChybsv_analysis(handle, trans, descr, hyb, info) type(cusparseHandle) :: handle integer(4) :: trans type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb type(cusparseSolveAnalysisInfo) :: info
5.3.28. cusparseZhybsv_analysis
This function performs the analysis phase of hybsv.
integer(4) function cusparseZhybsv_analysis(handle, trans, descr, hyb, info) type(cusparseHandle) :: handle integer(4) :: trans type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb type(cusparseSolveAnalysisInfo) :: info
5.3.29. cusparseShybsv_solve
This function performs the solve phase of hybsv.
integer(4) function cusparseShybsv_solve(handle, trans, alpha, descr, hyb, info, x, y) type(cusparseHandle) :: handle integer :: trans real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb type(cusparseSolveAnalysisInfo) :: info real(4), device :: x(*) real(4), device :: y(*)
5.3.30. cusparseDhybsv_solve
This function performs the solve phase of hybsv.
integer(4) function cusparseDhybsv_solve(handle, trans, alpha, descr, hyb, info, x, y) type(cusparseHandle) :: handle integer :: trans real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb type(cusparseSolveAnalysisInfo) :: info real(8), device :: x(*) real(8), device :: y(*)
5.3.31. cusparseChybsv_solve
This function performs the solve phase of hybsv.
integer(4) function cusparseChybsv_solve(handle, trans, alpha, descr, hyb, info, x, y) type(cusparseHandle) :: handle integer :: trans complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb type(cusparseSolveAnalysisInfo) :: info complex(4), device :: x(*) complex(4), device :: y(*)
5.3.32. cusparseZhybsv_solve
This function performs the solve phase of hybsv.
integer(4) function cusparseZhybsv_solve(handle, trans, alpha, descr, hyb, info, x, y) type(cusparseHandle) :: handle integer :: trans complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descr type(cusparseHybMat) :: hyb type(cusparseSolveAnalysisInfo) :: info complex(8), device :: x(*) complex(8), device :: y(*)
5.3.33. cusparseSbsrsv2_bufferSize
This function returns the size of the buffer used in bsrsv2.
integer(4) function cusparseSbsrsv2_bufferSize(handle, dirA, transA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA, transA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrsv2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.3.34. cusparseDbsrsv2_bufferSize
This function returns the size of the buffer used in bsrsv2.
integer(4) function cusparseDbsrsv2_bufferSize(handle, dirA, transA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA, transA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrsv2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.3.35. cusparseCbsrsv2_bufferSize
This function returns the size of the buffer used in bsrsv2.
integer(4) function cusparseCbsrsv2_bufferSize(handle, dirA, transA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA, transA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrsv2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.3.36. cusparseZbsrsv2_bufferSize
This function returns the size of the buffer used in bsrsv2.
integer(4) function cusparseZbsrsv2_bufferSize(handle, dirA, transA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA, transA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrsv2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.3.37. cusparseSbsrsv2_analysis
This function performs the analysis phase of bsrsv2.
integer(4) function cusparseSbsrsv2_analysis(handle, dirA, transA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA, transA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrsv2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.3.38. cusparseDbsrsv2_analysis
This function performs the analysis phase of bsrsv2.
integer(4) function cusparseDbsrsv2_analysis(handle, dirA, transA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA, transA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrsv2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.3.39. cusparseCbsrsv2_analysis
This function performs the analysis phase of bsrsv2.
integer(4) function cusparseCbsrsv2_analysis(handle, dirA, transA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA, transA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrsv2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.3.40. cusparseZbsrsv2_analysis
This function performs the analysis phase of bsrsv2.
integer(4) function cusparseZbsrsv2_analysis(handle, dirA, transA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA, transA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrsv2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.3.41. cusparseSbsrsv2_solve
This function performs the solve phase of bsrsv2.
integer(4) function cusparseSbsrsv2_solve(handle, dirA, transA, mb, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, x, y, policy, pBuffer) type(cusparseHandle) :: handle integer :: dirA, transA, mb, nnzb real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: blockDim type(cusparseBsrsv2Info) :: info real(4), device :: x(*), y(*) integer :: policy character, device :: pBuffer(*)
5.3.42. cusparseDbsrsv2_solve
This function performs the solve phase of bsrsv2.
integer(4) function cusparseDbsrsv2_solve(handle, dirA, transA, mb, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, x, y, policy, pBuffer) type(cusparseHandle) :: handle integer :: dirA, transA, mb, nnzb real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: blockDim type(cusparseBsrsv2Info) :: info real(8), device :: x(*), y(*) integer :: policy character, device :: pBuffer(*)
5.3.43. cusparseCbsrsv2_solve
This function performs the solve phase of bsrsv2.
integer(4) function cusparseCbsrsv2_solve(handle, dirA, transA, mb, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, x, y, policy, pBuffer) type(cusparseHandle) :: handle integer :: dirA, transA, mb, nnzb complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: blockDim type(cusparseBsrsv2Info) :: info complex(4), device :: x(*), y(*) integer :: policy character, device :: pBuffer(*)
5.3.44. cusparseZbsrsv2_solve
This function performs the solve phase of bsrsv2.
integer(4) function cusparseZbsrsv2_solve(handle, dirA, transA, mb, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, x, y, policy, pBuffer) type(cusparseHandle) :: handle integer :: dirA, transA, mb, nnzb complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: blockDim type(cusparseBsrsv2Info) :: info complex(8), device :: x(*), y(*) integer :: policy character, device :: pBuffer(*)
5.3.45. cusparseXbsrsv2_zeroPivot
This function returns an error code equal to CUSPARSE_STATUS_ZERO_PIVOT and sets position to j when A(j,j) is either structural zero or numerical zero. Otherwise, position is set to -1.
integer(4) function cusparseXbsrsv2_zeroPivot(handle, info, position) type(cusparseHandle) :: handle type(cusparseBsrsv2Info) :: info integer(4), device :: position ! device or host variable
5.3.46. cusparseScsrsv2_bufferSize
This function returns the size of the buffer used in csrsv2.
integer(4) function cusparseScsrsv2_bufferSize(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.3.47. cusparseDcsrsv2_bufferSize
This function returns the size of the buffer used in csrsv2.
integer(4) function cusparseDcsrsv2_bufferSize(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.3.48. cusparseCcsrsv2_bufferSize
This function returns the size of the buffer used in csrsv2.
integer(4) function cusparseCcsrsv2_bufferSize(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.3.49. cusparseZcsrsv2_bufferSize
This function returns the size of the buffer used in csrsv2.
integer(4) function cusparseZcsrsv2_bufferSize(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.3.50. cusparseScsrsv2_analysis
This function performs the analysis phase of csrsv2.
integer(4) function cusparseScsrsv2_analysis(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.3.51. cusparseDcsrsv2_analysis
This function performs the analysis phase of csrsv2.
integer(4) function cusparseDcsrsv2_analysis(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.3.52. cusparseCcsrsv2_analysis
This function performs the analysis phase of csrsv2.
integer(4) function cusparseCcsrsv2_analysis(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.3.53. cusparseZcsrsv2_analysis
This function performs the analysis phase of csrsv2.
integer(4) function cusparseZcsrsv2_analysis(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.3.54. cusparseScsrsv2_solve
This function performs the solve phase of csrsv2.
integer(4) function cusparseScsrsv2_solve(handle, transA, m, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, info, x, y, policy, pBuffer) type(cusparseHandle) :: handle real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*), x(*), y(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer :: policy character, device :: pBuffer(*)
5.3.55. cusparseDcsrsv2_solve
This function performs the solve phase of csrsv2.
integer(4) function cusparseDcsrsv2_solve(handle, transA, m, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, info, x, y, policy, pBuffer) type(cusparseHandle) :: handle real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*), x(*), y(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer :: policy character, device :: pBuffer(*)
5.3.56. cusparseCcsrsv2_solve
This function performs the solve phase of csrsv2.
integer(4) function cusparseCcsrsv2_solve(handle, transA, m, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, info, x, y, policy, pBuffer) type(cusparseHandle) :: handle complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*), x(*), y(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer :: policy character, device :: pBuffer(*)
5.3.57. cusparseZcsrsv2_solve
This function performs the solve phase of csrsv2.
integer(4) function cusparseZcsrsv2_solve(handle, transA, m, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, info, x, y, policy, pBuffer) type(cusparseHandle) :: handle complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*), x(*), y(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrsv2Info) :: info integer :: policy character, device :: pBuffer(*)
5.3.58. cusparseXcsrsv2_zeroPivot
This function returns an error code equal to CUSPARSE_STATUS_ZERO_PIVOT and sets position to j when A(j,j) is either structural zero or numerical zero. Otherwise, position is set to -1.
integer(4) function cusparseXcsrsv2_zeroPivot(handle, info, position) type(cusparseHandle) :: handle type(cusparseCsrsv2Info) :: info integer(4), device :: position ! device or host variable
5.4. CUSPARSE Level 3 Functions
This section contains interfaces for the level 3 sparse linear algebra functions that perform operations between sparse and dense matrices.
5.4.1. cusparseScsrmm
CSRMM performs one of the matrix-matrix operations C := alpha*op( A ) * B + beta*C, where op( A ) is one of op( A ) = A or op( A ) = A**T, alpha and beta are scalars. A is an m x k sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA. B and C are dense matrices.
integer(4) function cusparseScsrmm(handle, transA, m, n, k, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: transA, m, n, k, nnz real(4), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*), B(*), C(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) integer :: ldb, ldc
5.4.2. cusparseDcsrmm
CSRMM performs one of the matrix-matrix operations C := alpha*op( A ) * B + beta*C, where op( A ) is one of op( A ) = A or op( A ) = A**T, alpha and beta are scalars. A is an m x k sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA. B and C are dense matrices.
integer(4) function cusparseDcsrmm(handle, transA, m, n, k, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: transA, m, n, k, nnz real(8), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*), B(*), C(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) integer :: ldb, ldc
5.4.3. cusparseCcsrmm
CSRMM performs one of the matrix-matrix operations C := alpha*op( A ) * B + beta*C, where op( A ) is one of op( A ) = A or op( A ) = A**T, alpha and beta are scalars. A is an m x k sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA. B and C are dense matrices.
integer(4) function cusparseCcsrmm(handle, transA, m, n, k, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: transA, m, n, k, nnz complex(4), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*), B(*), C(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) integer :: ldb, ldc
5.4.4. cusparseZcsrmm
CSRMM performs one of the matrix-matrix operations C := alpha*op( A ) * B + beta*C, where op( A ) is one of op( A ) = A or op( A ) = A**T, alpha and beta are scalars. A is an m x k sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA. B and C are dense matrices.
integer(4) function cusparseZcsrmm(handle, transA, m, n, k, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: transA, m, n, k, nnz complex(8), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*), B(*), C(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) integer :: ldb, ldc
5.4.5. cusparseScsrmm2
CSRMM2 performs one of the matrix-matrix operations C := alpha*op( A ) * op( B ) + beta*C, where op( A ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars. A is an m x k sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA. B and C are dense matrices.
integer(4) function cusparseScsrmm2(handle, transA, transB, m, n, k, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: transA, transB, m, n, k, nnz real(4), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*), B(*), C(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) integer :: ldb, ldc
5.4.6. cusparseDcsrmm2
CSRMM2 performs one of the matrix-matrix operations C := alpha*op( A ) * op( B ) + beta*C, where op( A ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars. A is an m x k sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA. B and C are dense matrices.
integer(4) function cusparseDcsrmm2(handle, transA, transB, m, n, k, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: transA, transB, m, n, k, nnz real(8), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*), B(*), C(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) integer :: ldb, ldc
5.4.7. cusparseCcsrmm2
CSRMM2 performs one of the matrix-matrix operations C := alpha*op( A ) * op( B ) + beta*C, where op( A ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars. A is an m x k sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA. B and C are dense matrices.
integer(4) function cusparseCcsrmm2(handle, transA, transB, m, n, k, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: transA, transB, m, n, k, nnz complex(4), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*), B(*), C(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) integer :: ldb, ldc
5.4.8. cusparseZcsrmm2
CSRMM2 performs one of the matrix-matrix operations C := alpha*op( A ) * op( B ) + beta*C, where op( A ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars. A is an m x k sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA. B and C are dense matrices.
integer(4) function cusparseZcsrmm2(handle, transA, transB, m, n, k, nnz, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: transA, transB, m, n, k, nnz complex(8), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*), B(*), C(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) integer :: ldb, ldc
5.4.9. cusparseScsrsm_analysis
This function performs the analysis phase of csrsm.
integer(4) function cusparseScsrsm_analysis(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.4.10. cusparseDcsrsm_analysis
This function performs the analysis phase of csrsm.
integer(4) function cusparseDcsrsm_analysis(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.4.11. cusparseCcsrsm_analysis
This function performs the analysis phase of csrsm.
integer(4) function cusparseCcsrsm_analysis(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.4.12. cusparseZcsrsm_analysis
This function performs the analysis phase of csrsm.
integer(4) function cusparseZcsrsm_analysis(handle, transA, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: transA, m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.4.13. cusparseScsrsm_solve
This function performs the solve phase of csrsm.
integer(4) function cusparseScsrsm_solve(handle, transA, m, n, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, info, X, ldx, Y, ldy) type(cusparseHandle) :: handle integer :: transA, m, n real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info real(4), device :: X(*), Y(*) integer :: ldx, ldy
5.4.14. cusparseDcsrsm_solve
This function performs the solve phase of csrsm.
integer(4) function cusparseDcsrsm_solve(handle, transA, m, n, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, info, X, ldx, Y, ldy) type(cusparseHandle) :: handle integer :: transA, m, n real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info real(8), device :: X(*), Y(*) integer :: ldx, ldy
5.4.15. cusparseCcsrsm_solve
This function performs the solve phase of csrsm.
integer(4) function cusparseCcsrsm_solve(handle, transA, m, n, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, info, X, ldx, Y, ldy) type(cusparseHandle) :: handle integer :: transA, m, n complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info complex(4), device :: X(*), Y(*) integer :: ldx, ldy
5.4.16. cusparseZcsrsm_solve
This function performs the solve phase of csrsm.
integer(4) function cusparseZcsrsm_solve(handle, transA, m, n, alpha, descrA, csrValA, csrRowPtrA, csrColIndA, info, X, ldx, Y, ldy) type(cusparseHandle) :: handle integer :: transA, m, n complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info complex(8), device :: X(*), Y(*) integer :: ldx, ldy
5.4.17. cusparseSbsrmm
BSRMM performs one of the matrix-matrix operations C := alpha*op( A ) * op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars. A is an mb x kb sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA. B and C are dense matrices.
integer(4) function cusparseSbsrmm(handle, dirA, transA, transB, mb, n, kb, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: dirA, transA, transB, mb, n, kb, nnzb, blockDim real(4), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*), B(*), C(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: ldb, ldc
5.4.18. cusparseDbsrmm
BSRMM performs one of the matrix-matrix operations C := alpha*op( A ) * op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars. A is an mb x kb sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA. B and C are dense matrices.
integer(4) function cusparseDbsrmm(handle, dirA, transA, transB, mb, n, kb, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: dirA, transA, transB, mb, n, kb, nnzb, blockDim real(8), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*), B(*), C(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: ldb, ldc
5.4.19. cusparseCbsrmm
BSRMM performs one of the matrix-matrix operations C := alpha*op( A ) * op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars. A is an mb x kb sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA. B and C are dense matrices.
integer(4) function cusparseCbsrmm(handle, dirA, transA, transB, mb, n, kb, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: dirA, transA, transB, mb, n, kb, nnzb, blockDim complex(4), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*), B(*), C(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: ldb, ldc
5.4.20. cusparseZbsrmm
BSRMM performs one of the matrix-matrix operations C := alpha*op( A ) * op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars. A is an mb x kb sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA. B and C are dense matrices.
integer(4) function cusparseZbsrmm(handle, dirA, transA, transB, mb, n, kb, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, B, ldb, beta, C, ldc) type(cusparseHandle) :: handle integer :: dirA, transA, transB, mb, n, kb, nnzb, blockDim complex(8), device :: alpha, beta ! device or host variable type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*), B(*), C(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: ldb, ldc
5.4.21. cusparseSbsrsm2_bufferSize
This function returns the size of the buffer used in bsrsm2.
integer(4) function cusparseSbsrsm2_bufferSize(handle, dirA, transA, transX, mb, n, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA, transA, transX, mb, n, nnzb, blockDim type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) type(cusparseBsrsm2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.4.22. cusparseDbsrsm2_bufferSize
This function returns the size of the buffer used in bsrsm2.
integer(4) function cusparseDbsrsm2_bufferSize(handle, dirA, transA, transX, mb, n, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA, transA, transX, mb, n, nnzb, blockDim type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) type(cusparseBsrsm2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.4.23. cusparseCbsrsm2_bufferSize
This function returns the size of the buffer used in bsrsm2.
integer(4) function cusparseCbsrsm2_bufferSize(handle, dirA, transA, transX, mb, n, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA, transA, transX, mb, n, nnzb, blockDim type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) type(cusparseBsrsm2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.4.24. cusparseZbsrsm2_bufferSize
This function returns the size of the buffer used in bsrsm2.
