cuSOLVER
The API reference guide for cuSOLVER, a GPU accelerated library for decompositions and linear system solutions for both dense and sparse matrices.
1. Introduction
 The cuSolver API on a single GPU
 The cuSolverMG API on a single node multiGPU
The intent of cuSolver is to provide useful LAPACKlike features, such as common matrix factorization and triangular solve routines for dense matrices, a sparse leastsquares solver and an eigenvalue solver. In addition cuSolver provides a new refactorization library useful for solving sequences of matrices with a shared sparsity pattern.
cuSolver combines three separate components under a single umbrella. The first part of cuSolver is called cuSolverDN, and deals with dense matrix factorization and solve routines such as LU, QR, SVD and LDLT, as well as useful utilities such as matrix and vector permutations.
Next, cuSolverSP provides a new set of sparse routines based on a sparse QR factorization. Not all matrices have a good sparsity pattern for parallelism in factorization, so the cuSolverSP library also provides a CPU path to handle those sequentiallike matrices. For those matrices with abundant parallelism, the GPU path will deliver higher performance. The library is designed to be called from C and C++.
The final part is cuSolverRF, a sparse refactorization package that can provide very good performance when solving a sequence of matrices where only the coefficients are changed but the sparsity pattern remains the same.
The GPU path of the cuSolver library assumes data is already in the device memory. It is the responsibility of the developer to allocate memory and to copy data between GPU memory and CPU memory using standard CUDA runtime API routines, such as cudaMalloc(), cudaFree(), cudaMemcpy(), and cudaMemcpyAsync().
cuSolverMg is GPUaccelerated ScaLAPACK. By now, cuSolverMg supports 1D column block cyclic layout and provides symmetric eigenvalue solver.
1.1. cuSolverDN: Dense LAPACK
The cuSolverDN library was designed to solve dense linear systems of the form
$Ax=b$ 
where the coefficient matrix $A\in {R}^{\mathrm{nxn}}$ , righthandside vector $b\in {R}^{n}$ and solution vector $x\in {R}^{n}$
The cuSolverDN library provides QR factorization and LU with partial pivoting to handle a general matrix A, which may be nonsymmetric. Cholesky factorization is also provided for symmetric/Hermitian matrices. For symmetric indefinite matrices, we provide BunchKaufman (LDL) factorization.
The cuSolverDN library also provides a helpful bidiagonalization routine and singular value decomposition (SVD).
The cuSolverDN library targets computationallyintensive and popular routines in LAPACK, and provides an API compatible with LAPACK. The user can accelerate these timeconsuming routines with cuSolverDN and keep others in LAPACK without a major change to existing code.
1.2. cuSolverSP: Sparse LAPACK
The cuSolverSP library was mainly designed to a solve sparse linear system
$Ax=b$ 
and the leastsquares problem
$x=\mathrm{argmin}\mathrm{}A*zb\mathrm{}$ 
where sparse matrix $A\in {R}^{\mathrm{mxn}}$ , righthandside vector $b\in {R}^{m}$ and solution vector $x\in {R}^{n}$ . For a linear system, we require m=n.
The core algorithm is based on sparse QR factorization. The matrix A is accepted in CSR format. If matrix A is symmetric/Hermitian, the user has to provide a full matrix, ie fill missing lower or upper part.
If matrix A is symmetric positive definite and the user only needs to solve $Ax=b$ , Cholesky factorization can work and the user only needs to provide the lower triangular part of A.
On top of the linear and leastsquares solvers, the cuSolverSP library provides a simple eigenvalue solver based on shiftinverse power method, and a function to count the number of eigenvalues contained in a box in the complex plane.
1.3. cuSolverRF: Refactorization
The cuSolverRF library was designed to accelerate solution of sets of linear systems by fast refactorization when given new coefficients in the same sparsity pattern
${A}_{i}{x}_{i}={f}_{i}$ 
where a sequence of coefficient matrices ${A}_{i}\in {R}^{\mathrm{nxn}}$ , righthandsides ${f}_{i}\in {R}^{n}$ and solutions ${x}_{i}\in {R}^{n}$ are given for i=1,...,k.
The cuSolverRF library is applicable when the sparsity pattern of the coefficient matrices ${A}_{i}$ as well as the reordering to minimize fillin and the pivoting used during the LU factorization remain the same across these linear systems. In that case, the first linear system (i=1) requires a full LU factorization, while the subsequent linear systems (i=2,...,k) require only the LU refactorization. The later can be performed using the cuSolverRF library.
Notice that because the sparsity pattern of the coefficient matrices, the reordering and pivoting remain the same, the sparsity pattern of the resulting triangular factors ${L}_{i}$ and ${U}_{i}$ also remains the same. Therefore, the real difference between the full LU factorization and LU refactorization is that the required memory is known ahead of time.
1.4. Naming Conventions
The cuSolverDN library provides two different APIs; legacy and generic.
The functions in the legacy API are available for data types float, double, cuComplex, and cuDoubleComplex. The naming convention for the legacy API is as follows:
cusolverDn<t><operation> 
where <t> can be S, D, C, Z, or X, corresponding to the data types float, double, cuComplex, cuDoubleComplex, and the generic type, respectively. <operation> can be Cholesky factorization (potrf), LU with partial pivoting (getrf), QR factorization (geqrf) and BunchKaufman factorization (sytrf).
The functions in the generic API provide a single entry point for each routine and support for 64bit integers to define matrix and vector dimensions. The naming convention for the generic API is dataagnostic and is as follows:
cusolverDn<operation> 
where <operation> can be Cholesky factorization (potrf), LU with partial pivoting (getrf) and QR factorization (geqrf).
The cuSolverSP library functions are available for data types float, double, cuComplex, and cuDoubleComplex. The naming convention is as follows:
cusolverSp[Host]<t>[<matrix data format>]<operation>[<output matrix data format>]<based on> 
where cuSolverSp is the GPU path and cusolverSpHost is the corresponding CPU path. <t> can be S, D, C, Z, or X, corresponding to the data types float, double, cuComplex, cuDoubleComplex, and the generic type, respectively.
The <matrix data format> is csr, compressed sparse row format.
The <operation> can be ls, lsq, eig, eigs, corresponding to linear solver, leastsquare solver, eigenvalue solver and number of eigenvalues in a box, respectively.
The <output matrix data format> can be v or m, corresponding to a vector or a matrix.
<based on> describes which algorithm is used. For example, qr (sparse QR factorization) is used in linear solver and leastsquare solver.
All of the functions have the return type cusolverStatus_t and are explained in more detail in the chapters that follow.
Routine  Data format  Operation  Output format  Based on 