integer(4) function cusparseZbsrsm2_bufferSize(handle, dirA, transA, transX, mb, n, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA, transA, transX, mb, n, nnzb, blockDim type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) type(cusparseBsrsm2Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.4.25. cusparseSbsrsm2_analysis
This function performs the analysis phase of bsrsm2.
integer(4) function cusparseSbsrsm2_analysis(handle, dirA, transA, transX, mb, n, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA, transA, transX, mb, n, nnzb, blockDim type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) type(cusparseBsrsm2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.4.26. cusparseDbsrsm2_analysis
This function performs the analysis phase of bsrsm2.
integer(4) function cusparseDbsrsm2_analysis(handle, dirA, transA, transX, mb, n, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA, transA, transX, mb, n, nnzb, blockDim type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) type(cusparseBsrsm2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.4.27. cusparseCbsrsm2_analysis
This function performs the analysis phase of bsrsm2.
integer(4) function cusparseCbsrsm2_analysis(handle, dirA, transA, transX, mb, n, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA, transA, transX, mb, n, nnzb, blockDim type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) type(cusparseBsrsm2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.4.28. cusparseZbsrsm2_analysis
This function performs the analysis phase of bsrsm2.
integer(4) function cusparseZbsrsm2_analysis(handle, dirA, transA, transX, mb, n, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA, transA, transX, mb, n, nnzb, blockDim type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) type(cusparseBsrsm2Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.4.29. cusparseSbsrsm2_solve
This function performs the solve phase of bsrsm2.
integer(4) function cusparseSbsrsm2_solve(handle, dirA, transA, transX, mb, n, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, x, ldx, y, ldy, policy, pBuffer) type(cusparseHandle) :: handle integer :: dirA, transA, transX, mb, n, nnzb real(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*), x(*), y(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: blockDim, policy, ldx, ldy type(cusparseBsrsm2Info) :: info character, device :: pBuffer(*)
5.4.30. cusparseDbsrsm2_solve
This function performs the solve phase of bsrsm2.
integer(4) function cusparseDbsrsm2_solve(handle, dirA, transA, transX, mb, n, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, x, ldx, y, ldy, policy, pBuffer) type(cusparseHandle) :: handle integer :: dirA, transA, transX, mb, n, nnzb real(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*), x(*), y(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: blockDim, policy, ldx, ldy type(cusparseBsrsm2Info) :: info character, device :: pBuffer(*)
5.4.31. cusparseCbsrsm2_solve
This function performs the solve phase of bsrsm2.
integer(4) function cusparseCbsrsm2_solve(handle, dirA, transA, transX, mb, n, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, x, ldx, y, ldy, policy, pBuffer) type(cusparseHandle) :: handle integer :: dirA, transA, transX, mb, n, nnzb complex(4), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*), x(*), y(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: blockDim, policy, ldx, ldy type(cusparseBsrsm2Info) :: info character, device :: pBuffer(*)
5.4.32. cusparseZbsrsm2_solve
This function performs the solve phase of bsrsm2.
integer(4) function cusparseZbsrsm2_solve(handle, dirA, transA, transX, mb, n, nnzb, alpha, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, x, ldx, y, ldy, policy, pBuffer) type(cusparseHandle) :: handle integer :: dirA, transA, transX, mb, n, nnzb complex(8), device :: alpha ! device or host variable type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*), x(*), y(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: blockDim, policy, ldx, ldy type(cusparseBsrsm2Info) :: info character, device :: pBuffer(*)
5.4.33. cusparseXbsrsm2_zeroPivot
This function returns an error code equal to CUSPARSE_STATUS_ZERO_PIVOT and sets position to j when A(j,j) is either structural zero or numerical zero. Otherwise, position is set to -1.
integer(4) function cusparseXbsrsm2_zeroPivot(handle, info, position) type(cusparseHandle) :: handle type(cusparseBsrsm2Info) :: info integer(4), device :: position ! device or host variable
5.5. CUSPARSE Extra Functions
This section contains interfaces for the extra functions that are used to manipulate sparse matrices.
5.5.1. cusparseXcsrgeamNnz
cusparseXcsrgeamNnz computes the number of nonzero elements which will be produced by CSRGEAM.
integer(4) function cusparseXcsrgeamNnz(handle, m, n, descrA, nnzA, csrRowPtrA, csrColIndA, descrB, nnzB, csrRowPtrB, csrColIndB, descrC, csrRowPtrC, nnzTotalDevHostPtr) type(cusparseHandle) :: handle integer :: m, n, nnzA, nnzB type(cusparseMatDescr) :: descrA, descrB, descrC integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*) integer(4), device :: nnzTotalDevHostPtr ! device or host variable
5.5.2. cusparseScsrgeam
CSRGEAM performs the matrix-matrix operation C := alpha * A + beta * B, alpha and beta are scalars. A, B, and C are m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C}, csrRowPtr{A|B|C}, and csrColInd{A|B|C}. cusparseXcsrgeamNnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseScsrgeam(handle, m, n, alpha, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, beta, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer :: m, n, nnzA, nnzB real(4), device :: alpha, beta ! device or host variable type(cusparseMatDescr):: descrA, descrB, descrC real(4), device :: csrValA(*), csrValB(*), csrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*), csrColIndC(*)
5.5.3. cusparseDcsrgeam
CSRGEAM performs the matrix-matrix operation C := alpha * A + beta * B, alpha and beta are scalars. A, B, and C are m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C}, csrRowPtr{A|B|C}, and csrColInd{A|B|C}. cusparseXcsrgeamNnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseDcsrgeam(handle, m, n, alpha, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, beta, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer :: m, n, nnzA, nnzB real(8), device :: alpha, beta ! device or host variable type(cusparseMatDescr):: descrA, descrB, descrC real(8), device :: csrValA(*), csrValB(*), csrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*), csrColIndC(*)
5.5.4. cusparseCcsrgeam
CSRGEAM performs the matrix-matrix operation C := alpha * A + beta * B, alpha and beta are scalars. A, B, and C are m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C}, csrRowPtr{A|B|C}, and csrColInd{A|B|C}. cusparseXcsrgeamNnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseCcsrgeam(handle, m, n, alpha, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, beta, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer :: m, n, nnzA, nnzB complex(4), device :: alpha, beta ! device or host variable type(cusparseMatDescr):: descrA, descrB, descrC complex(4), device :: csrValA(*), csrValB(*), csrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*), csrColIndC(*)
5.5.5. cusparseZcsrgeam
CSRGEAM performs the matrix-matrix operation C := alpha * A + beta * B, alpha and beta are scalars. A, B, and C are m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C}, csrRowPtr{A|B|C}, and csrColInd{A|B|C}. cusparseXcsrgeamNnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseZcsrgeam(handle, m, n, alpha, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, beta, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer :: m, n, nnzA, nnzB complex(8), device :: alpha, beta ! device or host variable type(cusparseMatDescr):: descrA, descrB, descrC complex(8), device :: csrValA(*), csrValB(*), csrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*), csrColIndC(*)
5.5.6. cusparseXcsrgemmNnz
cusparseXcsrgemmNnz computes the number of nonzero elements which will be produced by CSRGEMM.
integer(4) function cusparseXcsrgemmNnz(handle, transA, transB, m, n, k, descrA, nnzA, csrRowPtrA, csrColIndA, descrB, nnzB, csrRowPtrB, csrColIndB, descrC, csrRowPtrC, nnzTotalDevHostPtr) type(cusparseHandle) :: handle integer :: transA, transB, m, n, k, nnzA, nnzB type(cusparseMatDescr) :: descrA, descrB, descrC integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*) integer(4), device :: nnzTotalDevHostPtr ! device or host variable
5.5.7. cusparseScsrgemm
CSRGEMM performs the matrix-matrix operation C := op( A ) * op( B ), where op( X ) is one of op( X ) = X or op( X ) = X**T, A, B, and C are m x k, k x n, and m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C}, csrRowPtr{A|B|C}, and csrColInd{A|B|C}. cusparseXcsrgemmNnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseScsrgemm(handle, transA, transB, m, n, k, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: transA, transB, m, n, k, nnzA, nnzB type(cusparseMatDescr) :: descrA, descrB, descrC real(4), device :: csrValA(*), csrValB(*), csrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*), csrColIndC(*)
5.5.8. cusparseDcsrgemm
CSRGEMM performs the matrix-matrix operation C := op( A ) * op( B ), where op( X ) is one of op( X ) = X or op( X ) = X**T, A, B, and C are m x k, k x n, and m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C}, csrRowPtr{A|B|C}, and csrColInd{A|B|C}. cusparseXcsrgemmNnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseDcsrgemm(handle, transA, transB, m, n, k, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: transA, transB, m, n, k, nnzA, nnzB type(cusparseMatDescr) :: descrA, descrB, descrC real(8), device :: csrValA(*), csrValB(*), csrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*), csrColIndC(*)
5.5.9. cusparseCcsrgemm
CSRGEMM performs the matrix-matrix operation C := op( A ) * op( B ), where op( X ) is one of op( X ) = X or op( X ) = X**T, A, B, and C are m x k, k x n, and m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C}, csrRowPtr{A|B|C}, and csrColInd{A|B|C}. cusparseXcsrgemmNnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseCcsrgemm(handle, transA, transB, m, n, k, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: transA, transB, m, n, k, nnzA, nnzB type(cusparseMatDescr) :: descrA, descrB, descrC complex(4), device :: csrValA(*), csrValB(*), csrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*), csrColIndC(*)
5.5.10. cusparseZcsrgemm
CSRGEMM performs the matrix-matrix operation C := op( A ) * op( B ), where op( X ) is one of op( X ) = X or op( X ) = X**T, A, B, and C are m x k, k x n, and m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C}, csrRowPtr{A|B|C}, and csrColInd{A|B|C}. cusparseXcsrgemmNnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseZcsrgemm(handle, transA, transB, m, n, k, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: transA, transB, m, n, k, nnzA, nnzB type(cusparseMatDescr) :: descrA, descrB, descrC complex(8), device :: csrValA(*), csrValB(*), csrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrC(*), csrColIndC(*)
5.5.11. cusparseScsrgemm2_bufferSizeExt
This function returns the size of the buffer used in csrgemm2.
integer(4) function cusparseScsrgemm2_bufferSizeExt(handle, m, n, k, alpha, descrA, nnzA, csrRowPtrA, csrColIndA, descrB, nnzB, csrRowPtrB, csrColIndB, beta, descrD, nnzD, csrRowPtrD, csrColIndD, info, pBufferSizeInBytes) type(cusparseHandle) :: handle real(4), device :: alpha, beta ! device or host variable integer :: m, n, k, nnzA, nnzB, nnzD type(cusparseMatDescr) :: descrA, descrB, descrD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*) type(cusparseCsrgemm2Info) :: info integer(8) :: pBufferSizeInBytes
5.5.12. cusparseDcsrgemm2_bufferSizeExt
This function returns the size of the buffer used in csrgemm2.
integer(4) function cusparseDcsrgemm2_bufferSizeExt(handle, m, n, k, alpha, descrA, nnzA, csrRowPtrA, csrColIndA, descrB, nnzB, csrRowPtrB, csrColIndB, beta, descrD, nnzD, csrRowPtrD, csrColIndD, info, pBufferSizeInBytes) type(cusparseHandle) :: handle real(8), device :: alpha, beta ! device or host variable integer :: m, n, k, nnzA, nnzB, nnzD type(cusparseMatDescr) :: descrA, descrB, descrD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*) type(cusparseCsrgemm2Info) :: info integer(8) :: pBufferSizeInBytes
5.5.13. cusparseCcsrgemm2_bufferSizeExt
This function returns the size of the buffer used in csrgemm2.
integer(4) function cusparseCcsrgemm2_bufferSizeExt(handle, m, n, k, alpha, descrA, nnzA, csrRowPtrA, csrColIndA, descrB, nnzB, csrRowPtrB, csrColIndB, beta, descrD, nnzD, csrRowPtrD, csrColIndD, info, pBufferSizeInBytes) type(cusparseHandle) :: handle complex(4), device :: alpha, beta ! device or host variable integer :: m, n, k, nnzA, nnzB, nnzD type(cusparseMatDescr) :: descrA, descrB, descrD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*) type(cusparseCsrgemm2Info) :: info integer(8) :: pBufferSizeInBytes
5.5.14. cusparseZcsrgemm2_bufferSizeExt
This function returns the size of the buffer used in csrgemm2.
integer(4) function cusparseZcsrgemm2_bufferSizeExt(handle, m, n, k, alpha, descrA, nnzA, csrRowPtrA, csrColIndA, descrB, nnzB, csrRowPtrB, csrColIndB, beta, descrD, nnzD, csrRowPtrD, csrColIndD, info, pBufferSizeInBytes) type(cusparseHandle) :: handle complex(8), device :: alpha, beta ! device or host variable integer :: m, n, k, nnzA, nnzB, nnzD type(cusparseMatDescr) :: descrA, descrB, descrD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*) type(cusparseCsrgemm2Info) :: info integer(8) :: pBufferSizeInBytes
5.5.15. cusparseXcsrgemm2Nnz
cusparseXcsrgemm2Nnz computes the number of nonzero elements which will be produced by CSRGEMM2.
integer(4) function cusparseXcsrgemm2Nnz(handle, m, n, k, descrA, nnzA, csrRowPtrA, csrColIndA, descrB, nnzB, csrRowPtrB, csrColIndB, descrD, nnzD, csrRowPtrD, csrColIndD, descrC, csrRowPtrC, nnzTotalDevHostPtr, info, pBuffer) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA, descrB, descrD, descrC type(cusparseCsrgemm2Info) :: info integer(4) :: m, n, k, nnzA, nnzB, nnzD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*), csrRowPtrC(*) integer(c_int) :: nnzTotalDevHostPtr character(c_char), device :: pBuffer(*)
5.5.16. cusparseScsrgemm2
CSRGEMM2 performs the matrix-matrix operation C := alpha * A * B + beta * D alpha and beta are scalars. A, B, and C are m x k, k x n, and m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C|D}, csrRowPtr{A|B|C|D}, and csrColInd{A|B|C|D}. cusparseXcsrgemm2Nnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseScsrgemm2(handle, m, n, k, alpha, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, beta, descrD, nnzD, csrValD, csrRowPtrD, csrColIndD, descrC, csrValC, csrRowPtrC, csrColIndC, info, pBuffer) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA, descrB, descrD, descrC type(cusparseCsrgemm2Info) :: info integer :: m, n, k, nnzA, nnzB, nnzD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*), csrRowPtrC(*), csrColIndC(*) real(4), device :: csrValA(*), csrValB(*), csrValD(*), csrValC(*) real(4), device :: alpha, beta ! device or host variable integer(4), device :: nnzTotalDevHostPtr ! device or host variable character, device :: pBuffer(*)
5.5.17. cusparseDcsrgemm2
CSRGEMM2 performs the matrix-matrix operation C := alpha * A * B + beta * D alpha and beta are scalars. A, B, and C are m x k, k x n, and m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C|D}, csrRowPtr{A|B|C|D}, and csrColInd{A|B|C|D}. cusparseXcsrgemm2Nnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseDcsrgemm2(handle, m, n, k, alpha, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, beta, descrD, nnzD, csrValD, csrRowPtrD, csrColIndD, descrC, csrValC, csrRowPtrC, csrColIndC, info, pBuffer) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA, descrB, descrD, descrC type(cusparseCsrgemm2Info) :: info integer :: m, n, k, nnzA, nnzB, nnzD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*), csrRowPtrC(*), csrColIndC(*) real(8), device :: csrValA(*), csrValB(*), csrValD(*), csrValC(*) real(8), device :: alpha, beta ! device or host variable integer(4), device :: nnzTotalDevHostPtr ! device or host variable character, device :: pBuffer(*)
5.5.18. cusparseCcsrgemm2
CSRGEMM2 performs the matrix-matrix operation C := alpha * A * B + beta * D alpha and beta are scalars. A, B, and C are m x k, k x n, and m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C|D}, csrRowPtr{A|B|C|D}, and csrColInd{A|B|C|D}. cusparseXcsrgemm2Nnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseCcsrgemm2(handle, m, n, k, alpha, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, beta, descrD, nnzD, csrValD, csrRowPtrD, csrColIndD, descrC, csrValC, csrRowPtrC, csrColIndC, info, pBuffer) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA, descrB, descrD, descrC type(cusparseCsrgemm2Info) :: info integer :: m, n, k, nnzA, nnzB, nnzD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*), csrRowPtrC(*), csrColIndC(*) complex(4), device :: csrValA(*), csrValB(*), csrValD(*), csrValC(*) complex(4), device :: alpha, beta ! device or host variable integer(4), device :: nnzTotalDevHostPtr ! device or host variable character, device :: pBuffer(*)
5.5.19. cusparseZcsrgemm2
CSRGEMM2 performs the matrix-matrix operation C := alpha * A * B + beta * D alpha and beta are scalars. A, B, and C are m x k, k x n, and m x n sparse matrices that are defined in CSR storage format by the three arrays csrVal{A|B|C|D}, csrRowPtr{A|B|C|D}, and csrColInd{A|B|C|D}. cusparseXcsrgemm2Nnz should be used to determine csrRowPtrC and the number of nonzero elements in the result.
integer(4) function cusparseZcsrgemm2(handle, m, n, k, alpha, descrA, nnzA, csrValA, csrRowPtrA, csrColIndA, descrB, nnzB, csrValB, csrRowPtrB, csrColIndB, beta, descrD, nnzD, csrValD, csrRowPtrD, csrColIndD, descrC, csrValC, csrRowPtrC, csrColIndC, info, pBuffer) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA, descrB, descrD, descrC type(cusparseCsrgemm2Info) :: info integer :: m, n, k, nnzA, nnzB, nnzD integer(4), device :: csrRowPtrA(*), csrColIndA(*), csrRowPtrB(*), csrColIndB(*), csrRowPtrD(*), csrColIndD(*), csrRowPtrC(*), csrColIndC(*) complex(8), device :: csrValA(*), csrValB(*), csrValD(*), csrValC(*) complex(8), device :: alpha, beta ! device or host variable integer(4), device :: nnzTotalDevHostPtr ! device or host variable character, device :: pBuffer(*)
5.6. CUSPARSE Preconditioning Functions
This section contains interfaces for the preconditioning functions that are used in processing sparse matrices.
5.6.1. cusparseScsric0
CSRIC0 computes the incomplete-Cholesky factorization with zero fill-in and no pivoting. A is an m x n Hermitian/symmetric positive definite sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA.
integer(4) function cusparseScsric0(handle, trans, m, descrA, csrValM, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: trans, m type(cusparseMatDescr) :: descrA real(4), device :: csrValM(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.6.2. cusparseDcsric0
CSRIC0 computes the incomplete-Cholesky factorization with zero fill-in and no pivoting. A is an m x n Hermitian/symmetric positive definite sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA.
integer(4) function cusparseDcsric0(handle, trans, m, descrA, csrValM, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: trans, m type(cusparseMatDescr) :: descrA real(8), device :: csrValM(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.6.3. cusparseCcsric0
CSRIC0 computes the incomplete-Cholesky factorization with zero fill-in and no pivoting. A is an m x n Hermitian/symmetric positive definite sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA.
integer(4) function cusparseCcsric0(handle, trans, m, descrA, csrValM, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: trans, m type(cusparseMatDescr) :: descrA complex(4), device :: csrValM(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.6.4. cusparseZcsric0
CSRIC0 computes the incomplete-Cholesky factorization with zero fill-in and no pivoting. A is an m x n Hermitian/symmetric positive definite sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA.
integer(4) function cusparseZcsric0(handle, trans, m, descrA, csrValM, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: trans, m type(cusparseMatDescr) :: descrA complex(8), device :: csrValM(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.6.5. cusparseScsrilu0
CSRILU0 computes the incomplete-LU factorization with zero fill-in and no pivoting. A is an m x n sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA.
integer(4) function cusparseScsrilu0(handle, trans, m, descrA, csrValM, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: trans, m type(cusparseMatDescr) :: descrA real(4), device :: csrValM(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.6.6. cusparseDcsrilu0
CSRILU0 computes the incomplete-LU factorization with zero fill-in and no pivoting. A is an m x n sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA.
integer(4) function cusparseDcsrilu0(handle, trans, m, descrA, csrValM, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: trans, m type(cusparseMatDescr) :: descrA real(8), device :: csrValM(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.6.7. cusparseCcsrilu0
CSRILU0 computes the incomplete-LU factorization with zero fill-in and no pivoting. A is an m x n sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA.
integer(4) function cusparseCcsrilu0(handle, trans, m, descrA, csrValM, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: trans, m type(cusparseMatDescr) :: descrA complex(4), device :: csrValM(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.6.8. cusparseZcsrilu0
CSRILU0 computes the incomplete-LU factorization with zero fill-in and no pivoting. A is an m x n sparse matrix that is defined in CSR storage format by the three arrays csrValA, csrRowPtrA, and csrColIndA.
integer(4) function cusparseZcsrilu0(handle, trans, m, descrA, csrValM, csrRowPtrA, csrColIndA, info) type(cusparseHandle) :: handle integer(4) :: trans, m type(cusparseMatDescr) :: descrA complex(8), device :: csrValM(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseSolveAnalysisInfo) :: info
5.6.9. cusparseSgtsv
GTSV computes the solution of a tridiagonal linear system with multiple right hand sides: A * Y = a * x The coefficient matrix A of this tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit.
integer(4) function cusparseSgtsv(handle, m, n, dl, d, du, B, ldb) type(cusparseHandle) :: handle integer(4) :: m, n, ldb real(4), device :: dl(*), d(*), du(*), B(*)
5.6.10. cusparseDgtsv
GTSV computes the solution of a tridiagonal linear system with multiple right hand sides: A * Y = a * x The coefficient matrix A of this tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit.
integer(4) function cusparseDgtsv(handle, m, n, dl, d, du, B, ldb) type(cusparseHandle) :: handle integer(4) :: m, n, ldb real(8), device :: dl(*), d(*), du(*), B(*)
5.6.11. cusparseCgtsv
GTSV computes the solution of a tridiagonal linear system with multiple right hand sides: A * Y = a * x The coefficient matrix A of this tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit.
integer(4) function cusparseCgtsv(handle, m, n, dl, d, du, B, ldb) type(cusparseHandle) :: handle integer(4) :: m, n, ldb complex(4), device :: dl(*), d(*), du(*), B(*)
5.6.12. cusparseZgtsv
GTSV computes the solution of a tridiagonal linear system with multiple right hand sides: A * Y = a * x The coefficient matrix A of this tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit.
integer(4) function cusparseZgtsv(handle, m, n, dl, d, du, B, ldb) type(cusparseHandle) :: handle integer(4) :: m, n, ldb complex(8), device :: dl(*), d(*), du(*), B(*)
5.6.13. cusparseSgtsv_nopivot
GTSV computes the solution of a tridiagonal linear system with multiple right hand sides: A * Y = a * x The coefficient matrix A of this tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit. This function does not perform pivoting.
integer(4) function cusparseSgtsv_nopivot(handle, m, n, dl, d, du, B, ldb) type(cusparseHandle) :: handle integer(4) :: m, n, ldb real(4), device :: dl(*), d(*), du(*), B(*)
5.6.14. cusparseDgtsv_nopivot
GTSV computes the solution of a tridiagonal linear system with multiple right hand sides: A * Y = a * x The coefficient matrix A of this tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit. This function does not perform pivoting.
integer(4) function cusparseDgtsv_nopivot(handle, m, n, dl, d, du, B, ldb) type(cusparseHandle) :: handle integer(4) :: m, n, ldb real(8), device :: dl(*), d(*), du(*), B(*)
5.6.15. cusparseCgtsv_nopivot
GTSV computes the solution of a tridiagonal linear system with multiple right hand sides: A * Y = a * x The coefficient matrix A of this tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit. This function does not perform pivoting.
integer(4) function cusparseCgtsv_nopivot(handle, m, n, dl, d, du, B, ldb) type(cusparseHandle) :: handle integer(4) :: m, n, ldb complex(4), device :: dl(*), d(*), du(*), B(*)
5.6.16. cusparseZgtsv_nopivot
GTSV computes the solution of a tridiagonal linear system with multiple right hand sides: A * Y = a * x The coefficient matrix A of this tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit. This function does not perform pivoting.
integer(4) function cusparseZgtsv_nopivot(handle, m, n, dl, d, du, B, ldb) type(cusparseHandle) :: handle integer(4) :: m, n, ldb complex(8), device :: dl(*), d(*), du(*), B(*)
5.6.17. cusparseSgtsvStridedBatch
GTSVStridedBatch computes the solution of multiple tridiagonal linear systems with multiple right hand sides: A * Y = a * x The coefficient matrix A of each tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit.
integer(4) function cusparseSgtsvStridedBatch(handle, m, dl, d, du, x, batchCount, batchStride) type(cusparseHandle) :: handle integer(4) :: m, n, batchCount, batchStride real(4), device :: dl(*), d(*), du(*), x(*)
5.6.18. cusparseDgtsvStridedBatch
GTSVStridedBatch computes the solution of multiple tridiagonal linear systems with multiple right hand sides: A * Y = a * x The coefficient matrix A of each tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit.
integer(4) function cusparseDgtsvStridedBatch(handle, m, dl, d, du, x, batchCount, batchStride) type(cusparseHandle) :: handle integer(4) :: m, n, batchCount, batchStride real(8), device :: dl(*), d(*), du(*), x(*)
5.6.19. cusparseCgtsvStridedBatch
GTSVStridedBatch computes the solution of multiple tridiagonal linear systems with multiple right hand sides: A * Y = a * x The coefficient matrix A of each tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit.
integer(4) function cusparseCgtsvStridedBatch(handle, m, dl, d, du, x, batchCount, batchStride) type(cusparseHandle) :: handle integer(4) :: m, n, batchCount, batchStride complex(4), device :: dl(*), d(*), du(*), x(*)
5.6.20. cusparseZgtsvStridedBatch
GTSVStridedBatch computes the solution of multiple tridiagonal linear systems with multiple right hand sides: A * Y = a * x The coefficient matrix A of each tri-diagonal linear system is defined with three vectors corresponding to its lower (dl), main (d), and upper (du) matrix diagonals; the right-hand sides are stored in the dense matrix X. The solution Y overwrites the righthand-side matrix X on exit.
integer(4) function cusparseZgtsvStridedBatch(handle, m, dl, d, du, x, batchCount, batchStride) type(cusparseHandle) :: handle integer(4) :: m, n, batchCount, batchStride complex(8), device :: dl(*), d(*), du(*), x(*)
5.6.21. cusparseScsric02_bufferSize
This function returns the size of the buffer used in csric02.
integer(4) function cusparseScsric02_bufferSize(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.22. cusparseDcsric02_bufferSize
This function returns the size of the buffer used in csric02.
integer(4) function cusparseDcsric02_bufferSize(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.23. cusparseCcsric02_bufferSize
This function returns the size of the buffer used in csric02.
integer(4) function cusparseCcsric02_bufferSize(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.24. cusparseZcsric02_bufferSize
This function returns the size of the buffer used in csric02.
integer(4) function cusparseZcsric02_bufferSize(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.25. cusparseScsric02_analysis
This function performs the analysis phase of csric02.
integer(4) function cusparseScsric02_analysis(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.26. cusparseDcsric02_analysis
This function performs the analysis phase of csric02.
integer(4) function cusparseDcsric02_analysis(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.27. cusparseCcsric02_analysis
This function performs the analysis phase of csric02.
integer(4) function cusparseCcsric02_analysis(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.28. cusparseZcsric02_analysis
This function performs the analysis phase of csric02.
integer(4) function cusparseZcsric02_analysis(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.29. cusparseScsric02
CSRIC02 performs the solve phase of computing the incomplete-Cholesky factorization with zero fill-in and no pivoting.
integer(4) function cusparseScsric02(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.30. cusparseDcsric02
CSRIC02 performs the solve phase of computing the incomplete-Cholesky factorization with zero fill-in and no pivoting.
integer(4) function cusparseDcsric02(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.31. cusparseCcsric02
CSRIC02 performs the solve phase of computing the incomplete-Cholesky factorization with zero fill-in and no pivoting.
integer(4) function cusparseCcsric02(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.32. cusparseZcsric02
CSRIC02 performs the solve phase of computing the incomplete-Cholesky factorization with zero fill-in and no pivoting.
integer(4) function cusparseZcsric02(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.33. cusparseXcsric02_zeroPivot
This function returns an error code equal to CUSPARSE_STATUS_ZERO_PIVOT and sets position to j when A(j,j) is either structural zero or numerical zero. Otherwise, position is set to -1.
integer(4) function cusparseXcsric02_zeroPivot(handle, info, position) type(cusparseHandle) :: handle type(cusparseCsric02Info) :: info integer(4), device :: position ! device or host variable
5.6.34. cusparseScsrilu02_numericBoost
This function boosts the value to replace a numerical value in incomplete LU factorization, based on the tol input argument.
integer(4) function cusparseScsrilu02_numericBoost(handle, info, enable_boost, tol, boost_val) type(cusparseHandle) :: handle type(cusparseCsrilu02Info) :: info integer :: enable_boost real(8), device :: tol ! device or host variable real(4), device :: boost_val ! device or host variable
5.6.35. cusparseDcsrilu02_numericBoost
This function boosts the value to replace a numerical value in incomplete LU factorization, based on the tol input argument.
integer(4) function cusparseDcsrilu02_numericBoost(handle, info, enable_boost, tol, boost_val) type(cusparseHandle) :: handle type(cusparseCsrilu02Info) :: info integer :: enable_boost real(8), device :: tol ! device or host variable real(8), device :: boost_val ! device or host variable
5.6.36. cusparseCcsrilu02_numericBoost
This function boosts the value to replace a numerical value in incomplete LU factorization, based on the tol input argument.
integer(4) function cusparseCcsrilu02_numericBoost(handle, info, enable_boost, tol, boost_val) type(cusparseHandle) :: handle type(cusparseCsrilu02Info) :: info integer :: enable_boost real(8), device :: tol ! device or host variable complex(4), device :: boost_val ! device or host variable
5.6.37. cusparseZcsrilu02_numericBoost
This function boosts the value to replace a numerical value in incomplete LU factorization, based on the tol input argument.
integer(4) function cusparseZcsrilu02_numericBoost(handle, info, enable_boost, tol, boost_val) type(cusparseHandle) :: handle type(cusparseCsrilu02Info) :: info integer :: enable_boost real(8), device :: tol ! device or host variable complex(8), device :: boost_val ! device or host variable
5.6.38. cusparseScsrilu02_bufferSize
This function returns the size of the buffer used in csrilu02.
integer(4) function cusparseScsrilu02_bufferSize(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.39. cusparseDcsrilu02_bufferSize
This function returns the size of the buffer used in csrilu02.
integer(4) function cusparseDcsrilu02_bufferSize(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.40. cusparseCcsrilu02_bufferSize
This function returns the size of the buffer used in csrilu02.
integer(4) function cusparseCcsrilu02_bufferSize(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.41. cusparseZcsrilu02_bufferSize
This function returns the size of the buffer used in csrilu02.
integer(4) function cusparseZcsrilu02_bufferSize(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.42. cusparseScsrilu02_analysis
This function performs the analysis phase of csrilu02.
integer(4) function cusparseScsrilu02_analysis(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.43. cusparseDcsrilu02_analysis
This function performs the analysis phase of csrilu02.
integer(4) function cusparseDcsrilu02_analysis(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.44. cusparseCcsrilu02_analysis
This function performs the analysis phase of csrilu02.
integer(4) function cusparseCcsrilu02_analysis(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.45. cusparseZcsrilu02_analysis
This function performs the analysis phase of csrilu02.
integer(4) function cusparseZcsrilu02_analysis(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.46. cusparseScsrilu02
CSRILU02 performs the solve phase of the incomplete-LU factorization with zero fill-in and no pivoting. A is an m x m sparse matrix that is defined in CSR storage format by the three arrays csrValA_valM, csrRowPtrA, and csrColIndA.
integer(4) function cusparseScsrilu02(handle, m, nnz, descrA, type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.47. cusparseDcsrilu02
CSRILU02 performs the solve phase of the incomplete-LU factorization with zero fill-in and no pivoting. A is an m x m sparse matrix that is defined in CSR storage format by the three arrays csrValA_valM, csrRowPtrA, and csrColIndA.
integer(4) function cusparseDcsrilu02(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.48. cusparseCcsrilu02
CSRILU02 performs the solve phase of the incomplete-LU factorization with zero fill-in and no pivoting. A is an m x m sparse matrix that is defined in CSR storage format by the three arrays csrValA_valM, csrRowPtrA, and csrColIndA.
integer(4) function cusparseCcsrilu02(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.49. cusparseZcsrilu02
CSRILU02 performs the solve phase of the incomplete-LU factorization with zero fill-in and no pivoting. A is an m x m sparse matrix that is defined in CSR storage format by the three arrays csrValA_valM, csrRowPtrA, and csrColIndA.
integer(4) function cusparseZcsrilu02(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseCsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.50. cusparseXcsrilu02_zeroPivot
This function returns an error code equal to CUSPARSE_STATUS_ZERO_PIVOT and sets position to j when A(j,j) is either structural zero or numerical zero. Otherwise, position is set to -1.
integer(4) function cusparseXcsrilu02_zeroPivot(handle, info, position) type(cusparseHandle) :: handle type(cusparseCsrilu02Info) :: info integer(4), device :: position ! device or host variable
5.6.51. cusparseSbsric02_bufferSize
This function returns the size of the buffer used in bsric02.
integer(4) function cusparseSbsric02_bufferSize(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.52. cusparseDbsric02_bufferSize
This function returns the size of the buffer used in bsric02.
integer(4) function cusparseDbsric02_bufferSize(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.53. cusparseCbsric02_bufferSize
This function returns the size of the buffer used in bsric02.
integer(4) function cusparseCbsric02_bufferSize(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.54. cusparseZbsric02_bufferSize
This function returns the size of the buffer used in bsric02.