csrlsvlu  csr  linear solver (ls)  vector (v)  LU (lu) with partial pivoting 
csrlsvqr  csr  linear solver (ls)  vector (v)  QR factorization (qr) 
csrlsvchol  csr  linear solver (ls)  vector (v)  Cholesky factorization (chol) 
csrlsqvqr  csr  leastsquare solver (lsq)  vector (v)  QR factorization (qr) 
csreigvsi  csr  eigenvalue solver (eig)  vector (v)  shiftinverse 
csreigs  csr  number of eigenvalues in a box (eigs)  
csrsymrcm  csr  Symmetric Reverse CuthillMcKee (symrcm) 
The cuSolverRF library routines are available for data type double. Most of the routines follow the naming convention:
cusolverRf_<operation>_[[Host]](...) 
where the trailing optional Host qualifier indicates the data is accessed on the host versus on the device, which is the default. The <operation> can be Setup, Analyze, Refactor, Solve, ResetValues, AccessBundledFactors and ExtractSplitFactors.
Finally, the return type of the cuSolverRF library routines is cusolverStatus_t.
1.5. Asynchronous Execution
The cuSolver library functions prefer to keep asynchronous execution as much as possible. Developers can always use the cudaDeviceSynchronize() function to ensure that the execution of a particular cuSolver library routine has completed.
A developer can also use the cudaMemcpy() routine to copy data from the device to the host and vice versa, using the cudaMemcpyDeviceToHost and cudaMemcpyHostToDevice parameters, respectively. In this case there is no need to add a call to cudaDeviceSynchronize() because the call to cudaMemcpy() with the above parameters is blocking and completes only when the results are ready on the host.
1.6. Library Property
The libraryPropertyType data type is an enumeration of library property types. (ie. CUDA version X.Y.Z would yield MAJOR_VERSION=X, MINOR_VERSION=Y, PATCH_LEVEL=Z)
typedef enum libraryPropertyType_t { MAJOR_VERSION, MINOR_VERSION, PATCH_LEVEL } libraryPropertyType;
The following code can show the version of cusolver library.
int major=1,minor=1,patch=1; cusolverGetProperty(MAJOR_VERSION, &major); cusolverGetProperty(MINOR_VERSION, &minor); cusolverGetProperty(PATCH_LEVEL, &patch); printf("CUSOLVER Version (Major,Minor,PatchLevel): %d.%d.%d\n", major,minor,patch);
1.7. High Precision Package
The cusolver library uses high precision for iterative refinement when necessary.
2. Using the CUSOLVER API
2.1.1. Thread Safety
The library is threadsafe, and its functions can be called from multiple host threads.
2.1.2. Scalar Parameters
In the cuSolver API, the scalar parameters can be passed by reference on the host.
2.1.3. Parallelism with Streams
If the application performs several small independent computations, or if it makes data transfers in parallel with the computation, then CUDA streams can be used to overlap these tasks.
 Create CUDA streams using the function cudaStreamCreate(), and
 Set the stream to be used by each individual cuSolver library routine by calling, for example, cusolverDnSetStream(), just prior to calling the actual cuSolverDN routine.
The computations performed in separate streams would then be overlapped automatically on the GPU, when possible. This approach is especially useful when the computation performed by a single task is relatively small, and is not enough to fill the GPU with work, or when there is a data transfer that can be performed in parallel with the computation.
2.1.4. How to Link cusolver Library
cusolver library provides dynamic library libcusolver.so and static library libcusolver_static.a. If the user links the application with libcusolver.so, libcublas.so and libcublasLt.so are also required. If the user links the application with libcusolver_static.a, the following libraries are also needed, libcudart_static.a, libculibos.aliblapack_static.a, libmetis_static.a, libcublas_static.a and libcusparse_static.a.
2.1.5. Link Thirdparty LAPACK Library
 If you use libcusolver_static.a, then you must link with liblapack_static.a explicitly, otherwise the linker will report missing symbols. No conflict of symbols between liblapack_static.a and other thirdparty LAPACK library, you are free to link the latter to your application.
 The liblapack_static.a is built inside libcusolver.so. Hence, if you use libcusolver.so, then you don't need to specify a LAPACK library. The libcusolver.so will not pick up any routines from the thirdparty LAPACK library even you link the application with it.
2.1.6. Convention of info
Each LAPACK routine returns an info which indicates the position of invalid parameter. If info = i, then ith parameter is invalid. To be consistent with base1 in LAPACK, cusolver does not report invalid handle into info. Instead, cusolver returns CUSOLVER_STATUS_NOT_INITIALIZED for invalid handle.
2.1.7. Usage of _bufferSize
There is no cudaMalloc inside cuSolver library, the user must allocate the device workspace explicitly. The routine xyz_bufferSize is to query the size of workspace of the routine xyz, for example xyz = potrf. To make the API simple, xyz_bufferSize follows almost the same signature of xyz even it only depends on some parameters, for example, device pointer is not used to decide the size of workspace. In most cases, xyz_bufferSize is called in the beginning before actual device data (pointing by a device pointer) is prepared or before the device pointer is allocated. In such case, the user can pass null pointer to xyz_bufferSize without breaking the functionality.
2.2. cuSolver Types Reference
2.2.1. cuSolverDN Types
The float, double, cuComplex, and cuDoubleComplex data types are supported. The first two are standard C data types, while the last two are exported from cuComplex.h. In addition, cuSolverDN uses some familiar types from cuBLAS.
2.2.1.1. cusolverDnHandle_t
This is a pointer type to an opaque cuSolverDN context, which the user must initialize by calling cusolverDnCreate() prior to calling any other library function. An uninitialized Handle object will lead to unexpected behavior, including crashes of cuSolverDN. The handle created and returned by cusolverDnCreate() must be passed to every cuSolverDN function.
2.2.1.2. cublasFillMode_t
The type indicates which part (lower or upper) of the dense matrix was filled and consequently should be used by the function. Its values correspond to Fortran characters ‘L’ or ‘l’ (lower) and ‘U’ or ‘u’ (upper) that are often used as parameters to legacy BLAS implementations.
Value  Meaning 