integer(4) function cusparseZbsric02_bufferSize(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.55. cusparseSbsric02_analysis
This function performs the analysis phase of bsric02.
integer(4) function cusparseSbsric02_analysis(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.56. cusparseDbsric02_analysis
This function performs the analysis phase of bsric02.
integer(4) function cusparseDbsric02_analysis(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.57. cusparseCbsric02_analysis
This function performs the analysis phase of bsric02.
integer(4) function cusparseCbsric02_analysis(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.58. cusparseZbsric02_analysis
This function performs the analysis phase of bsric02.
integer(4) function cusparseZbsric02_analysis(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.59. cusparseSbsric02
BSRIC02 performs the solve phase of the incomplete-Cholesky factorization with zero fill-in and no pivoting. A is an (mb*blockDim) x (mb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA.
integer(4) function cusparseSbsric02(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.60. cusparseDbsric02
BSRIC02 performs the solve phase of the incomplete-Cholesky factorization with zero fill-in and no pivoting. A is an (mb*blockDim) x (mb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA.
integer(4) function cusparseDbsric02(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.61. cusparseCbsric02
BSRIC02 performs the solve phase of the incomplete-Cholesky factorization with zero fill-in and no pivoting. A is an (mb*blockDim) x (mb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA.
integer(4) function cusparseCbsric02(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.62. cusparseZbsric02
BSRIC02 performs the solve phase of the incomplete-Cholesky factorization with zero fill-in and no pivoting. A is an (mb*blockDim) x (mb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA.
integer(4) function cusparseZbsric02(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsric02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.63. cusparseXbsric02_zeroPivot
This function returns an error code equal to CUSPARSE_STATUS_ZERO_PIVOT and sets position to j when A(j,j) is either structural zero or numerical zero. Otherwise, position is set to -1.
integer(4) function cusparseXbsric02_zeroPivot(handle, info, position) type(cusparseHandle) :: handle type(cusparseBsric02Info) :: info integer(4), device :: position ! device or host variable
5.6.64. cusparseSbsrilu02_numericBoost
This function boosts the value to replace a numerical value in incomplete LU factorization, based on the tol input argument.
integer(4) function cusparseSbsrilu02_numericBoost(handle, info, enable_boost, tol, boost_val) type(cusparseHandle) :: handle type(cusparseBsrilu02Info) :: info integer :: enable_boost real(8), device :: tol ! device or host variable real(4), device :: boost_val ! device or host variable
5.6.65. cusparseDbsrilu02_numericBoost
This function boosts the value to replace a numerical value in incomplete LU factorization, based on the tol input argument.
integer(4) function cusparseDbsrilu02_numericBoost(handle, info, enable_boost, tol, boost_val) type(cusparseHandle) :: handle type(cusparseBsrilu02Info) :: info integer :: enable_boost real(8), device :: tol ! device or host variable real(8), device :: boost_val ! device or host variable
5.6.66. cusparseCbsrilu02_numericBoost
This function boosts the value to replace a numerical value in incomplete LU factorization, based on the tol input argument.
integer(4) function cusparseCbsrilu02_numericBoost(handle, info, enable_boost, tol, boost_val) type(cusparseHandle) :: handle type(cusparseBsrilu02Info) :: info integer :: enable_boost real(8), device :: tol ! device or host variable complex(4), device :: boost_val ! device or host variable
5.6.67. cusparseZbsrilu02_numericBoost
This function boosts the value to replace a numerical value in incomplete LU factorization, based on the tol input argument.
integer(4) function cusparseZbsrilu02_numericBoost(handle, info, enable_boost, tol, boost_val) type(cusparseHandle) :: handle type(cusparseBsrilu02Info) :: info integer :: enable_boost real(8), device :: tol ! device or host variable complex(8), device :: boost_val ! device or host variable
5.6.68. cusparseSbsrilu02_bufferSize
This function returns the size of the buffer used in bsrilu02.
integer(4) function cusparseSbsrilu02_bufferSize(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.69. cusparseDbsrilu02_bufferSize
This function returns the size of the buffer used in bsrilu02.
integer(4) function cusparseDbsrilu02_bufferSize(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.70. cusparseCbsrilu02_bufferSize
This function returns the size of the buffer used in bsrilu02.
integer(4) function cusparseCbsrilu02_bufferSize(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.71. cusparseZbsrilu02_bufferSize
This function returns the size of the buffer used in bsrilu02.
integer(4) function cusparseZbsrilu02_bufferSize(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: pBufferSize ! integer(8) also accepted
5.6.72. cusparseSbsrilu02_analysis
This function performs the analysis phase of bsrilu02.
integer(4) function cusparseSbsrilu02_analysis(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.73. cusparseDbsrilu02_analysis
This function performs the analysis phase of bsrilu02.
integer(4) function cusparseDbsrilu02_analysis(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.74. cusparseCbsrilu02_analysis
This function performs the analysis phase of bsrilu02.
integer(4) function cusparseCbsrilu02_analysis(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.75. cusparseZbsrilu02_analysis
This function performs the analysis phase of bsrilu02.
integer(4) function cusparseZbsrilu02_analysis(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.76. cusparseSbsrilu02
BSRILU02 performs the solve phase of the incomplete-LU factorization with zero fill-in and no pivoting. A is an (mb*blockDim) x (mb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA.
integer(4) function cusparseSbsrilu02(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.77. cusparseDbsrilu02
BSRILU02 performs the solve phase of the incomplete-LU factorization with zero fill-in and no pivoting. A is an (mb*blockDim) x (mb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA.
integer(4) function cusparseDbsrilu02(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.78. cusparseCbsrilu02
BSRILU02 performs the solve phase of the incomplete-LU factorization with zero fill-in and no pivoting. A is an (mb*blockDim) x (mb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA.
integer(4) function cusparseCbsrilu02(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.79. cusparseZbsrilu02
BSRILU02 performs the solve phase of the incomplete-LU factorization with zero fill-in and no pivoting. A is an (mb*blockDim) x (mb*blockDim) sparse matrix that is defined in BSR storage format by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA.
integer(4) function cusparseZbsrilu02(handle, dirA, mb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, info, policy, pBuffer) type(cusparseHandle) :: handle integer(4) :: dirA integer(4) :: mb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: blockDim type(cusparseBsrilu02Info) :: info integer(4) :: policy character(c_char), device :: pBuffer(*)
5.6.80. cusparseXbsrilu02_zeroPivot
This function returns an error code equal to CUSPARSE_STATUS_ZERO_PIVOT and sets position to j when A(j,j) is either structural zero or numerical zero. Otherwise, position is set to -1.
integer(4) function cusparseXbsrilu02_zeroPivot(handle, info, position) type(cusparseHandle) :: handle type(cusparseBsrilu02Info) :: info integer(4), device :: position ! device or host variable
5.7. CUSPARSE Reordering Functions
This section contains interfaces for the reordering functions that are used to manipulate sparse matrices.
5.7.1. cusparseScsrColor
This function performs the coloring of the adjacency graph associated with the matrix A stored in CSR format.
integer(4) function cusparseScsrColor(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, fractionToColor, ncolors, coloring, reordering, info) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseColorInfo) :: info integer :: m, nnz real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), coloring(*), reordering(*) real(4), device :: fractionToColor ! device or host variable integer(4), device :: ncolors ! device or host variable
5.7.2. cusparseDcsrColor
This function performs the coloring of the adjacency graph associated with the matrix A stored in CSR format.
integer(4) function cusparseDcsrColor(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, fractionToColor, ncolors, coloring, reordering, info) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseColorInfo) :: info integer :: m, nnz real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), coloring(*), reordering(*) real(8), device :: fractionToColor ! device or host variable integer(4), device :: ncolors ! device or host variable
5.7.3. cusparseCcsrColor
This function performs the coloring of the adjacency graph associated with the matrix A stored in CSR format.
integer(4) function cusparseCcsrColor(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, fractionToColor, ncolors, coloring, reordering, info) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseColorInfo) :: info integer :: m, nnz complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), coloring(*), reordering(*) real(4), device :: fractionToColor ! device or host variable integer(4), device :: ncolors ! device or host variable
5.7.4. cusparseZcsrColor
This function performs the coloring of the adjacency graph associated with the matrix A stored in CSR format.
integer(4) function cusparseZcsrColor(handle, m, nnz, descrA, csrValA, csrRowPtrA, csrColIndA, fractionToColor, ncolors, coloring, reordering, info) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseColorInfo) :: info integer :: m, nnz complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), coloring(*), reordering(*) real(8), device :: fractionToColor ! device or host variable integer(4), device :: ncolors ! device or host variable
5.8. CUSPARSE Format Conversion Functions
This section contains interfaces for the conversion functions that are used to switch between different sparse and dense matrix storage formats.
5.8.1. cusparseSbsr2csr
This function converts a sparse matrix in BSR format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in CSR format that is defined by the arrays csrValC, csrRowPtrC, and csrColIndC.
integer(4) function cusparseSbsr2csr(handle, dirA, nm, nb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: dirA, mb, nb, blockDim type(cusparseMatDescr) :: descrA, descrC real(4), device :: bsrValA(*), csrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*), csrRowPtrC(*), csrColIndC(*)
5.8.2. cusparseDbsr2csr
This function converts a sparse matrix in BSR format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in CSR format that is defined by the arrays csrValC, csrRowPtrC, and csrColIndC.
integer(4) function cusparseDbsr2csr(handle, dirA, nm, nb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: dirA, mb, nb, blockDim type(cusparseMatDescr) :: descrA, descrC real(8), device :: bsrValA(*), csrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*), csrRowPtrC(*), csrColIndC(*)
5.8.3. cusparseCbsr2csr
This function converts a sparse matrix in BSR format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in CSR format that is defined by the arrays csrValC, csrRowPtrC, and csrColIndC.
integer(4) function cusparseCbsr2csr(handle, dirA, nm, nb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: dirA, mb, nb, blockDim type(cusparseMatDescr) :: descrA, descrC complex(4), device :: bsrValA(*), csrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*), csrRowPtrC(*), csrColIndC(*)
5.8.4. cusparseZbsr2csr
This function converts a sparse matrix in BSR format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in CSR format that is defined by the arrays csrValC, csrRowPtrC, and csrColIndC.
integer(4) function cusparseZbsr2csr(handle, dirA, nm, nb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, blockDim, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: dirA, mb, nb, blockDim type(cusparseMatDescr) :: descrA, descrC complex(8), device :: bsrValA(*), csrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*), csrRowPtrC(*), csrColIndC(*)
5.8.5. cusparseXcoo2csr
This function converts the array containing the uncompressed row indices (corresponding to COO format) into an array of compressed row pointers (corresponding to CSR format).
integer(4) function cusparseXcoo2csr(handle, cooRowInd, nnz, m, csrRowPtr, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz, m, idxBase integer(4), device :: cooRowInd(*), csrRowPtr(*)
5.8.6. cusparseScsc2dense
This function converts the sparse matrix in CSC format that is defined by the three arrays cscValA, cscColPtrA, and cscRowIndA into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseScsc2dense(handle, m, n, descrA, cscValA, cscRowIndA, cscColPtrA, A, lda) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA real(4), device :: cscValA(*), A(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*)
5.8.7. cusparseDcsc2dense
This function converts the sparse matrix in CSC format that is defined by the three arrays cscValA, cscColPtrA, and cscRowIndA into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseDcsc2dense(handle, m, n, descrA, cscValA, cscRowIndA, cscColPtrA, A, lda) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA real(8), device :: cscValA(*), A(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*)
5.8.8. cusparseCcsc2dense
This function converts the sparse matrix in CSC format that is defined by the three arrays cscValA, cscColPtrA, and cscRowIndA into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseCcsc2dense(handle, m, n, descrA, cscValA, cscRowIndA, cscColPtrA, A, lda) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA complex(4), device :: cscValA(*), A(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*)
5.8.9. cusparseZcsc2dense
This function converts the sparse matrix in CSC format that is defined by the three arrays cscValA, cscColPtrA, and cscRowIndA into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseZcsc2dense(handle, m, n, descrA, cscValA, cscRowIndA, cscColPtrA, A, lda) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA complex(8), device :: cscValA(*), A(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*)
5.8.10. cusparseScsc2hyb
This function converts the sparse matrix in CSC format that is defined by the three arrays cscValA, cscColPtrA, and cscRowIndA into the sparse matrix A in HYB format.
integer(4) function cusparseScsc2hyb(handle, m, n, descrA, cscValA, cscRowIndA, cscColPtrA, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, userEllWidth, partitionType type(cusparseMatDescr) :: descrA real(4), device :: cscValA(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*) type(cusparseHybMat) :: hybA
5.8.11. cusparseDcsc2hyb
This function converts the sparse matrix in CSC format that is defined by the three arrays cscValA, cscColPtrA, and cscRowIndA into the sparse matrix A in HYB format.
integer(4) function cusparseDcsc2hyb(handle, m, n, descrA, cscValA, cscRowIndA, cscColPtrA, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, userEllWidth, partitionType type(cusparseMatDescr) :: descrA real(8), device :: cscValA(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*) type(cusparseHybMat) :: hybA
5.8.12. cusparseCcsc2hyb
This function converts the sparse matrix in CSC format that is defined by the three arrays cscValA, cscColPtrA, and cscRowIndA into the sparse matrix A in HYB format.
integer(4) function cusparseCcsc2hyb(handle, m, n, descrA, cscValA, cscRowIndA, cscColPtrA, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, userEllWidth, partitionType type(cusparseMatDescr) :: descrA complex(4), device :: cscValA(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*) type(cusparseHybMat) :: hybA
5.8.13. cusparseZcsc2hyb
This function converts the sparse matrix in CSC format that is defined by the three arrays cscValA, cscColPtrA, and cscRowIndA into the sparse matrix A in HYB format.
integer(4) function cusparseZcsc2hyb(handle, m, n, descrA, cscValA, cscRowIndA, cscColPtrA, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, userEllWidth, partitionType type(cusparseMatDescr) :: descrA complex(8), device :: cscValA(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*) type(cusparseHybMat) :: hybA
5.8.14. cusparseXcsr2bsrNnz
cusparseXcsrgeamNnz computes the number of nonzero elements which will be produced by CSRGEAM.
integer(4) function cusparseXcsr2bsrNnz(handle, dirA, m, n, descrA, csrRowPtrA, csrColIndA, blockDim, descrC, bsrRowPtrC, nnzTotalDevHostPtr) type(cusparseHandle) :: handle integer :: dirA, m, n, blockdim type(cusparseMatDescr) :: descrA, descrC integer(4), device :: csrRowPtrA(*), csrColIndA(*), bsrRowPtrC(*) integer(4), device :: nnzTotalDevHostPtr ! device or host variable
5.8.15. cusparseScsr2bsr
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrValA, csrRowPtrA, and csrColIndA into a sparse matrix in BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseScsr2bsr(handle, dirA, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, blockDim, descrC, bsrValC, bsrRowPtrC, bsrColIndC) type(cusparseHandle) :: handle integer(4) :: dirA, m, n, blockdim type(cusparseMatDescr) :: descrA, descrC real(4), device :: csrValA(*), bsrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), bsrRowPtrC(*), bsrColIndC(*)
5.8.16. cusparseDcsr2bsr
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrValA, csrRowPtrA, and csrColIndA into a sparse matrix in BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseDcsr2bsr(handle, dirA, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, blockDim, descrC, bsrValC, bsrRowPtrC, bsrColIndC) type(cusparseHandle) :: handle integer(4) :: dirA, m, n, blockdim type(cusparseMatDescr) :: descrA, descrC real(8), device :: csrValA(*), bsrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), bsrRowPtrC(*), bsrColIndC(*)
5.8.17. cusparseCcsr2bsr
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrValA, csrRowPtrA, and csrColIndA into a sparse matrix in BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseCcsr2bsr(handle, dirA, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, blockDim, descrC, bsrValC, bsrRowPtrC, bsrColIndC) type(cusparseHandle) :: handle integer(4) :: dirA, m, n, blockdim type(cusparseMatDescr) :: descrA, descrC complex(4), device :: csrValA(*), bsrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), bsrRowPtrC(*), bsrColIndC(*)
5.8.18. cusparseZcsr2bsr
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrValA, csrRowPtrA, and csrColIndA into a sparse matrix in BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseZcsr2bsr(handle, dirA, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, blockDim, descrC, bsrValC, bsrRowPtrC, bsrColIndC) type(cusparseHandle) :: handle integer(4) :: dirA, m, n, blockdim type(cusparseMatDescr) :: descrA, descrC complex(8), device :: csrValA(*), bsrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*), bsrRowPtrC(*), bsrColIndC(*)
5.8.19. cusparseXcsr2coo
This function converts the array containing the compressed row pointers (corresponding to CSR format) into an array of uncompressed row indices (corresponding to COO format).
integer(4) function cusparseXcsr2coo(handle, csrRowPtr, nnz, m, cooRowInd, idxBase) type(cusparseHandle) :: handle integer(4) :: nnz, m, idxBase integer(4), device :: csrRowPtr(*), cooRowInd(*)
5.8.20. cusparseScsr2csc
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrVal, csrRowPtr, and csrColInd into a sparse matrix in CSC format that is defined by arrays cscVal, cscRowInd, and cscColPtr.
integer(4) function cusparseScsr2csc(handle, m, n, nnz, csrVal, csrRowPtr, csrColInd, cscVal, cscRowInd, cscColPtr, copyValues, idxBase) type(cusparseHandle) :: handle integer(4) :: m, n, nnz, copyValues, idxBase real(4), device :: csrVal(*), cscVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*), cscRowInd(*), cscColPtr(*)
5.8.21. cusparseDcsr2csc
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrVal, csrRowPtr, and csrColInd into a sparse matrix in CSC format that is defined by arrays cscVal, cscRowInd, and cscColPtr.
integer(4) function cusparseDcsr2csc(handle, m, n, nnz, csrVal, csrRowPtr, csrColInd, cscVal, cscRowInd, cscColPtr, copyValues, idxBase) type(cusparseHandle) :: handle integer(4) :: m, n, nnz, copyValues, idxBase real(8), device :: csrVal(*), cscVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*), cscRowInd(*), cscColPtr(*)
5.8.22. cusparseCcsr2csc
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrVal, csrRowPtr, and csrColInd into a sparse matrix in CSC format that is defined by arrays cscVal, cscRowInd, and cscColPtr.
integer(4) function cusparseCcsr2csc(handle, m, n, nnz, csrVal, csrRowPtr, csrColInd, cscVal, cscRowInd, cscColPtr, copyValues, idxBase) type(cusparseHandle) :: handle integer(4) :: m, n, nnz, copyValues, idxBase complex(4), device :: csrVal(*), cscVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*), cscRowInd(*), cscColPtr(*)
5.8.23. cusparseZcsr2csc
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrVal, csrRowPtr, and csrColInd into a sparse matrix in CSC format that is defined by arrays cscVal, cscRowInd, and cscColPtr.
integer(4) function cusparseZcsr2csc(handle, m, n, nnz, csrVal, csrRowPtr, csrColInd, cscVal, cscRowInd, cscColPtr, copyValues, idxBase) type(cusparseHandle) :: handle integer(4) :: m, n, nnz, copyValues, idxBase complex(8), device :: csrVal(*), cscVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*), cscRowInd(*), cscColPtr(*)
5.8.24. cusparseScsr2dense
This function converts the sparse matrix in CSR format that is defined by the three arrays cscValA, cscRowPtrA, and cscColIndA into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseScsr2dense(handle, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, A, lda) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*), A(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.25. cusparseDcsr2dense
This function converts the sparse matrix in CSR format that is defined by the three arrays cscValA, cscRowPtrA, and cscColIndA into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseDcsr2dense(handle, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, A, lda) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*), A(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.26. cusparseCcsr2dense
This function converts the sparse matrix in CSR format that is defined by the three arrays cscValA, cscRowPtrA, and cscColIndA into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseCcsr2dense(handle, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, A, lda) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*), A(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.27. cusparseZcsr2dense
This function converts the sparse matrix in CSR format that is defined by the three arrays cscValA, cscRowPtrA, and cscColIndA into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseZcsr2dense(handle, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, A, lda) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*), A(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.28. cusparseScsr2hyb
This function converts the sparse matrix in CSR format that is defined by the three arrays cscValA, cscRowPtrA, and cscColIndA into a sparse matrix in HYB format.
integer(4) function cusparseScsr2hyb(handle, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, userEllWidth, partitionType type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.29. cusparseDcsr2hyb
This function converts the sparse matrix in CSR format that is defined by the three arrays cscValA, cscRowPtrA, and cscColIndA into a sparse matrix in HYB format.
integer(4) function cusparseDcsr2hyb(handle, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, userEllWidth, partitionType type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.30. cusparseCcsr2hyb
This function converts the sparse matrix in CSR format that is defined by the three arrays cscValA, cscRowPtrA, and cscColIndA into a sparse matrix in HYB format.
integer(4) function cusparseCcsr2hyb(handle, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, userEllWidth, partitionType type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.31. cusparseZcsr2hyb
This function converts the sparse matrix in CSR format that is defined by the three arrays cscValA, cscRowPtrA, and cscColIndA into a sparse matrix in HYB format.
integer(4) function cusparseZcsr2hyb(handle, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, userEllWidth, partitionType type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.32. cusparseSdense2csc
This function converts the matrix A in dense format into a sparse matrix in CSC format.
integer(4) function cusparseSdense2csc(handle, m, n, descrA, A, lda, nnzPerCol, cscValA, cscRowIndA, cscColPtrA) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA real(4), device :: A(*), cscValA(*) integer(4), device :: nnzPerCol(*), cscRowIndA(*), cscColPtrA(*)
5.8.33. cusparseDdense2csc
This function converts the matrix A in dense format into a sparse matrix in CSC format.
integer(4) function cusparseDdense2csc(handle, m, n, descrA, A, lda, nnzPerCol, cscValA, cscRowIndA, cscColPtrA) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA real(8), device :: A(*), cscValA(*) integer(4), device :: nnzPerCol(*), cscRowIndA(*), cscColPtrA(*)
5.8.34. cusparseCdense2csc
This function converts the matrix A in dense format into a sparse matrix in CSC format.
integer(4) function cusparseCdense2csc(handle, m, n, descrA, A, lda, nnzPerCol, cscValA, cscRowIndA, cscColPtrA) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA complex(4), device :: A(*), cscValA(*) integer(4), device :: nnzPerCol(*), cscRowIndA(*), cscColPtrA(*)
5.8.35. cusparseZdense2csc
This function converts the matrix A in dense format into a sparse matrix in CSC format.
integer(4) function cusparseZdense2csc(handle, m, n, descrA, A, lda, nnzPerCol, cscValA, cscRowIndA, cscColPtrA) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA complex(8), device :: A(*), cscValA(*) integer(4), device :: nnzPerCol(*), cscRowIndA(*), cscColPtrA(*)
5.8.36. cusparseSdense2csr
This function converts the matrix A in dense format into a sparse matrix in CSR format.
integer(4) function cusparseSdense2csr(handle, m, n, descrA, A, lda, nnzPerRow, csrValA, csrRowPtrA, csrColIndA) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA real(4), device :: A(*), csrValA(*) integer(4), device :: nnzPerRow(*), csrRowPtrA(*), csrColIndA(*)
5.8.37. cusparseDdense2csr
This function converts the matrix A in dense format into a sparse matrix in CSR format.
integer(4) function cusparseDdense2csr(handle, m, n, descrA, A, lda, nnzPerRow, csrValA, csrRowPtrA, csrColIndA) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA real(8), device :: A(*), csrValA(*) integer(4), device :: nnzPerRow(*), csrRowPtrA(*), csrColIndA(*)
5.8.38. cusparseCdense2csr
This function converts the matrix A in dense format into a sparse matrix in CSR format.
integer(4) function cusparseCdense2csr(handle, m, n, descrA, A, lda, nnzPerRow, csrValA, csrRowPtrA, csrColIndA) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA complex(4), device :: A(*), csrValA(*) integer(4), device :: nnzPerRow(*), csrRowPtrA(*), csrColIndA(*)
5.8.39. cusparseZdense2csr
This function converts the matrix A in dense format into a sparse matrix in CSR format.
integer(4) function cusparseZdense2csr(handle, m, n, descrA, A, lda, nnzPerRow, csrValA, csrRowPtrA, csrColIndA) type(cusparseHandle) :: handle integer(4) :: m, n, lda type(cusparseMatDescr) :: descrA complex(8), device :: A(*), csrValA(*) integer(4), device :: nnzPerRow(*), csrRowPtrA(*), csrColIndA(*)
5.8.40. cusparseSdense2hyb
This function converts the matrix A in dense format into a sparse matrix in HYB format.
integer(4) function cusparseSdense2hyb(handle, m, n, descrA, A, lda, nnzPerRow, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, lda, userEllWidth, partitionType type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(4), device :: A(*) integer(4), device :: nnzPerRow(*)
5.8.41. cusparseDdense2hyb
This function converts the matrix A in dense format into a sparse matrix in HYB format.
integer(4) function cusparseDdense2hyb(handle, m, n, descrA, A, lda, nnzPerRow, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, lda, userEllWidth, partitionType type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(8), device :: A(*) integer(4), device :: nnzPerRow(*)
5.8.42. cusparseCdense2hyb
This function converts the matrix A in dense format into a sparse matrix in HYB format.
integer(4) function cusparseCdense2hyb(handle, m, n, descrA, A, lda, nnzPerRow, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, lda, userEllWidth, partitionType type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(4), device :: A(*) integer(4), device :: nnzPerRow(*)
5.8.43. cusparseZdense2hyb
This function converts the matrix A in dense format into a sparse matrix in HYB format.
integer(4) function cusparseZdense2hyb(handle, m, n, descrA, A, lda, nnzPerRow, hybA, userEllWidth, partitionType) type(cusparseHandle) :: handle integer(4) :: m, n, lda, userEllWidth, partitionType type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(8), device :: A(*) integer(4), device :: nnzPerRow(*)
5.8.44. cusparseShyb2csc
This function converts the sparse matrix A in HYB format into a sparse matrix in CSC format.
integer(4) function cusparseShyb2csc(handle, descrA, hybA, cscValA, cscRowIndA, cscColPtrA) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(4), device :: cscValA(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*)
5.8.45. cusparseDhyb2csc
This function converts the sparse matrix A in HYB format into a sparse matrix in CSC format.
integer(4) function cusparseDhyb2csc(handle, descrA, hybA, cscValA, cscRowIndA, cscColPtrA) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(8), device :: cscValA(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*)
5.8.46. cusparseChyb2csc
This function converts the sparse matrix A in HYB format into a sparse matrix in CSC format.
integer(4) function cusparseChyb2csc(handle, descrA, hybA, cscValA, cscRowIndA, cscColPtrA) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(4), device :: cscValA(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*)
5.8.47. cusparseZhyb2csc
This function converts the sparse matrix A in HYB format into a sparse matrix in CSC format.
integer(4) function cusparseZhyb2csc(handle, descrA, hybA, cscValA, cscRowIndA, cscColPtrA) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(8), device :: cscValA(*) integer(4), device :: cscRowIndA(*), cscColPtrA(*)
5.8.48. cusparseShyb2csr
This function converts the sparse matrix A in HYB format into a sparse matrix in CSR format.
integer(4) function cusparseShyb2csr(handle, descrA, hybA, csrValA, csrRowPtrA, csrColIndA) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.49. cusparseDhyb2csr
This function converts the sparse matrix A in HYB format into a sparse matrix in CSR format.
integer(4) function cusparseDhyb2csr(handle, descrA, hybA, csrValA, csrRowPtrA, csrColIndA) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.50. cusparseChyb2csr
This function converts the sparse matrix A in HYB format into a sparse matrix in CSR format.
integer(4) function cusparseChyb2csr(handle, descrA, hybA, csrValA, csrRowPtrA, csrColIndA) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(4), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.51. cusparseZhyb2csr
This function converts the sparse matrix A in HYB format into a sparse matrix in CSR format.
integer(4) function cusparseZhyb2csr(handle, descrA, hybA, csrValA, csrRowPtrA, csrColIndA) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(8), device :: csrValA(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*)
5.8.52. cusparseShyb2dense
This function converts the sparse matrix in HYB format into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseShyb2dense(handle, descrA, hybA, A, lda) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(4), device :: A(*) integer(4) :: lda
5.8.53. cusparseDhyb2dense
This function converts the sparse matrix in HYB format into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseDhyb2dense(handle, descrA, hybA, A, lda) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA real(8), device :: A(*) integer(4) :: lda
5.8.54. cusparseChyb2dense
This function converts the sparse matrix in HYB format into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseChyb2dense(handle, descrA, hybA, A, lda) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(4), device :: A(*) integer(4) :: lda
5.8.55. cusparseZhyb2dense
This function converts the sparse matrix in HYB format into the matrix A in dense format. The dense matrix A is filled in with the values of the sparse matrix and with zeros elsewhere.
integer(4) function cusparseZhyb2dense(handle, descrA, hybA, A, lda) type(cusparseHandle) :: handle type(cusparseMatDescr) :: descrA type(cusparseHybMat) :: hybA complex(8), device :: A(*) integer(4) :: lda
5.8.56. cusparseSnnz
This function computes the number of nonzero elements per row or column and the total number of nonzero elements in a dense matrix.
integer(4) function cusparseSnnz(handle, dirA, m, n, descrA, A, lda, nnzPerRowColumn, nnzTotalDevHostPtr) type(cusparseHandle) :: handle integer :: dirA, m, n, lda type(cusparseMatDescr) :: descrA real(4), device :: A(*) integer(4), device :: nnzPerRowColumn(*) integer(4), device :: nnzTotalDevHostPtr ! device or host variable
5.8.57. cusparseDnnz
This function computes the number of nonzero elements per row or column and the total number of nonzero elements in a dense matrix.
integer(4) function cusparseDnnz(handle, dirA, m, n, descrA, A, lda, nnzPerRowColumn, nnzTotalDevHostPtr) type(cusparseHandle) :: handle integer :: dirA, m, n, lda type(cusparseMatDescr) :: descrA real(8), device :: A(*) integer(4), device :: nnzPerRowColumn(*) integer(4), device :: nnzTotalDevHostPtr ! device or host variable
5.8.58. cusparseCnnz
This function computes the number of nonzero elements per row or column and the total number of nonzero elements in a dense matrix.
integer(4) function cusparseCnnz(handle, dirA, m, n, descrA, A, lda, nnzPerRowColumn, nnzTotalDevHostPtr) type(cusparseHandle) :: handle integer :: dirA, m, n, lda type(cusparseMatDescr) :: descrA complex(4), device :: A(*) integer(4), device :: nnzPerRowColumn(*) integer(4), device :: nnzTotalDevHostPtr ! device or host variable
5.8.59. cusparseZnnz
This function computes the number of nonzero elements per row or column and the total number of nonzero elements in a dense matrix.
integer(4) function cusparseZnnz(handle, dirA, m, n, descrA, A, lda, nnzPerRowColumn, nnzTotalDevHostPtr) type(cusparseHandle) :: handle integer :: dirA, m, n, lda type(cusparseMatDescr) :: descrA complex(8), device :: A(*) integer(4), device :: nnzPerRowColumn(*) integer(4), device :: nnzTotalDevHostPtr ! device or host variable
5.8.60. cusparseSgebsr2gebsc_bufferSize
This function returns the size of the buffer used in gebsr2gebsc.
integer(4) function cusparseSgebsr2gebsc_bufferSize(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDim, colBlockDim, pBufferSize) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb real(4), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDim, colBlockDim integer(4) :: pBufferSize ! integer(8) also accepted
5.8.61. cusparseDgebsr2gebsc_bufferSize
This function returns the size of the buffer used in gebsr2gebsc.
integer(4) function cusparseDgebsr2gebsc_bufferSize(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDim, colBlockDim, pBufferSize) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb real(8), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDim, colBlockDim integer(4) :: pBufferSize ! integer(8) also accepted
5.8.62. cusparseCgebsr2gebsc_bufferSize
This function returns the size of the buffer used in gebsr2gebsc.
integer(4) function cusparseCgebsr2gebsc_bufferSize(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDim, colBlockDim, pBufferSize) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb complex(4), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDim, colBlockDim integer(4) :: pBufferSize ! integer(8) also accepted
5.8.63. cusparseZgebsr2gebsc_bufferSize
This function returns the size of the buffer used in gebsr2gebsc.
integer(4) function cusparseZgebsr2gebsc_bufferSize(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDim, colBlockDim, pBufferSize) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb complex(8), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDim, colBlockDim integer(4) :: pBufferSize ! integer(8) also accepted
5.8.64. cusparseSgebsr2gebsc
This function converts a sparse matrix in general block-CSR storage format to a sparse matrix in general block-CSC storage format.
integer(4) function cusparseSgebsr2gebsc(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDim, colBlockDim, bscVal, bscRowInd, bscColPtr, copyValues, baseIdx, pBuffer) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb real(4), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDim, colBlockDim real(4), device :: bscVal(*) integer(4), device :: bscRowInd(*), bscColPtr(*) integer(4) :: copyValues, baseIdx character(c_char), device :: pBuffer(*)
5.8.65. cusparseDgebsr2gebsc
This function converts a sparse matrix in general block-CSR storage format to a sparse matrix in general block-CSC storage format.
integer(4) function cusparseDgebsr2gebsc(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDim, colBlockDim, bscVal, bscRowInd, bscColPtr, copyValues, baseIdx, pBuffer) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb real(8), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDim, colBlockDim real(8), device :: bscVal(*) integer(4), device :: bscRowInd(*), bscColPtr(*) integer(4) :: copyValues, baseIdx character(c_char), device :: pBuffer(*)
5.8.66. cusparseCgebsr2gebsc
This function converts a sparse matrix in general block-CSR storage format to a sparse matrix in general block-CSC storage format.