CUBLAS_FILL_MODE_LOWER  The lower part of the matrix is filled. 
CUBLAS_FILL_MODE_UPPER  The upper part of the matrix is filled. 
2.2.1.3. cublasOperation_t
The cublasOperation_t type indicates which operation needs to be performed with the dense matrix. Its values correspond to Fortran characters ‘N’ or ‘n’ (nontranspose), ‘T’ or ‘t’ (transpose) and ‘C’ or ‘c’ (conjugate transpose) that are often used as parameters to legacy BLAS implementations.
Value  Meaning 

CUBLAS_OP_N  The nontranspose operation is selected. 
CUBLAS_OP_T  The transpose operation is selected. 
CUBLAS_OP_C  The conjugate transpose operation is selected. 
2.2.1.4. cusolverEigType_t
The cusolverEigType_t type indicates which type of eigenvalue the solver is. Its values correspond to Fortran integer 1 (A*x = lambda*B*x), 2 (A*B*x = lambda*x), 3 (B*A*x = lambda*x), used as parameters to legacy LAPACK implementations.
Value  Meaning 

CUSOLVER_EIG_TYPE_1  A*x = lambda*B*x 
CUSOLVER_EIG_TYPE_2  A*B*x = lambda*x 
CUSOLVER_EIG_TYPE_3  B*A*x = lambda*x 
2.2.1.5. cusolverEigMode_t
The cusolverEigMode_t type indicates whether or not eigenvectors are computed. Its values correspond to Fortran character 'N' (only eigenvalues are computed), 'V' (both eigenvalues and eigenvectors are computed) used as parameters to legacy LAPACK implementations.
Value  Meaning 

CUSOLVER_EIG_MODE_NOVECTOR  Only eigenvalues are computed. 
CUSOLVER_EIG_MODE_VECTOR  Both eigenvalues and eigenvectors are computed. 
2.2.1.6. cusolverIRSRefinement_t
The cusolverIRSRefinement_t type indicates which solver type would be used for the specific cusolver function. Most of our experimentation shows that CUSOLVER_IRS_REFINE_GMRES is the best option.
More details about the refinement process can be found in Azzam Haidar, Stanimire Tomov, Jack Dongarra, and Nicholas J. Higham. 2018. Harnessing GPU tensor cores for fast FP16 arithmetic to speed up mixedprecision iterative refinement solvers. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC '18). IEEE Press, Piscataway, NJ, USA, Article 47, 11 pages.
Value  Meaning 

CUSOLVER_IRS_REFINE_NOT_SET  Solver is not set; this value is what is set when creating the params structure. IRS solver will return an error. 
CUSOLVER_IRS_REFINE_NONE  No refinement solver, the IRS solver performs a factorisation followed by a solve without any refinement. For example if the IRS solver was cusolverDnIRSXgesv(), this is equivalent to a Xgesv routine without refinement and where the factorisation is carried out in the lowest precision. If for example the main precision was CUSOLVER_R_64F and the lowest was CUSOLVER_R_64F as well, then this is equivalent to a call to cusolverDnDgesv(). 
CUSOLVER_IRS_REFINE_CLASSICAL  Classical iterative refinement solver. Similar to the one used in LAPACK routines. 
CUSOLVER_IRS_REFINE_GMRES  GMRES (Generalized Minimal Residual) based iterative refinement solver. In recent study, the GMRES method has drawn the scientific community attention for its ability to be used as refinement solver that outperforms the classical iterative refinement method. based on our experimentation, we recommend this setting. 
CUSOLVER_IRS_REFINE_CLASSICAL_GMRES  Classical iterative refinement solver that uses the GMRES (Generalized Minimal Residual) internally to solve the correction equation at each iteration. We call the classical refinement iteration the outer iteration while the GMRES is called inner iteration. Note that if the tolerance of the inner GMRES is set very low, lets say to machine precision, then the outer classical refinement iteration will performs only one iteration and thus this option will behave like CUSOLVER_IRS_REFINE_GMRES. 
CUSOLVER_IRS_REFINE_GMRES_GMRES  Similar to CUSOLVER_IRS_REFINE_CLASSICAL_GMRES which consists of classical refinement process that uses GMRES to solve the inner correction system; here it is a GMRES (Generalized Minimal Residual) based iterative refinement solver that uses another GMRES internally to solve the preconditioned system. 
2.2.1.7. cusolverDnIRSParams_t
This is a pointer type to an opaque cusolverDnIRSParams_t structure, which holds parameters for the iterative refinement linear solvers such as cusolverDnXgesv(). Use corresponding helper functions described below to either Create/Destroy this structure or Set/Get solver parameters.
2.2.1.8. cusolverDnIRSInfos_t
This is a pointer type to an opaque cusolverDnIRSInfos_t structure, which holds information about the performed call to an iterative refinement linear solver (e.g., cusolverDnXgesv()). Use corresponding helper functions described below to either Create/Destroy this structure or retrieve solve information.
2.2.1.9. cusolverDnFunction_t
The cusolverDnFunction_t type indicates which routine needs to be configured by cusolverDnSetAdvOptions(). The value CUSOLVERDN_GETRF corresponds to the routine Getrf.
Value  Meaning 