integer(4) function cusparseCgebsr2gebsc(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDim, colBlockDim, bscVal, bscRowInd, bscColPtr, copyValues, baseIdx, pBuffer) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb complex(4), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDim, colBlockDim complex(4), device :: bscVal(*) integer(4), device :: bscRowInd(*), bscColPtr(*) integer(4) :: copyValues, baseIdx character(c_char), device :: pBuffer(*)
5.8.67. cusparseZgebsr2gebsc
This function converts a sparse matrix in general block-CSR storage format to a sparse matrix in general block-CSC storage format.
integer(4) function cusparseZgebsr2gebsc(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDim, colBlockDim, bscVal, bscRowInd, bscColPtr, copyValues, baseIdx, pBuffer) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb complex(8), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDim, colBlockDim complex(8), device :: bscVal(*) integer(4), device :: bscRowInd(*), bscColPtr(*) integer(4) :: copyValues, baseIdx character(c_char), device :: pBuffer(*)
5.8.68. cusparseSgebsr2gebsr_bufferSize
This function returns the size of the buffer used in gebsr2gebsrnnz and gebsr2gebsr.
integer(4) function cusparseSgebsr2gebsr_bufferSize(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDimA, colBlockDimA, rowBlockDimC, colBlockDimC, pBufferSize) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb real(4), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDimA, colBlockDimA, rowBlockDimC, colBlockDimC integer(4) :: pBufferSize ! integer(8) also accepted
5.8.69. cusparseDgebsr2gebsr_bufferSize
This function returns the size of the buffer used in gebsr2gebsrnnz and gebsr2gebsr.
integer(4) function cusparseDgebsr2gebsr_bufferSize(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDimA, colBlockDimA, rowBlockDimC, colBlockDimC, pBufferSize) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb real(8), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDimA, colBlockDimA, rowBlockDimC, colBlockDimC integer(4) :: pBufferSize ! integer(8) also accepted
5.8.70. cusparseCgebsr2gebsr_bufferSize
This function returns the size of the buffer used in gebsr2gebsrnnz and gebsr2gebsr.
integer(4) function cusparseCgebsr2gebsr_bufferSize(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDimA, colBlockDimA, rowBlockDimC, colBlockDimC, pBufferSize) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb complex(4), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDimA, colBlockDimA, rowBlockDimC, colBlockDimC integer(4) :: pBufferSize ! integer(8) also accepted
5.8.71. cusparseZgebsr2gebsr_bufferSize
This function returns the size of the buffer used in gebsr2gebsrnnz and gebsr2gebsr.
integer(4) function cusparseZgebsr2gebsr_bufferSize(handle, mb, nb, nnzb, bsrVal, bsrRowPtr, bsrColInd, rowBlockDimA, colBlockDimA, rowBlockDimC, colBlockDimC, pBufferSize) type(cusparseHandle) :: handle integer(4) :: mb, nb, nnzb complex(8), device :: bsrVal(*) integer(4), device :: bsrRowPtr(*), bsrColInd(*) integer(4) :: rowBlockDimA, colBlockDimA, rowBlockDimC, colBlockDimC integer(4) :: pBufferSize ! integer(8) also accepted
5.8.72. cusparseXgebsr2gebsrNnz
cusparseXcsrgeamNnz computes the number of nonzero elements which will be produced by CSRGEAM.
integer(4) function cusparseXgebsr2gebsrNnz(handle, dir, mb, nb, nnzb, descrA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, bsrRowPtrC, rowBlockDimC, colBlockDimC, nnzTotalDevHostPtr, pBuffer) type(cusparseHandle) :: handle integer :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*) integer :: rowBlockDimC, colBlockDimC integer(4), device :: nnzTotalDevHostPtr ! device or host variable character, device :: pBuffer(*)
5.8.73. cusparseSgebsr2gebsr
This function converts a sparse matrix in general BSR storage format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in another general BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseSgebsr2gebsr(handle, dir, mb, nb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, bsrValC, bsrRowPtrC, bsrColIndC, rowBlockDimC, colBlockDimC, pBuffer) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*), bsrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*), bsrColIndC(*) integer(4) :: rowBlockDimC, colBlockDimC character(c_char), device :: pBuffer(*)
5.8.74. cusparseDgebsr2gebsr
This function converts a sparse matrix in general BSR storage format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in another general BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseDgebsr2gebsr(handle, dir, mb, nb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, bsrValC, bsrRowPtrC, bsrColIndC, rowBlockDimC, colBlockDimC, pBuffer) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*), bsrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*), bsrColIndC(*) integer(4) :: rowBlockDimC, colBlockDimC character(c_char), device :: pBuffer(*)
5.8.75. cusparseCgebsr2gebsr
This function converts a sparse matrix in general BSR storage format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in another general BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseCgebsr2gebsr(handle, dir, mb, nb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, bsrValC, bsrRowPtrC, bsrColIndC, rowBlockDimC, colBlockDimC, pBuffer) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*), bsrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*), bsrColIndC(*) integer(4) :: rowBlockDimC, colBlockDimC character(c_char), device :: pBuffer(*)
5.8.76. cusparseZgebsr2gebsr
This function converts a sparse matrix in general BSR storage format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in another general BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseZgebsr2gebsr(handle, dir, mb, nb, nnzb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, bsrValC, bsrRowPtrC, bsrColIndC, rowBlockDimC, colBlockDimC, pBuffer) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*), bsrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*), bsrColIndC(*) integer(4) :: rowBlockDimC, colBlockDimC character(c_char), device :: pBuffer(*)
5.8.77. cusparseSgebsr2csr
This function converts a sparse matrix in general BSR storage format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in CSR format that is defined by arrays csrValC, csrRowPtrC, and csrColIndC.
integer(4) function cusparseSgebsr2csr(handle, dir, mb, nb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: bsrValA(*), csrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: csrRowPtrC(*), csrColIndC(*)
5.8.78. cusparseDgebsr2csr
This function converts a sparse matrix in general BSR storage format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in CSR format that is defined by arrays csrValC, csrRowPtrC, and csrColIndC.
integer(4) function cusparseDgebsr2csr(handle, dir, mb, nb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: bsrValA(*), csrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: csrRowPtrC(*), csrColIndC(*)
5.8.79. cusparseCgebsr2csr
This function converts a sparse matrix in general BSR storage format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in CSR format that is defined by arrays csrValC, csrRowPtrC, and csrColIndC.
integer(4) function cusparseCgebsr2csr(handle, dir, mb, nb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: bsrValA(*), csrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: csrRowPtrC(*), csrColIndC(*)
5.8.80. cusparseZgebsr2csr
This function converts a sparse matrix in general BSR storage format that is defined by the three arrays bsrValA, bsrRowPtrA, and bsrColIndA into a sparse matrix in CSR format that is defined by arrays csrValC, csrRowPtrC, and csrColIndC.
integer(4) function cusparseZgebsr2csr(handle, dir, mb, nb, descrA, bsrValA, bsrRowPtrA, bsrColIndA, rowBlockDimA, colBlockDimA, descrC, csrValC, csrRowPtrC, csrColIndC) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: bsrValA(*), csrValC(*) integer(4), device :: bsrRowPtrA(*), bsrColIndA(*) integer(4) :: rowBlockDimA, colBlockDimA type(cusparseMatDescr) :: descrC integer(4), device :: csrRowPtrC(*), csrColIndC(*)
5.8.81. cusparseScsr2gebsr_bufferSize
This function returns the size of the buffer used in csr2gebsrnnz and csr2gebsr.
integer(4) function cusparseScsr2gebsr_bufferSize(handle, dir, m, n, descrA, csrVal, csrRowPtr, csrColInd, rowBlockDim, colBlockDim, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dir, m, n real(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) integer(4) :: rowBlockDim, colBlockDim integer(4) :: pBufferSize ! integer(8) also accepted
5.8.82. cusparseDcsr2gebsr_bufferSize
This function returns the size of the buffer used in csr2gebsrnnz and csr2gebsr.
integer(4) function cusparseDcsr2gebsr_bufferSize(handle, dir, m, n, descrA, csrVal, csrRowPtr, csrColInd, rowBlockDim, colBlockDim, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dir, m, n real(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) integer(4) :: rowBlockDim, colBlockDim integer(4) :: pBufferSize ! integer(8) also accepted
5.8.83. cusparseCcsr2gebsr_bufferSize
This function returns the size of the buffer used in csr2gebsrnnz and csr2gebsr.
integer(4) function cusparseCcsr2gebsr_bufferSize(handle, dir, m, n, descrA, csrVal, csrRowPtr, csrColInd, rowBlockDim, colBlockDim, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dir, m, n complex(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) integer(4) :: rowBlockDim, colBlockDim integer(4) :: pBufferSize ! integer(8) also accepted
5.8.84. cusparseZcsr2gebsr_bufferSize
This function returns the size of the buffer used in csr2gebsrnnz and csr2gebsr.
integer(4) function cusparseZcsr2gebsr_bufferSize(handle, dir, m, n, descrA, csrVal, csrRowPtr, csrColInd, rowBlockDim, colBlockDim, pBufferSize) type(cusparseHandle) :: handle integer(4) :: dir, m, n complex(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) integer(4) :: rowBlockDim, colBlockDim integer(4) :: pBufferSize ! integer(8) also accepted
5.8.85. cusparseXcsr2gebsrNnz
cusparseXcsrgeamNnz computes the number of nonzero elements which will be produced by CSRGEAM.
integer(4) function cusparseXcsr2gebsrNnz(handle, dir, m, n, descrA, csrRowPtrA, csrColIndA, descrC, bsrRowPtrC, rowBlockDimC, colBlockDimC, nnzTotalDevHostPtr, pBuffer) type(cusparseHandle) :: handle integer :: dir, m, n type(cusparseMatDescr) :: descrA integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*) integer :: rowBlockDimC, colBlockDimC integer(4), device :: nnzTotalDevHostPtr ! device or host variable character, device :: pBuffer(*)
5.8.86. cusparseScsr2gebsr
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrValA, csrRowPtrA, and csrColIndA into a sparse matrix in general BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseScsr2gebsr(handle, dir, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, descrC, bsrValC, bsrRowPtrC, bsrColIndC, rowBlockDimC, colBlockDimC, pBuffer) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA real(4), device :: csrValA(*), bsrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*), bsrColIndC(*) integer(4) :: rowBlockDimC, colBlockDimC character(c_char), device :: pBuffer(*)
5.8.87. cusparseDcsr2gebsr
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrValA, csrRowPtrA, and csrColIndA into a sparse matrix in general BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseDcsr2gebsr(handle, dir, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, descrC, bsrValC, bsrRowPtrC, bsrColIndC, rowBlockDimC, colBlockDimC, pBuffer) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA real(8), device :: csrValA(*), bsrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*), bsrColIndC(*) integer(4) :: rowBlockDimC, colBlockDimC character(c_char), device :: pBuffer(*)
5.8.88. cusparseCcsr2gebsr
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrValA, csrRowPtrA, and csrColIndA into a sparse matrix in general BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseCcsr2gebsr(handle, dir, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, descrC, bsrValC, bsrRowPtrC, bsrColIndC, rowBlockDimC, colBlockDimC, pBuffer) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA complex(4), device :: csrValA(*), bsrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*), bsrColIndC(*) integer(4) :: rowBlockDimC, colBlockDimC character(c_char), device :: pBuffer(*)
5.8.89. cusparseZcsr2gebsr
This function converts a sparse matrix in CSR storage format that is defined by the three arrays csrValA, csrRowPtrA, and csrColIndA into a sparse matrix in general BSR format that is defined by arrays bsrValC, bsrRowPtrC, and bsrColIndC.
integer(4) function cusparseZcsr2gebsr(handle, dir, m, n, descrA, csrValA, csrRowPtrA, csrColIndA, descrC, bsrValC, bsrRowPtrC, bsrColIndC, rowBlockDimC, colBlockDimC, pBuffer) type(cusparseHandle) :: handle integer(4) :: dir, mb, nb, nnzb type(cusparseMatDescr) :: descrA complex(8), device :: csrValA(*), bsrValC(*) integer(4), device :: csrRowPtrA(*), csrColIndA(*) type(cusparseMatDescr) :: descrC integer(4), device :: bsrRowPtrC(*), bsrColIndC(*) integer(4) :: rowBlockDimC, colBlockDimC character(c_char), device :: pBuffer(*)
5.8.90. cusparseCreateIdentityPermutation
This function creates an identity map. The output parameter p represents such map by p = 0:1:(n-1). This function is typically used with coosort, csrsort, cscsort, and csr2csc_indexOnly.
integer(4) function cusparseCreateIdentityPermutation(handle, n, p) type(cusparseHandle) :: handle integer(4) :: n integer(4), device :: p(*)
5.8.91. cusparseXcoosort_bufferSize
This function returns the size of the buffer used in coosort.
integer(4) function cusparseXcoosort_bufferSize(handle, m, n, nnz, cooRows, cooCols, pBufferSizeInBytes) type(cusparseHandle) :: handle integer(4) :: m, n, nnz integer(4), device :: cooRows(*), cooCols(*) integer(8) :: pBufferSizeInBytes
5.8.92. cusparseXcoosortByRow
This function sorts the sparse matrix stored in COO format.
integer(4) function cusparseXcoosortByRow(handle, m, n, nnz, cooRows, cooCols, P, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz integer(4), device :: cooRows(*), cooCols(*), P(*) character(c_char), device :: pBuffer(*)
5.8.93. cusparseXcoosortByColumn
This function sorts the sparse matrix stored in COO format.
integer(4) function cusparseXcoosortByColumn(handle, m, n, nnz, cooRows, cooCols, P, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz integer(4), device :: cooRows(*), cooCols(*), P(*) character(c_char), device :: pBuffer(*)
5.8.94. cusparseXcsrsort_bufferSize
This function returns the size of the buffer used in csrsort.
integer(4) function cusparseXcsrsort_bufferSize(handle, m, n, nnz, csrRowInd, csrColInd, pBufferSizeInBytes) type(cusparseHandle) :: handle integer(4) :: m, n, nnz integer(4), device :: csrRowInd(*), csrColInd(*) integer(8) :: pBufferSizeInBytes
5.8.95. cusparseXcsrsort
This function sorts the sparse matrix stored in CSR format.
integer(4) function cusparseXcsrsort(handle, m, n, nnz, csrRowInd, csrColInd, P, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz integer(4), device :: csrRowInd(*), csrColInd(*), P(*) character(c_char), device :: pBuffer(*)
5.8.96. cusparseXcscsort_bufferSize
This function returns the size of the buffer used in cscsort.
integer(4) function cusparseXcscsort_bufferSize(handle, m, n, nnz, cscColPtr, cscRowInd, pBufferSizeInBytes) type(cusparseHandle) :: handle integer(4) :: m, n, nnz integer(4), device :: cscColPtr(*), cscRowInd(*) integer(8) :: pBufferSizeInBytes
5.8.97. cusparseXcscsort
This function sorts the sparse matrix stored in CSC format.
integer(4) function cusparseXcscsort(handle, m, n, nnz, cscColPtr, cscRowInd, P, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz integer(4), device :: cscColPtr(*), cscRowInd(*), P(*) character(c_char), device :: pBuffer(*)
5.8.98. cusparseScsru2csr_bufferSize
This function returns the size of the buffer used in csru2csr.
integer(4) function cusparseScsru2csr_bufferSize(handle, m, n, nnz, csrVal, csrRowPtr, csrColInd, info, pBufferSizeInBytes) type(cusparseHandle) :: handle integer(4) :: m, n, nnz real(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info integer(8) :: pBufferSizeInBytes
5.8.99. cusparseDcsru2csr_bufferSize
This function returns the size of the buffer used in csru2csr.
integer(4) function cusparseDcsru2csr_bufferSize(handle, m, n, nnz, csrVal, csrRowPtr, csrColInd, info, pBufferSizeInBytes) type(cusparseHandle) :: handle integer(4) :: m, n, nnz real(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info integer(8) :: pBufferSizeInBytes
5.8.100. cusparseCcsru2csr_bufferSize
This function returns the size of the buffer used in csru2csr.
integer(4) function cusparseCcsru2csr_bufferSize(handle, m, n, nnz, csrVal, csrRowPtr, csrColInd, info, pBufferSizeInBytes) type(cusparseHandle) :: handle integer(4) :: m, n, nnz complex(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info integer(8) :: pBufferSizeInBytes
5.8.101. cusparseZcsru2csr_bufferSize
This function returns the size of the buffer used in csru2csr.
integer(4) function cusparseZcsru2csr_bufferSize(handle, m, n, nnz, csrVal, csrRowPtr, csrColInd, info, pBufferSizeInBytes) type(cusparseHandle) :: handle integer(4) :: m, n, nnz complex(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info integer(8) :: pBufferSizeInBytes
5.8.102. cusparseScsru2csr
This function transfers unsorted CSR format to CSR format.
integer(4) function cusparseScsru2csr(handle, m, n, nnz, descrA, csrVal, csrRowPtr, csrColInd, info, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info character(c_char), device :: pBuffer(*)
5.8.103. cusparseDcsru2csr
This function transfers unsorted CSR format to CSR format.
integer(4) function cusparseDcsru2csr(handle, m, n, nnz, descrA, csrVal, csrRowPtr, csrColInd, info, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info character(c_char), device :: pBuffer(*)
5.8.104. cusparseCcsru2csr
This function transfers unsorted CSR format to CSR format.
integer(4) function cusparseCcsru2csr(handle, m, n, nnz, descrA, csrVal, csrRowPtr, csrColInd, info, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info character(c_char), device :: pBuffer(*)
5.8.105. cusparseZcsru2csr
This function transfers unsorted CSR format to CSR format.
integer(4) function cusparseZcsru2csr(handle, m, n, nnz, descrA, csrVal, csrRowPtr, csrColInd, info, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info character(c_char), device :: pBuffer(*)
5.8.106. cusparseScsr2csru
This function performs the backwards transformation from sorted CSR format to unsorted CSR format.
integer(4) function cusparseScsr2csru(handle, m, n, nnz, descrA, csrVal, csrRowPtr, csrColInd, info, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz type(cusparseMatDescr) :: descrA real(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info character(c_char), device :: pBuffer(*)
5.8.107. cusparseDcsr2csru
This function performs the backwards transformation from sorted CSR format to unsorted CSR format.
integer(4) function cusparseDcsr2csru(handle, m, n, nnz, descrA, csrVal, csrRowPtr, csrColInd, info, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz type(cusparseMatDescr) :: descrA real(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info character(c_char), device :: pBuffer(*)
5.8.108. cusparseCcsr2csru
This function performs the backwards transformation from sorted CSR format to unsorted CSR format.
integer(4) function cusparseCcsr2csru(handle, m, n, nnz, descrA, csrVal, csrRowPtr, csrColInd, info, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz type(cusparseMatDescr) :: descrA complex(4), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info character(c_char), device :: pBuffer(*)
5.8.109. cusparseZcsr2csru
This function performs the backwards transformation from sorted CSR format to unsorted CSR format.
integer(4) function cusparseZcsr2csru(handle, m, n, nnz, descrA, csrVal, csrRowPtr, csrColInd, info, pBuffer) type(cusparseHandle) :: handle integer(4) :: m, n, nnz type(cusparseMatDescr) :: descrA complex(8), device :: csrVal(*) integer(4), device :: csrRowPtr(*), csrColInd(*) type(cusparseCsru2csrInfo) :: info character(c_char), device :: pBuffer(*)
6. Examples
This section contains examples with source code.