CUSOLVERDN_GETRF  Corresponds to Getrf. 
2.2.1.10. cusolverAlgMode_t
The cusolverAlgMode_t type indicates which algorithm is selected by cusolverDnSetAdvOptions(). The set of algorithms supported for each routine is described in detail along with the routine's documentation.
The default algorithm is CUSOLVER_ALG_0. The user can also provide NULL to use the default algorithm.
2.2.2. cuSolverSP Types
The float, double, cuComplex, and cuDoubleComplex data types are supported. The first two are standard C data types, while the last two are exported from cuComplex.h.
2.2.2.1. cusolverSpHandle_t
This is a pointer type to an opaque cuSolverSP context, which the user must initialize by calling cusolverSpCreate() prior to calling any other library function. An uninitialized Handle object will lead to unexpected behavior, including crashes of cuSolverSP. The handle created and returned by cusolverSpCreate() must be passed to every cuSolverSP function.
2.2.2.2. cusparseMatDescr_t
We have chosen to keep the same structure as exists in cuSparse to describe the shape and properties of a matrix. This enables calls to either cuSPARSE or cuSOLVER using the same matrix description.
typedef struct { cusparseMatrixType_t MatrixType; cusparseFillMode_t FillMode; cusparseDiagType_t DiagType; cusparseIndexBase_t IndexBase; } cusparseMatDescr_t;
Please read documenation of the cuSPARSE Library to understand each field of cusparseMatDescr_t.
2.2.2.3. cusolverStatus_t
This is a status type returned by the library functions and it can have the following values.
CUSOLVER_STATUS_SUCCESS 
The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED 
The cuSolver library was not initialized. This is usually caused by the lack of a prior call, an error in the CUDA Runtime API called by the cuSolver routine, or an error in the hardware setup. To correct: call cusolverCreate() prior to the function call; and check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed. 
CUSOLVER_STATUS_ALLOC_FAILED 
Resource allocation failed inside the cuSolver library. This is usually caused by a cudaMalloc() failure. To correct: prior to the function call, deallocate previously allocated memory as much as possible. 
CUSOLVER_STATUS_INVALID_VALUE 
An unsupported value or parameter was passed to the function (a negative vector size, for example). To correct: ensure that all the parameters being passed have valid values. 
CUSOLVER_STATUS_ARCH_MISMATCH 
The function requires a feature absent from the device architecture; usually caused by the lack of support for atomic operations or double precision. To correct: compile and run the application on a device with compute capability 2.0 or above. 
CUSOLVER_STATUS_EXECUTION_FAILED 
The GPU program failed to execute. This is often caused by a launch failure of the kernel on the GPU, which can be caused by multiple reasons. To correct: check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed. 
CUSOLVER_STATUS_INTERNAL_ERROR 
An internal cuSolver operation failed. This error is usually caused by a cudaMemcpyAsync() failure. To correct: check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed. Also, check that the memory passed as a parameter to the routine is not being deallocated prior to the routine’s completion. 
CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED 
The matrix type is not supported by this function. This is usually caused by passing an invalid matrix descriptor to the function. To correct: check that the fields in descrA were set correctly. 
2.2.3. cuSolverRF Types
cuSolverRF only supports double.
2.2.3.1. cusolverRfHandle_t
The cusolverRfHandle_t is a pointer to an opaque data structure that contains the cuSolverRF library handle. The user must initialize the handle by calling cusolverRfCreate() prior to any other cuSolverRF library calls. The handle is passed to all other cuSolverRF library calls.
2.2.3.2. cusolverRfMatrixFormat_t
The cusolverRfMatrixFormat_t is an enum that indicates the input/output matrix format assumed by the cusolverRfSetupDevice(), cusolverRfSetupHost(), cusolverRfResetValues(), cusolveRfExtractBundledFactorsHost() and cusolverRfExtractSplitFactorsHost() routines.
Value  Meaning 

CUSOLVER_MATRIX_FORMAT_CSR  Matrix format CSR is assumed. (default) 
CUSOLVER_MATRIX_FORMAT_CSC  Matrix format CSC is assumed. 
2.2.3.3. cusolverRfNumericBoostReport_t
The cusolverRfNumericBoostReport_t is an enum that indicates whether numeric boosting (of the pivot) was used during the cusolverRfRefactor() and cusolverRfSolve() routines. The numeric boosting is disabled by default.
Value  Meaning 

CUSOLVER_NUMERIC_BOOST_NOT_USED  Numeric boosting not used. (default) 
CUSOLVER_NUMERIC_BOOST_USED  Numeric boosting used. 
2.2.3.4. cusolverRfResetValuesFastMode_t
The cusolverRfResetValuesFastMode_t is an enum that indicates the mode used for the cusolverRfResetValues() routine. The fast mode requires extra memory and is recommended only if very fast calls to cusolverRfResetValues() are needed.
Value  Meaning 

CUSOLVER_RESET_VALUES_FAST_MODE_OFF  Fast mode disabled. (default) 
CUSOLVER_RESET_VALUES_FAST_MODE_ON  Ffast mode enabled. 
2.2.3.5. cusolverRfFactorization_t
The cusolverRfFactorization_t is an enum that indicates which (internal) algorithm is used for refactorization in the cusolverRfRefactor() routine.
Value  Meaning 

CUSOLVER_FACTORIZATION_ALG0  Algorithm 0. (default) 
CUSOLVER_FACTORIZATION_ALG1  Algorithm 1. 
CUSOLVER_FACTORIZATION_ALG2  Algorithm 2. Dominobased scheme. 
2.2.3.6. cusolverRfTriangularSolve_t
The cusolverRfTriangularSolve_t is an enum that indicates which (internal) algorithm is used for triangular solve in the cusolverRfSolve() routine.
Value  Meaning 

CUSOLVER_TRIANGULAR_SOLVE_ALG1  Algorithm 1. (default) 
CUSOLVER_TRIANGULAR_SOLVE_ALG2  Algorithm 2. Dominobased scheme. 
CUSOLVER_TRIANGULAR_SOLVE_ALG3  Aalgorithm 3. Dominobased scheme. 
2.2.3.7. cusolverRfUnitDiagonal_t
The cusolverRfUnitDiagonal_t is an enum that indicates whether and where the unit diagonal is stored in the input/output triangular factors in the cusolverRfSetupDevice(), cusolverRfSetupHost() and cusolverRfExtractSplitFactorsHost() routines.
Value  Meaning 

CUSOLVER_UNIT_DIAGONAL_STORED_L  Unit diagonal is stored in lower triangular factor. (default) 
CUSOLVER_UNIT_DIAGONAL_STORED_U  Unit diagonal is stored in upper triangular factor. 
CUSOLVER_UNIT_DIAGONAL_ASSUMED_L  Unit diagonal is assumed in lower triangular factor. 
CUSOLVER_UNIT_DIAGONAL_ASSUMED_U  Unit diagonal is assumed in upper triangular factor. 
2.2.3.8. cusolverStatus_t
The cusolverStatus_t is an enum that indicates success or failure of the cuSolverRF library call. It is returned by all the cuSolver library routines, and it uses the same enumerated values as the sparse and dense Lapack routines.
2.3. cuSolver Formats Reference
2.3.1. Index Base Format
The CSR or CSC format requires either zerobased or onebased index for a sparse matrix A. The GLU library supports only zerobased indexing. Otherwise, both onebased and zerobased indexing are supported in cuSolver.
2.3.2. Vector (Dense) Format
The vectors are assumed to be stored linearly in memory. For example, the vector
$x=\left(\begin{array}{c}{x}_{1}\\ {x}_{2}\\ \vdots \\ {x}_{\mathrm{n}}\end{array}\right)$ 
is represented as
$\left(\begin{array}{cccc}{x}_{1}& {x}_{2}& \dots & {x}_{\mathrm{n}}\end{array}\right)$ 
2.3.3. Matrix (Dense) Format
The dense matrices are assumed to be stored in columnmajor order in memory. The submatrix can be accessed using the leading dimension of the original matrix. For examle, the m*n (sub)matrix
$\left(\begin{array}{ccc}{a}_{1,1}& \dots & {a}_{1,n}\\ {a}_{2,1}& \dots & {a}_{2,n}\\ \vdots \\ {a}_{m,1}& \dots & {a}_{m,n}\end{array}\right)$ 
is represented as
$\left(\begin{array}{ccc}{a}_{1,1}& \dots & {a}_{1,n}\\ {a}_{2,1}& \dots & {a}_{2,n}\\ \vdots & \ddots & \vdots \\ {a}_{m,1}& \dots & {a}_{m,n}\\ \vdots & \ddots & \vdots \\ {a}_{\mathrm{lda},1}& \dots & {a}_{\mathrm{lda},n}\end{array}\right)$ 
with its elements arranged linearly in memory as
$\left(\begin{array}{ccccccccccccc}{a}_{1,1}& {a}_{2,1}& \dots & {a}_{m,1}& \dots & {a}_{\mathrm{lda},1}& \dots & {a}_{1,n}& {a}_{2,n}& \dots & {a}_{m,n}& \dots & {a}_{\mathrm{lda},n}\end{array}\right)$ 
where lda ≥ m is the leading dimension of A.
2.3.4. Matrix (CSR) Format
In CSR format the matrix is represented by the following parameters:
Parameter  Type  Size  Meaning 

n  (int)  The number of rows (and columns) in the matrix.  
nnz  (int)  The number of nonzero elements in the matrix.  
csrRowPtr  (int *)  n+1  The array of offsets corresponding to the start of each row in the arrays csrColInd and csrVal. This array has also an extra entry at the end that stores the number of nonzero elements in the matrix. 
csrColInd  (int *)  nnz  The array of column indices corresponding to the nonzero elements in the matrix. It is assumed that this array is sorted by row and by column within each row. 
csrVal  (SDCZ)*  nnz  The array of values corresponding to the nonzero elements in the matrix. It is assumed that this array is sorted by row and by column within each row. 
Note that in our CSR format, sparse matrices are assumed to be stored in rowmajor order, in other words, the index arrays are first sorted by row indices and then within each row by column indices. Also it is assumed that each pair of row and column indices appears only once.
For example, the 4x4 matrix
$A=\left(\begin{array}{cccc}\mathrm{1.0}& \mathrm{3.0}& \mathrm{0.0}& \mathrm{0.0}\\ \mathrm{0.0}& \mathrm{4.0}& \mathrm{6.0}& \mathrm{0.0}\\ \mathrm{2.0}& \mathrm{5.0}& \mathrm{7.0}& \mathrm{8.0}\\ \mathrm{0.0}& \mathrm{0.0}& \mathrm{0.0}& \mathrm{9.0}\end{array}\right)$ 
is represented as
$\mathrm{csrRowPtr}=\left(\begin{array}{ccccc}0& 2& 4& 8& 9\end{array}\right)$ 
$\mathrm{csrColInd}=\left(\begin{array}{ccccccccc}0& 1& 1& 2& 0& 1& 2& 3& 3\end{array}\right)$ 
$\mathrm{csrVal}=\left(\begin{array}{ccccccccc}1.0& 3.0& 4.0& 6.0& 2.0& 5.0& 7.0& 8.0& 9.0\end{array}\right)$ 
2.3.5. Matrix (CSC) Format
In CSC format the matrix is represented by the following parameters:
Parameter  Type  Size  Meaning 

n  (int)  The number of rows (and columns) in the matrix.  
nnz  (int)  The number of nonzero elements in the matrix.  
cscColPtr  (int *)  n+1  The array of offsets corresponding to the start of each column in the arrays cscRowInd and cscVal. This array has also an extra entry at the end that stores the number of nonzero elements in the matrix. 
cscRowInd  (int *)  nnz  The array of row indices corresponding to the nonzero elements in the matrix. It is assumed that this array is sorted by column and by row within each column. 
cscVal  (SDCZ)*  nnz  The array of values corresponding to the nonzero elements in the matrix. It is assumed that this array is sorted by column and by row within each column. 
Note that in our CSC format, sparse matrices are assumed to be stored in columnmajor order, in other words, the index arrays are first sorted by column indices and then within each column by row indices. Also it is assumed that each pair of row and column indices appears only once.
For example, the 4x4 matrix
$A=\left(\begin{array}{cccc}\mathrm{1.0}& \mathrm{3.0}& \mathrm{0.0}& \mathrm{0.0}\\ \mathrm{0.0}& \mathrm{4.0}& \mathrm{6.0}& \mathrm{0.0}\\ \mathrm{2.0}& \mathrm{5.0}& \mathrm{7.0}& \mathrm{8.0}\\ \mathrm{0.0}& \mathrm{0.0}& \mathrm{0.0}& \mathrm{9.0}\end{array}\right)$ 
is represented as
$\mathrm{cscColPtr}=\left(\begin{array}{ccccc}0& 2& 5& 7& 9\end{array}\right)$ 
$\mathrm{cscRowInd}=\left(\begin{array}{ccccccccc}0& 2& 0& 1& 2& 1& 2& 2& 3\end{array}\right)$ 
$\mathrm{cscVal}=\left(\begin{array}{ccccccccc}1.0& 2.0& 3.0& 4.0& 5.0& 6.0& 7.0& 8.0& 9.0\end{array}\right)$ 
2.4. cuSolverDN: dense LAPACK Function Reference
This section describes the API of cuSolverDN, which provides a subset of dense LAPACK functions.
2.4.1. cuSolverDN Helper Function Reference
The cuSolverDN helper functions are described in this section.
2.4.1.1. cusolverDnCreate()
cusolverStatus_t cusolverDnCreate(cusolverDnHandle_t *handle);
This function initializes the cuSolverDN library and creates a handle on the cuSolverDN context. It must be called before any other cuSolverDN API function is invoked. It allocates hardware resources necessary for accessing the GPU.
Parameter  Memory  In/out  Meaning 