6.1. Using cuBLAS from OpenACC Host Code
This example demonstrates the use of the cublas module, the cublasHandle type, and several forms of blas calls from OpenACC data regions.
Simple OpenACC BLAS Test
program testcublas ! compile with pgfortran -ta=tesla -Mcudalib=cublas -Mcuda testcublas.f90 call testcu1(1000) call testcu2(1000) end ! subroutine testcu1(n) use openacc use cublas integer :: a(n), b(n) type(cublasHandle) :: h istat = cublasCreate(h) ! Force OpenACC kernels and cuBLAS to use the OpenACC stream. istat = cublasSetStream(h, acc_get_cuda_stream(acc_async_sync)) !$acc data copyout(a, b) !$acc kernels a = 1 b = 2 !$acc end kernels ! No host_data, we are lexically inside a data region ! sswap will accept any kind(4) data type call sswap(n, a, 1, b, 1) call cublasSswap(n, a, 1, b, 1) !$acc end data if (all(a.eq.1).and.all(b.eq.2)) then print *,"Test PASSED" else print *,"Test FAILED" endif end ! subroutine testcu2(n) use openacc use cublas real(8) :: a(n), b(n) a = 1.0d0 b = 2.0d0 !$acc data copy(a, b) !$acc host_data use_device(a,b) call dswap(n, a, 1, b, 1) call cublasDswap(n, a, 1, b, 1) !$acc end host_data !$acc end data if (all(a.eq.1.0d0).and.all(b.eq.2.0d0)) then print *,"Test PASSED" else print *,"Test FAILED" endif end
CUBLASXT BLAS Test
This example demonstrates the use of the cublasxt module
program testcublasxt call testxt1(1000) call testxt2(1000) end ! subroutine testxt1(n) use cublasxt real(4) :: a(n,n), b(n,n), c(n,n), alpha, beta type(cublasXtHandle) :: h integer ndevices(1) a = 1.0 b = 2.0 c = -1.0 alpha = 1.0 beta = 0.0 istat = cublasXtCreate(h) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat ndevices(1) = 0 istat = cublasXtDeviceSelect(h, 1, ndevices) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat istat = cublasXtSgemm(h, CUBLAS_OP_N, CUBLAS_OP_N, & n, n, n, & alpha, A, n, B, n, beta, C, n) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat istat = cublasXtDestroy(h) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat if (all(c.eq.2.0*n)) then print *,"Test PASSED" else print *,"Test FAILED" endif print *,c(1,1),c(n,n) end ! subroutine testxt2(n) use cublasxt real(8) :: a(n,n), b(n,n), c(n,n), alpha, beta type(cublasXtHandle) :: h integer ndevices(1) a = 1.0d0 b = 2.0d0 c = -1.0d0 alpha = 1.0d0 beta = 0.0 istat = cublasXtCreate(h) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat ndevices(1) = 0 istat = cublasXtDeviceSelect(h, 1, ndevices) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat istat = cublasXtDgemm(h, CUBLAS_OP_N, CUBLAS_OP_N, & n, n, n, & alpha, A, n, B, n, beta, C, n) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat istat = cublasXtDestroy(h) if (istat .ne. CUBLAS_STATUS_SUCCESS) print *,istat if (all(c.eq.2.0d0*n)) then print *,"Test PASSED" else print *,"Test FAILED" endif print *,c(1,1),c(n,n) end
6.2. Using cuBLAS from OpenACC Device Code
This example demonstrates the use of the openacc_cublas module from within an OpenACC kernel.
Simple BLAS Test from Device Code
module mtests integer, parameter :: n = 100 contains subroutine testcu( a, b ) use openacc_cublas real :: a(n), b(n) type(cublasHandle) :: h !$acc parallel num_gangs(1) copy(a,b,h) j = cublasCreate(h) j = cublasSswap(h,n,a,1,b,1) j = cublasDestroy(h) !$acc end parallel return end subroutine end module mtests program t ! compile with pgfortran -ta=tesla:cc35 -Mcuda t.f90 -lcublas_device use mtests real :: a(n), b(n), c(n) logical passing a = 1.0 b = 2.0 passing = .true. call testcu(a,b) print *,"Should all be 2.0" print *,a passing = passing .and. all(a.eq.2.0) print *,"Should all be 1.0" print *,b passing = passing .and. all(b.eq.1.0) if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end program
6.3. Using cuBLAS from CUDA Fortran Host Code
This example demonstrates the use of the cublas module, the cublasHandle type, and several forms of blas calls.
Simple BLAS Test
program testisamax ! Compile with "pgfortran testisamax.cuf -Mcudalib=cublas -lblas" ! Use the PGI cudafor and cublas modules use cudafor use cublas ! real*4, device, allocatable :: xd(:) real*4 x(1000) integer, device :: kd type(cublasHandle) :: h call random_number(x) ! F90 way i = maxloc(x,dim=1) print *,i print *,x(i-1),x(i),x(i+1) ! Host way j = isamax(1000,x,1) print *,j print *,x(j-1),x(j),x(j+1) ! CUDA Generic BLAS way allocate(xd(1000)) xd = x k = isamax(1000,xd,1) print *,k print *,x(k-1),x(k),x(k+1) ! CUDA Specific BLAS way k = cublasIsamax(1000,xd,1) print *,k print *,x(k-1),x(k),x(k+1) ! CUDA V2 Host Specific BLAS way istat = cublasCreate(h) if (istat .ne. 0) print *,"cublasCreate returned ",istat k = 0 istat = cublasIsamax_v2(h, 1000, xd, 1, k) if (istat .ne. 0) print *,"cublasIsamax 1 returned ",istat print *,k print *,x(k-1),x(k),x(k+1) ! CUDA V2 Device Specific BLAS way k = 0 istat = cublasIsamax_v2(h, 1000, xd, 1, kd) if (istat .ne. 0) print *,"cublasIsamax 2 returned ",istat k = kd print *,k print *,x(k-1),x(k),x(k+1) istat = cublasDestroy(h) if (istat .ne. 0) print *,"cublasDestroy returned ",istat end program
Multi-threaded BLAS Test
This example demonstrates the use of the cublas module in a multi-threaded code. Each thread will attach to a different GPU, create a context, and combine the results at the end.
program tsgemm ! ! Multi-threaded example calling sgemm from cuda fortran. ! Compile with "pgfortran -mp tsgemm.cuf -Mcudalib=cublas" ! Set OMP_NUM_THREADS=number of GPUs in your system. ! use cublas use cudafor use omp_lib ! ! Size this according to number of GPUs ! ! Small !integer, parameter :: K = 2500 !integer, parameter :: M = 2000 !integer, parameter :: N = 2000 ! Large integer, parameter :: K = 10000 integer, parameter :: M = 10000 integer, parameter :: N = 10000 integer, parameter :: NTIMES = 10 ! real*4, device, allocatable :: a_d(:,:), b_d(:,:), c_d(:,:) !$omp THREADPRIVATE(a_d,b_d,c_d) real*4 a(m,k), b(k,n), c(m,n) real*4 alpha, beta integer, allocatable :: offs(:) type(cudaEvent) :: start, stop a = 1.0; b = 0.0; c = 0.0 do i = 1, N b(i,i) = 1.0 end do alpha = 1.0; beta = 1.0 ! Break up the B and C array into sections nthr = omp_get_max_threads() nsec = N / nthr print *,"Running with ",nthr," threads, each section = ",nsec allocate(offs(nthr)) offs = (/ (i*nsec,i=0,nthr-1) /) ! Allocate and initialize the arrays ! Each thread connects to a device and creates a CUDA context. !$omp PARALLEL private(i,istat) i = omp_get_thread_num() + 1 istat = cudaSetDevice(i-1) allocate(a_d(M,K), b_d(K,nsec), c_d(M,nsec)) a_d = a b_d = b(:,offs(i)+1:offs(i)+nsec) c_d = c(:,offs(i)+1:offs(i)+nsec) !$omp end parallel istat = cudaEventCreate(start) istat = cudaEventCreate(stop) time = 0.0 istat = cudaEventRecord(start, 0) ! Run the traditional blas kernel !$omp PARALLEL private(j,istat) do j = 1, NTIMES call sgemm('n','n', M, N/nthr, K, alpha, a_d, M, b_d, K, beta, c_d, M) end do istat = cudaDeviceSynchronize() !$omp end parallel istat = cudaEventRecord(stop, 0) istat = cudaEventElapsedTime(time, start, stop) time = time / (NTIMES*1.0e3) !$omp PARALLEL private(i) i = omp_get_thread_num() + 1 c(:,offs(i)+1:offs(i)+nsec) = c_d !$omp end parallel nerrors = 0 do j = 1, N do i = 1, M if (c(i,j) .ne. NTIMES) nerrors = nerrors + 1 end do end do print *,"Number of Errors:",nerrors gflops = 2.0 * N * M * K/time/1e9 write (*,901) m,k,k,N,time*1.0e3,gflops 901 format(i0,'x',i0,' * ',i0,'x',i0,':\t',f8.3,' ms\t',f12.3,' GFlops/s') end
6.4. Using cuBLAS from CUDA Fortran Device Code
This example demonstrates the use of the cublas_device module from a CUDA Fortran global subroutines.
Simple BLAS Test from Device Code
module mtests use cublas_device contains attributes(global) subroutine testb( a, b, n ) real, device :: a(*), b(*) integer, value :: n type(cublasHandle) :: h i = threadIdx%x if (i.eq.1) then j = cublasCreate(h) j = cublasSswap(h,n,a,1,b,1) j = cublasDestroy(h) end if return end subroutine end module mtests program tdev ! Compile and link with "pgfortran -Mcuda=cc35 tdev.cuf -lcublas_device" use mtests integer, parameter :: nt = 128 real, device :: a(nt), b(nt) real c(nt) a = 1.0 b = 2.0 call testb<<<1,nt>>> (a,b,nt) c = a print *,"Should be 2.0",all(c.eq.2.0) c = b print *,"Should be 1.0",all(c.eq.1.0) end end program
6.5. Using cuFFT from OpenACC Host Code
This example demonstrates the use of the cufft module, the cufftHandle type, and several cuFFT library calls.
Simple cuFFT Test
program cufft2dTest use cufft use openacc integer, parameter :: m=768, n=512 complex, allocatable :: a(:,:),b(:,:),c(:,:) real, allocatable :: r(:,:),q(:,:) integer :: iplan1, iplan2, iplan3, ierr allocate(a(m,n),b(m,n),c(m,n)) allocate(r(m,n),q(m,n)) a = 1; r = 1 xmx = -99.0 ierr = cufftPlan2D(iplan1,m,n,CUFFT_C2C) ierr = ierr + cufftSetStream(iplan1,acc_get_cuda_stream(acc_async_sync)) !$acc host_data use_device(a,b,c) ierr = ierr + cufftExecC2C(iplan1,a,b,CUFFT_FORWARD) ierr = ierr + cufftExecC2C(iplan1,b,c,CUFFT_INVERSE) !$acc end host_data ! scale c !$acc kernels c = c / (m*n) !$acc end kernels ! Check forward answer write(*,*) 'Max error C2C FWD: ', cmplx(maxval(real(b)) - sum(real(b)), & maxval(imag(b))) ! Check inverse answer write(*,*) 'Max error C2C INV: ', maxval(abs(a-c)) ! Real transform ierr = ierr + cufftPlan2D(iplan2,m,n,CUFFT_R2C) ierr = ierr + cufftPlan2D(iplan3,m,n,CUFFT_C2R) ierr = ierr + cufftSetStream(iplan2,acc_get_cuda_stream(acc_async_sync)) ierr = ierr + cufftSetStream(iplan3,acc_get_cuda_stream(acc_async_sync)) !$acc host_data use_device(r,b,q) ierr = ierr + cufftExecR2C(iplan2,r,b) ierr = ierr + cufftExecC2R(iplan3,b,q) !$acc end host_data !$acc kernels xmx = maxval(abs(r-q/(m*n))) !$acc end kernels ! Check R2C + C2R answer write(*,*) 'Max error R2C/C2R: ', xmx ierr = ierr + cufftDestroy(iplan1) ierr = ierr + cufftDestroy(iplan2) ierr = ierr + cufftDestroy(iplan3) if (ierr.eq.0) then print *,"test PASSED" else print *,"test FAILED" endif end program cufft2dTest
6.6. Using cuFFT from CUDA Fortran Host Code
This example demonstrates the use of the cuFFT module, the cufftHandle type, and several cuFFT library calls.
Simple cuFFT Test
program cufft2dTest use cudafor use cufft implicit none integer, parameter :: m=768, n=512 complex, managed :: a(m,n),b(m,n) real, managed :: ar(m,n),br(m,n) real x integer plan, ierr logical passing a = 1; ar = 1 ierr = cufftPlan2D(plan,m,n,CUFFT_C2C) ierr = ierr + cufftExecC2C(plan,a,b,CUFFT_FORWARD) ierr = ierr + cufftExecC2C(plan,b,b,CUFFT_INVERSE) ierr = ierr + cudaDeviceSynchronize() x = maxval(abs(a-b/(m*n))) write(*,*) 'Max error C2C: ', x passing = x .le. 1.0e-5 ierr = ierr + cufftPlan2D(plan,m,n,CUFFT_R2C) ierr = ierr + cufftExecR2C(plan,ar,b) ierr = ierr + cufftPlan2D(plan,m,n,CUFFT_C2R) ierr = ierr + cufftExecC2R(plan,b,br) ierr = ierr + cudaDeviceSynchronize() x = maxval(abs(ar-br/(m*n))) write(*,*) 'Max error R2C/C2R: ', x passing = passing .and. (x .le. 1.0e-5) ierr = ierr + cufftDestroy(plan) print *,ierr passing = passing .and. (ierr .eq. 0) if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end program cufft2dTest
6.7. Using cuRAND from OpenACC Host Code
This example demonstrates the use of the curand module, the curandHandle type, and several forms of rand calls.