handle  host  output  The pointer to the handle to the cuSolverDN context. 
CUSOLVER_STATUS_SUCCESS  The initialization succeeded. 
CUSOLVER_STATUS_NOT_INITIALIZED  The CUDA Runtime initialization failed. 
CUSOLVER_STATUS_ALLOC_FAILED  The resources could not be allocated. 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
2.4.1.2. cusolverDnDestroy()
cusolverStatus_t cusolverDnDestroy(cusolverDnHandle_t handle);
This function releases CPUside resources used by the cuSolverDN library.
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
CUSOLVER_STATUS_SUCCESS  The shutdown succeeded. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
2.4.1.3. cusolverDnSetStream()
cusolverStatus_t cusolverDnSetStream(cusolverDnHandle_t handle, cudaStream_t streamId)
This function sets the stream to be used by the cuSolverDN library to execute its routines.
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
streamId  host  input  The stream to be used by the library. 
CUSOLVER_STATUS_SUCCESS  The stream was set successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
2.4.1.4. cusolverDnGetStream()
cusolverStatus_t cusolverDnGetStream(cusolverDnHandle_t handle, cudaStream_t *streamId)
This function sets the stream to be used by the cuSolverDN library to execute its routines.
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
streamId  host  output  The stream to be used by the library. 
CUSOLVER_STATUS_SUCCESS  The stream was set successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
2.4.1.5. cusolverDnCreateSyevjInfo()
cusolverStatus_t cusolverDnCreateSyevjInfo( syevjInfo_t *info);
This function creates and initializes the structure of syevj, syevjBatched and sygvj to default values.
Parameter  Memory  In/out  Meaning 

info  host  output  The pointer to the structure of syevj. 
CUSOLVER_STATUS_SUCCESS  The structure was initialized successfully. 
CUSOLVER_STATUS_ALLOC_FAILED  The resources could not be allocated. 
2.4.1.6. cusolverDnDestroySyevjInfo()
cusolverStatus_t cusolverDnDestroySyevjInfo( syevjInfo_t info);
This function destroys and releases any memory required by the structure.
Parameter  Memory  In/out  Meaning 

info  host  input  The structure of syevj. 
CUSOLVER_STATUS_SUCCESS  The resources are released successfully. 
2.4.1.7. cusolverDnXsyevjSetTolerance()
cusolverStatus_t
cusolverDnXsyevjSetTolerance(
syevjInfo_t info,
double tolerance)
This function configures tolerance of syevj.
Parameter  Memory  In/out  Meaning 

info  host  in/out  The pointer to the structure of syevj. 
tolerance  host  input  accuracy of numerical eigenvalues. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
2.4.1.8. cusolverDnXsyevjSetMaxSweeps()
cusolverStatus_t
cusolverDnXsyevjSetMaxSweeps(
syevjInfo_t info,
int max_sweeps)
This function configures maximum number of sweeps in syevj. The default value is 100.
Parameter  Memory  In/out  Meaning 

info  host  in/out  The pointer to the structure of syevj. 
max_sweeps  host  input  Maximum number of sweeps. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
2.4.1.9. cusolverDnXsyevjSetSortEig()
cusolverStatus_t
cusolverDnXsyevjSetSortEig(
syevjInfo_t info,
int sort_eig)
If sort_eig is zero, the eigenvalues are not sorted. This function only works for syevjBatched. syevj and sygvj always sort eigenvalues in ascending order. By default, eigenvalues are always sorted in ascending order.
Parameter  Memory  In/out  Meaning 

info  host  in/out  The pointer to the structure of syevj. 
sort_eig  host  input  If sort_eig is zero, the eigenvalues are not sorted. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
2.4.1.10. cusolverDnXsyevjGetResidual()
cusolverStatus_t
cusolverDnXsyevjGetResidual(
cusolverDnHandle_t handle,
syevjInfo_t info,
double *residual)
This function reports residual of syevj or sygvj. It does not support syevjBatched. If the user calls this function after syevjBatched, the error CUSOLVER_STATUS_NOT_SUPPORTED is returned.
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
info  host  input  The pointer to the structure of syevj. 
residual  host  output  Residual of syevj. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_SUPPORTED  Does not support batched version. 
2.4.1.11. cusolverDnXsyevjGetSweeps()
cusolverStatus_t
cusolverDnXsyevjGetSweeps(
cusolverDnHandle_t handle,
syevjInfo_t info,
int *executed_sweeps)
This function reports number of executed sweeps of syevj or sygvj. It does not support syevjBatched. If the user calls this function after syevjBatched, the error CUSOLVER_STATUS_NOT_SUPPORTED is returned.
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
info  host  input  The pointer to the structure of syevj. 
executed_sweeps  host  output  Number of executed sweeps. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_SUPPORTED  Does not support batched version. 
2.4.1.12. cusolverDnCreateGesvdjInfo()
cusolverStatus_t cusolverDnCreateGesvdjInfo( gesvdjInfo_t *info);
This function creates and initializes the structure of gesvdj and gesvdjBatched to default values.
Parameter  Memory  In/out  Meaning 

info  host  output  The pointer to the structure of gesvdj. 
CUSOLVER_STATUS_SUCCESS  The structure was initialized successfully. 
CUSOLVER_STATUS_ALLOC_FAILED  The resources could not be allocated. 
2.4.1.13. cusolverDnDestroyGesvdjInfo()
cusolverStatus_t cusolverDnDestroyGesvdjInfo( gesvdjInfo_t info);
This function destroys and releases any memory required by the structure.
Parameter  Memory  In/out  Meaning 

info  host  input  The structure of gesvdj. 
CUSOLVER_STATUS_SUCCESS  The resources are released successfully. 
2.4.1.14. cusolverDnXgesvdjSetTolerance()
cusolverStatus_t
cusolverDnXgesvdjSetTolerance(
gesvdjInfo_t info,
double tolerance)
This function configures tolerance of gesvdj.
Parameter  Memory  In/out  Meaning 

info  host  in/out  The pointer to the structure of gesvdj. 
tolerance  host  input  Accuracy of numerical singular values. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
2.4.1.15. cusolverDnXgesvdjSetMaxSweeps()
cusolverStatus_t
cusolverDnXgesvdjSetMaxSweeps(
gesvdjInfo_t info,
int max_sweeps)
This function configures the maximum number of sweeps in gesvdj. The default value is 100.
Parameter  Memory  In/out  Meaning 