Simple cuRAND Tests
program testcurand ! compile with the flags -ta=tesla -Mcuda -Mcudalib=curand call cur1(1000, .true.); call cur1(1000, .false.) call cur2(1000, .true.); call cur2(1000, .false.) call cur3(1000, .true.); call cur3(1000, .false.) end ! subroutine cur1(n, onhost) use curand integer :: a(n) type(curandGenerator) :: g integer(8) nbits logical onhost, passing a = 0 passing = .true. if (onhost) then istat = curandCreateGeneratorHost(g,CURAND_RNG_PSEUDO_XORWOW) istat = curandGenerate(g, a, n) istat = curandDestroyGenerator(g) else !$acc data copy(a) istat = curandCreateGenerator(g,CURAND_RNG_PSEUDO_XORWOW) !$acc host_data use_device(a) istat = curandGenerate(g, a, n) !$acc end host_data istat = curandDestroyGenerator(g) !$acc end data endif nbits = 0 do i = 1, n if (i.lt.10) print *,i,a(i) nbits = nbits + popcnt(a(i)) end do print *,"Should be roughly half the bits set" nbits = nbits / n if ((nbits .lt. 12) .or. (nbits .gt. 20)) then passing = .false. else print *,"nbits is ",nbits," which passes" endif if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end ! subroutine cur2(n, onhost) use curand real :: a(n) type(curandGenerator) :: g logical onhost, passing a = 0.0 passing = .true. if (onhost) then istat = curandCreateGeneratorHost(g,CURAND_RNG_PSEUDO_XORWOW) istat = curandGenerate(g, a, n) istat = curandDestroyGenerator(g) else !$acc data copy(a) istat = curandCreateGenerator(g,CURAND_RNG_PSEUDO_XORWOW) !$acc host_data use_device(a) istat = curandGenerate(g, a, n) !$acc end host_data istat = curandDestroyGenerator(g) !$acc end data endif print *,"Should be uniform around 0.5" do i = 1, n if (i.lt.10) print *,i,a(i) if ((a(i).lt.0.0) .or. (a(i).gt.1.0)) passing = .false. end do rmean = sum(a)/n if ((rmean .lt. 0.4) .or. (rmean .gt. 0.6)) then passing = .false. else print *,"Mean is ",rmean," which passes" endif if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end ! subroutine cur3(n, onhost) use curand real(8) :: a(n) type(curandGenerator) :: g logical onhost, passing a = 0.0d0 passing = .true. if (onhost) then istat = curandCreateGeneratorHost(g,CURAND_RNG_PSEUDO_XORWOW) istat = curandGenerate(g, a, n) istat = curandDestroyGenerator(g) else !$acc data copy(a) istat = curandCreateGenerator(g,CURAND_RNG_PSEUDO_XORWOW) !$acc host_data use_device(a) istat = curandGenerate(g, a, n) !$acc end host_data istat = curandDestroyGenerator(g) !$acc end data endif do i = 1, n if (i.lt.10) print *,i,a(i) if ((a(i).lt.0.0d0) .or. (a(i).gt.1.0d0)) passing = .false. end do rmean = sum(a)/n if ((rmean .lt. 0.4d0) .or. (rmean .gt. 0.6d0)) then passing = .false. else print *,"Mean is ",rmean," which passes" endif if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end
6.8. Using cuRAND from OpenACC Device Code
This example demonstrates the use of the curand_device module from a CUDA Fortran global subroutines.
Simple cuRAND Test from OpenACC Device Code
module mtests integer, parameter :: n = 1000 contains subroutine testrand( a, b ) use openacc_curand real :: a(n), b(n) type(curandStateXORWOW) :: h integer(8) :: seed, seq, offset !$acc parallel num_gangs(1) vector_length(1) copy(a,b) private(h) seed = 12345 seq = 0 offset = 0 call curand_init(seed, seq, offset, h) !$acc loop seq do i = 1, n a(i) = curand_uniform(h) b(i) = curand_normal(h) end do !$acc end parallel return end subroutine end module mtests program t use mtests real :: a(n), b(n), c(n) logical passing a = 1.0 b = 2.0 passing = .true. call testrand(a,b) c = a print *,"Should be uniform around 0.5" do i = 1, n if (i.lt.10) print *,i,c(i) if ((c(i).lt.0.0) .or. (c(i).gt.1.0)) passing = .false. end do rmean = sum(c)/n if ((rmean .lt. 0.4) .or. (rmean .gt. 0.6)) then passing = .false. else print *,"Mean is ",rmean," which passes" endif c = b print *,"Should be normal around 0.0" nc1 = 0; nc2 = 0; do i = 1, n if (i.lt.10) print *,i,c(i) if ((c(i) .gt. -4.0) .and. (c(i) .lt. 0.0)) nc1 = nc1 + 1 if ((c(i) .gt. 0.0) .and. (c(i) .lt. 4.0)) nc2 = nc2 + 1 end do print *,"Found on each side of zero ",nc1,nc2 if (abs(nc1-nc2) .gt. (n/10)) npassing = .false. rmean = sum(c,mask=abs(c).lt.4.0)/n if ((rmean .lt. -0.1) .or. (rmean .gt. 0.1)) then passing = .false. else print *,"Mean is ",rmean," which passes" endif if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end program
6.9. Using cuRAND from CUDA Fortran Host Code
This example demonstrates the use of the curand module, the curandHandle type, and several forms of rand calls.
Simple cuRAND Test
program testcurand1 call testr1(1000) call testr2(1000) call testr3(1000) end ! subroutine testr1(n) use cudafor use curand integer, managed :: a(n) type(curandGenerator) :: g integer(8) nbits logical passing a = 0 passing = .true. istat = curandCreateGenerator(g,CURAND_RNG_PSEUDO_XORWOW) istat = curandGenerate(g, a, n) istat = cudaDeviceSynchronize() istat = curandDestroyGenerator(g) nbits = 0 do i = 1, n if (i.lt.10) print *,i,a(i) nbits = nbits + popcnt(a(i)) end do print *,"Should be roughly half the bits set" nbits = nbits / n if ((nbits .lt. 12) .or. (nbits .gt. 20)) then passing = .false. else print *,"nbits is ",nbits," which passes" endif if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end ! subroutine testr2(n) use cudafor use curand real, managed :: a(n) type(curandGenerator) :: g logical passing a = 0.0 passing = .true. istat = curandCreateGenerator(g,CURAND_RNG_PSEUDO_XORWOW) istat = curandGenerate(g, a, n) istat = cudaDeviceSynchronize() istat = curandDestroyGenerator(g) print *,"Should be uniform around 0.5" do i = 1, n if (i.lt.10) print *,i,a(i) if ((a(i).lt.0.0) .or. (a(i).gt.1.0)) passing = .false. end do rmean = sum(a)/n if ((rmean .lt. 0.4) .or. (rmean .gt. 0.6)) then passing = .false. else print *,"Mean is ",rmean," which passes" endif if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end ! subroutine testr3(n) use cudafor use curand real(8), managed :: a(n) type(curandGenerator) :: g logical passing a = 0.0d0 passing = .true. istat = curandCreateGenerator(g,CURAND_RNG_PSEUDO_XORWOW) istat = curandGenerate(g, a, n) istat = cudaDeviceSynchronize() istat = curandDestroyGenerator(g) do i = 1, n if (i.lt.10) print *,i,a(i) if ((a(i).lt.0.0d0) .or. (a(i).gt.1.0d0)) passing = .false. end do rmean = sum(a)/n if ((rmean .lt. 0.4d0) .or. (rmean .gt. 0.6d0)) then passing = .false. else print *,"Mean is ",rmean," which passes" endif if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end end program
6.10. Using cuRAND from CUDA Fortran Device Code
This example demonstrates the use of the curand_device module from a CUDA Fortran global subroutines.
Simple cuRAND Test from Device Code
module mtests use curand_device integer, parameter :: n = 10000 contains attributes(global) subroutine testr( a, b ) real, device :: a(n), b(n) type(curandStateXORWOW) :: h integer(8) :: seed, seq, offset integer :: iam iam = threadIdx%x seed = iam*64 + 12345 seq = 0 offset = 0 call curand_init(seed, seq, offset, h) do i = iam, n, blockdim%x a(i) = curand_uniform(h) b(i) = curand_normal(h) end do return end subroutine end module mtests program t use mtests real, allocatable, device :: a(:), b(:) real c(n), rmean logical passing allocate(a(n)) allocate(b(n)) a = 0.0 b = 0.0 passing = .true. call testr<<<1,32>>> (a,b) c = a print *,"Should be uniform around 0.5" do i = 1, n if (i.lt.10) print *,i,c(i) if ((c(i).lt.0.0) .or. (c(i).gt.1.0)) passing = .false. end do rmean = sum(c)/n if ((rmean .lt. 0.4) .or. (rmean .gt. 0.6)) then passing = .false. else print *,"Mean is ",rmean," which passes" endif c = b print *,"Should be normal around 0.0" nc1 = 0; nc2 = 0; do i = 1, n if (i.lt.10) print *,i,c(i) if ((c(i) .gt. -4.0) .and. (c(i) .lt. 0.0)) nc1 = nc1 + 1 if ((c(i) .gt. 0.0) .and. (c(i) .lt. 4.0)) nc2 = nc2 + 1 end do print *,"Found on each side of zero ",nc1,nc2 if (abs(nc1-nc2) .gt. (n/10)) npassing = .false. rmean = sum(c,mask=abs(c).lt.4.0)/n if ((rmean .lt. -0.1) .or. (rmean .gt. 0.1)) then passing = .false. else print *,"Mean is ",rmean," which passes" endif if (passing) then print *,"Test PASSED" else print *,"Test FAILED" endif end end program
6.11. Using cuSPARSE from OpenACC Host Code
This example demonstrates the use of the cuSPARSE module, the cusparseHandle type, and several calls to the cuSPARSE library.
Simple BLAS Test
program sparseMatVec integer n n = 25 ! # rows/cols in dense matrix call sparseMatVecSub1(n) n = 45 ! # rows/cols in dense matrix call sparseMatVecSub1(n) end program subroutine sparseMatVecSub1(n) use openacc use cusparse implicit none integer n ! dense data real(4), allocatable :: Ade(:,:), x(:), y(:) ! sparse CSR arrays real(4), allocatable :: csrValA(:) integer, allocatable :: nnzPerRowA(:), csrRowPtrA(:), csrColIndA(:) allocate(Ade(n,n), x(n), y(n)) allocate(csrValA(n)) allocate(nnzPerRowA(n), csrRowPtrA(n+1), csrColIndA(n)) call sparseMatVecSub2(Ade, x, y, csrValA, nnzPerRowA, csrRowPtrA, & csrColIndA, n) deallocate(Ade) deallocate(x) deallocate(y) deallocate(csrValA) deallocate(nnzPerRowA) deallocate(csrRowPtrA) deallocate(csrColIndA) end subroutine subroutine sparseMatVecSub2(Ade, x, y, csrValA, nnzPerRowA, csrRowPtrA, & csrColIndA, n) use openacc use cusparse implicit none ! dense data real(4) :: Ade(n,n), x(n), y(n) ! sparse CSR arrays real(4) :: csrValA(n) integer :: nnzPerRowA(n), csrRowPtrA(n+1), csrColIndA(n) integer :: n, nnz, status, i type(cusparseHandle) :: h type(cusparseMatDescr) :: descrA ! parameters real(4) :: alpha, beta ! result real(4) :: xerr ! initalize CUSPARSE and matrix descriptor status = cusparseCreate(h) if (status /= CUSPARSE_STATUS_SUCCESS) & write(*,*) 'cusparseCreate error: ', status status = cusparseCreateMatDescr(descrA) status = cusparseSetMatType(descrA, & CUSPARSE_MATRIX_TYPE_GENERAL) status = cusparseSetMatIndexBase(descrA, & CUSPARSE_INDEX_BASE_ONE) status = cusparseSetStream(h, acc_get_cuda_stream(acc_async_sync)) !$acc data create(Ade, x, y, csrValA, nnzPerRowA, csrRowPtrA, csrColIndA) ! Initialize matrix (upper circular shift matrix) !$acc kernels Ade = 0.0 do i = 1, n-1 Ade(i,i+1) = 1.0 end do Ade(n,1) = 1.0 ! Initialize vectors and constants do i = 1, n x(i) = i enddo y = 0.0 !$acc end kernels !$acc update host(x) write(*,*) 'Original vector:' write(*,'(5(1x,f7.2))') x ! convert matrix from dense to csr format !$acc host_data use_device(Ade, nnzPerRowA, csrValA, csrRowPtrA, csrColIndA) status = cusparseSnnz_v2(h, CUSPARSE_DIRECTION_ROW, & n, n, descrA, Ade, n, nnzPerRowA, nnz) status = cusparseSdense2csr(h, n, n, descrA, Ade, n, & nnzPerRowA, csrValA, csrRowPtrA, csrColIndA) !$acc end host_data ! A is upper circular shift matrix ! y = alpha*A*x + beta*y alpha = 1.0 beta = 0.0 !$acc host_data use_device(csrValA, csrRowPtrA, csrColIndA, x, y) status = cusparseScsrmv(h, CUSPARSE_OPERATION_NON_TRANSPOSE, & n, n, n, alpha, descrA, csrValA, csrRowPtrA, & csrColIndA, x, beta, y) !$acc end host_data !$acc wait write(*,*) 'Shifted vector:' write(*,'(5(1x,f7.2))') y ! shift-down y and add original x ! A' is lower circular shift matrix ! x = alpha*A'*y + beta*x beta = -1.0 !$acc host_data use_device(csrValA, csrRowPtrA, csrColIndA, x, y) status = cusparseScsrmv(h, CUSPARSE_OPERATION_TRANSPOSE, & n, n, n, alpha, descrA, csrValA, csrRowPtrA, & csrColIndA, y, beta, x) !$acc end host_data !$acc kernels xerr = maxval(abs(x)) !$acc end kernels !$acc end data write(*,*) 'Max error = ', xerr if (xerr.le.1.e-5) then write(*,*) 'Test PASSED' else write(*,*) 'Test FAILED' endif end subroutine
6.12. Using cuSPARSE from CUDA Fortran Host Code
This example demonstrates the use of the cuSPARSE module, the cusparseHandle type, and several forms of cusparse calls.
Simple BLAS Test
program sparseMatVec use cudafor use cusparse implicit none integer, parameter :: n = 25 ! # rows/cols in dense matrix type(cusparseHandle) :: h type(cusparseMatDescr) :: descrA ! dense data real(4), managed :: Ade(n,n), x(n), y(n) ! sparse CSR arrays real(4), managed :: csrValA(n) integer, managed :: nnzPerRowA(n), & csrRowPtrA(n+1), csrColIndA(n) integer :: nnz, status, i ! parameters real(4) :: alpha, beta ! initalize CUSPARSE and matrix descriptor status = cusparseCreate(h) if (status /= CUSPARSE_STATUS_SUCCESS) & write(*,*) 'cusparseCreate error: ', status status = cusparseCreateMatDescr(descrA) status = cusparseSetMatType(descrA, & CUSPARSE_MATRIX_TYPE_GENERAL) status = cusparseSetMatIndexBase(descrA, & CUSPARSE_INDEX_BASE_ONE) ! Initialize matrix (upper circular shift matrix) Ade = 0.0 do i = 1, n-1 Ade(i,i+1) = 1.0 end do Ade(n,1) = 1.0 ! Initialize vectors and constants x = [(i,i=1,n)] y = 0.0 write(*,*) 'Original vector:' write(*,'(5(1x,f7.2))') x ! convert matrix from dense to csr format status = cusparseSnnz_v2(h, CUSPARSE_DIRECTION_ROW, & n, n, descrA, Ade, n, nnzPerRowA, nnz) status = cusparseSdense2csr(h, n, n, descrA, Ade, n, & nnzPerRowA, csrValA, csrRowPtrA, csrColIndA) ! A is upper circular shift matrix ! y = alpha*A*x + beta*y alpha = 1.0 beta = 0.0 status = cusparseScsrmv(h, CUSPARSE_OPERATION_NON_TRANSPOSE, & n, n, n, alpha, descrA, csrValA, csrRowPtrA, & csrColIndA, x, beta, y) ! shift-down y and add original x ! A' is lower circular shift matrix ! x = alpha*A'*y + beta*x beta = -1.0 status = cusparseScsrmv(h, CUSPARSE_OPERATION_TRANSPOSE, & n, n, n, alpha, descrA, csrValA, csrRowPtrA, & csrColIndA, y, beta, x) status = cudaDeviceSynchronize() write(*,*) 'Shifted vector:' write(*,'(5(1x,f7.2))') y write(*,*) 'Max error = ', maxval(abs(x)) if (maxval(abs(x)).le.1.e-5) then write(*,*) 'Test PASSED' else write(*,*) 'Test FAILED' endif end program sparseMatVec
7. Contact Information
You can contact PGI at:
- 20400 NW Amberwood Drive Suite 100
- Beaverton, OR 97006
Or electronically using any of the following means:
- Fax: +1-503-682-2637
- Sales: mailto: sales@pgroup.com
- WWW: https://www.pgroup.com
The PGI User Forum is monitored by members of the PGI engineering and support teams as well as other PGI customers. The forums contain answers to many commonly asked questions. Log in to the PGI website to access the forums.
Many questions and problems can be resolved by following instructions and the information available in the PGI frequently asked questions (FAQ).
Submit support requests using the PGI Technical Support Request form.
Notices
Notice
ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, "MATERIALS") ARE BEING PROVIDED "AS IS." NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.
Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication of otherwise under any patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. NVIDIA Corporation products are not authorized as critical components in life support devices or systems without express written approval of NVIDIA Corporation.
Trademarks
NVIDIA, the NVIDIA logo, Cluster Development Kit, PGC++, PGCC, PGDBG, PGF77, PGF90, PGF95, PGFORTRAN, PGHPF, PGI, PGI Accelerator, PGI CDK, PGI Server, PGI Unified Binary, PGI Visual Fortran, PGI Workstation, PGPROF, PGROUP, PVF, and The Portland Group are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.