info  host  in/out  The pointer to the structure of gesvdj. 
max_sweeps  host  input  Maximum number of sweeps. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
2.4.1.16. cusolverDnXgesvdjSetSortEig()
cusolverStatus_t
cusolverDnXgesvdjSetSortEig(
gesvdjInfo_t info,
int sort_svd)
If sort_svd is zero, the singular values are not sorted. This function only works for gesvdjBatched. gesvdj always sorts singular values in descending order. By default, singular values are always sorted in descending order.
Parameter  Memory  In/out  Meaning 

info  host  in/out  The pointer to the structure of gesvdj. 
sort_svd  host  input  If sort_svd is zero, the singular values are not sorted. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
2.4.1.17. cusolverDnXgesvdjGetResidual()
cusolverStatus_t
cusolverDnXgesvdjGetResidual(
cusolverDnHandle_t handle,
gesvdjInfo_t info,
double *residual)
This function reports residual of gesvdj. It does not support gesvdjBatched. If the user calls this function after gesvdjBatched, the error CUSOLVER_STATUS_NOT_SUPPORTED is returned.
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
info  host  input  The pointer to the structure of gesvdj. 
residual  host  output  Residual of gesvdj. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_SUPPORTED  Does not support batched version 
2.4.1.18. cusolverDnXgesvdjGetSweeps()
cusolverStatus_t
cusolverDnXgesvdjGetSweeps(
cusolverDnHandle_t handle,
gesvdjInfo_t info,
int *executed_sweeps)
This function reports number of executed sweeps of gesvdj. It does not support gesvdjBatched. If the user calls this function after gesvdjBatched, the error CUSOLVER_STATUS_NOT_SUPPORTED is returned.
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
info  host  input  The pointer to the structure of gesvdj. 
executed_sweeps  host  output  Number of executed sweeps. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_SUPPORTED  Does not support batched version 
2.4.1.19. cusolverDnIRSParamsCreate()
cusolverStatus_t cusolverDnIRSParamsCreate(cusolverDnIRSParams_t *params);
This function creates and initializes the structure of parameters for an IRS solver such as the cusolverDnIRSXgesv() or the cusolverDnIRSXgels() functions to default values. The params structure created by this function can be used by one or more call to the same or to a different IRS solver. Note that in CUDA 10.2, the behavior was different and a new params structure was needed to be created per each call to an IRS solver. Also note that the user can also change configurations of the params and then call a new IRS instance, but be careful that the previous call was done because any change to the configuration before the previous call was done could affect it.
Parameter  Memory  In/out  Meaning 

params  host  output  Pointer to the cusolverDnIRSParams_t Params structure 
CUSOLVER_STATUS_SUCCESS  The structure was created and initialized successfully. 
CUSOLVER_STATUS_ALLOC_FAILED  The resources could not be allocated. 
2.4.1.20. cusolverDnIRSParamsDestroy()
cusolverStatus_t cusolverDnIRSParamsDestroy(cusolverDnIRSParams_t params);
This function destroys and releases any memory required by the Params structure.
Parameter  Memory  In/out  Meaning 

params  host  input  The cusolverDnIRSParams_t Params structure. 
CUSOLVER_STATUS_SUCCESS  The resources are released successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
CUSOLVER_STATUS_IRS_INFOS_NOT_DESTROYED  Not all the Infos structure associated with this Params structure have been destroyed yet. 
2.4.1.21. cusolverDnIRSParamsSetSolverPrecisions()
cusolverStatus_t cusolverDnIRSParamsSetSolverPrecisions( cusolverDnIRSParams_t params, cusolverPrecType_t solver_main_precision, cusolverPrecType_t solver_lowest_precision );
This function sets both the main and the lowest precision for the Iterative Refinement Solver (IRS). By main precision, we mean the precision of the Input and Output datatype. By lowest precision, we mean the solver is allowed to use as lowest computational precision during the LU factorization process. Note that the user has to set both the main and lowest precision before the first call to the IRS solver because they are NOT set by default with the params structure creation, as it depends on the Input Output data type and user request. It is a wrapper to both cusolverDnIRSParamsSetSolverMainPrecision() and cusolverDnIRSParamsSetSolverLowestPrecision(). All possible combinations of main/lowest precision are described in the table below. Usually the lowest precision defines the speedup that can be achieved. The ratio of the performance of the lowest precision over the main precision (e.g., Inputs/Outputs datatype) define the upper bound of the speedup that could be obtained. More precisely, it depends on many factors, but for large matrices sizes, it is the ratio of the matrixmatrix rankk product (e.g., GEMM where K is 256 and M=N=size of the matrix) that define the possible speedup. For instance, if the inout precision is real double precision CUSOLVER_R_64F and the lowest precision is CUSOLVER_R_32F, then we can expect a speedup of at most 2X for large problem sizes. If the lowest precision was CUSOLVER_R_16F, then we can expect 3X4X. A reasonable strategy should take the number of righthand sides, the size of the matrix as well as the convergence rate into account.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure. 
solver_main_precision  host  input  Allowed Inputs/Outputs datatype (for example CUSOLVER_R_FP64 for a real double precision data). See the table below for the supported precisions. 
solver_lowest_precision  host  input  Allowed lowest compute type (for example CUSOLVER_R_16F for half precision computation). See the table below for the supported precisions. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
Inputs/Outputs Data Type (e.g., main precision)  Supported values for the lowest precision 

CUSOLVER_C_64F  CUSOLVER_C_64F, CUSOLVER_C_32F, CUSOLVER_C_16F, CUSOLVER_C_16BF, CUSOLVER_C_TF32 
CUSOLVER_C_32F  CUSOLVER_C_32F, CUSOLVER_C_16F, CUSOLVER_C_16BF, CUSOLVER_C_TF32 
CUSOLVER_R_64F  CUSOLVER_R_64F, CUSOLVER_R_32F, CUSOLVER_R_16F, CUSOLVER_R_16BF, CUSOLVER_R_TF32 
CUSOLVER_R_32F  CUSOLVER_R_32F, CUSOLVER_R_16F, CUSOLVER_R_16BF, CUSOLVER_R_TF32 
2.4.1.22. cusolverDnIRSParamsSetSolverMainPrecision()
cusolverStatus_t cusolverDnIRSParamsSetSolverMainPrecision( cusolverDnIRSParams_t params, cusolverPrecType_t solver_main_precision);
This function sets the main precision for the Iterative Refinement Solver (IRS). By main precision, we mean, the type of the Input and Output data. Note that the user has to set both the main and lowest precision before a first call to the IRS solver because they are NOT set by default with the params structure creation, as it depends on the Input Output data type and user request. user can set it by either calling this function or by calling cusolverDnIRSParamsSetSolverPrecisions() which set both the main and the lowest precision together. All possible combinations of main/lowest precision are described in the table in the cusolverDnIRSParamsSetSolverPrecisions() section above.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure. 
solver_main_precision  host  input  Allowed Inputs/Outputs datatype (for example CUSOLVER_R_FP64 for a real double precision data). See the table in the cusolverDnIRSParamsSetSolverPrecisions() section above for the supported precisions. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
2.4.1.23. cusolverDnIRSParamsSetSolverLowestPrecision()
cusolverStatus_t cusolverDnIRSParamsSetSolverLowestPrecision( cusolverDnIRSParams_t params, cusolverPrecType_t lowest_precision_type);
This function sets the lowest precision that will be used by Iterative Refinement Solver. By lowest precision, we mean the solver is allowed to use as lowest computational precision during the LU factorization process. Note that the user has to set both the main and lowest precision before a first call to the IRS solver because they are NOT set by default with the params structure creation, as it depends on the Input Output data type and user request. Usually the lowest precision defines the speedup that can be achieved. The ratio of the performance of the lowest precision over the main precision (e.g., Inputs/Outputs datatype) define somehow the upper bound of the speedup that could be obtained. More precisely, it depends on many factors, but for large matrices sizes, it is the ratio of the matrixmatrix rankk product (e.g., GEMM where K is 256 and M=N=size of the matrix) that define the possible speedup. For instance, if the inout precision is real double precision CUSOLVER_R_64F and the lowest precision is CUSOLVER_R_32F, then we can expect a speedup of at most 2X for large problem sizes. If the lowest precision was CUSOLVER_R_16F, then we can expect 3X4X. A reasonable strategy should take the number of righthand sides, the size of the matrix as well as the convergence rate into account.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure. 
lowest_precision_type  host  input  Allowed lowest compute type (for example CUSOLVER_R_16F for half precision computation). See the table in the cusolverDnIRSParamsSetSolverPrecisions() section above for the supported precisions. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
2.4.1.24. cusolverDnIRSParamsSetRefinementSolver()
cusolverStatus_t cusolverDnIRSParamsSetRefinementSolver( cusolverDnIRSParams_t params, cusolverIRSRefinement_t solver);
This function sets the refinement solver to be used in the Iterative Refinement Solver functions such as the cusolverDnIRSXgesv() or the cusolverDnIRSXgels() functions. Note that the user has to set the refinement algorithm before a first call to the IRS solver because it is NOT set by default with the creating of params. Details about values that can be set to and theirs meaning are described in the table below.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_tParams structure 
solver  host  input  Type of the refinement solver to be used by the IRS solver such as cusolverDnIRSXgesv() or cusolverDnIRSXgels(). 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
CUSOLVER_IRS_REFINE_NOT_SET  Solver is not set, this value is what is set when creating the params structure. IRS solver will return an error. 
CUSOLVER_IRS_REFINE_NONE  No refinement solver; the IRS solver performs a factorization followed by a solve without any refinement. For example, if the IRS solver was cusolverDnIRSXgesv(), this is equivalent to a Xgesv routine without refinement and where the factorization is carried out in the lowest precision. If for example the main precision was CUSOLVER_R_64F and the lowest was CUSOLVER_R_64F as well, then this is equivalent to a call to cusolverDnDgesv(). 
CUSOLVER_IRS_REFINE_CLASSICAL  Classical iterative refinement solver. Similar to the one used in LAPACK routines. 
CUSOLVER_IRS_REFINE_GMRES  GMRES (Generalized Minimal Residual) based iterative refinement solver. In recent study, the GMRES method has drawn the scientific community attention for its ability to be used as refinement solver that outperforms the classical iterative refinement method. Based on our experimentation, we recommend this setting. 
CUSOLVER_IRS_REFINE_CLASSICAL_GMRES  Classical iterative refinement solver that uses the GMRES (Generalized Minimal Residual) internally to solve the correction equation at each iteration. We call the classical refinement iteration the outer iteration while the GMRES is called inner iteration. Note that if the tolerance of the inner GMRES is set very low, let say to machine precision, then the outer classical refinement iteration will performs only one iteration and thus this option will behaves like CUSOLVER_IRS_REFINE_GMRES. 
CUSOLVER_IRS_REFINE_GMRES_GMRES  Similar to CUSOLVER_IRS_REFINE_CLASSICAL_GMRES which consists of classical refinement process that uses GMRES to solve the inner correction system, here it is a GMRES (Generalized Minimal Residual) based iterative refinement solver that uses another GMRES internally to solve the preconditioned system. 
2.4.1.25. cusolverDnIRSParamsSetTol()
cusolverStatus_t
cusolverDnIRSParamsSetTol(
cusolverDnIRSParams_t params,
double val );
This function sets the tolerance for the refinement solver. By default it is such that all the RHS satisfy:
 RNRM is the infinitynorm of the residual
 XNRM is the infinitynorm of the solution
 ANRM is the infinityoperatornorm of the matrix A
 EPS is the machine epsilon for the Inputs/Outputs datatype that matches LAPACK <X>LAMCH('Epsilon')
 BWDMAX, the value BWDMAX is fixed to 1.0
The user can use this function to change the tolerance to a lower or higher value. Our goal is to give the user more control such a way he can investigate and control every detail of the IRS solver. Note that the tolerance value is always in real double precision whatever the Inputs/Outputs datatype is.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure. 
val  host  input  Double precision real value to which the refinement tolerance will be set. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
2.4.1.26. cusolverDnIRSParamsSetTolInner()
cusolverStatus_t
cusolverDnIRSParamsSetTolInner(
cusolverDnIRSParams_t params,
double val );
This function sets the tolerance for the inner refinement solver when the refinement solver consists of twolevels solver (e.g., CUSOLVER_IRS_REFINE_CLASSICAL_GMRES or CUSOLVER_IRS_REFINE_GMRES_GMRES cases). It is not referenced in case of one level refinement solver such as CUSOLVER_IRS_REFINE_CLASSICAL or CUSOLVER_IRS_REFINE_GMRES. It is set to 1e4 by default. This function set the tolerance for the inner solver (e.g. the inner GMRES). For example, if the Refinement Solver was set to CUSOLVER_IRS_REFINE_CLASSICAL_GMRES, setting this tolerance mean that the inner GMRES solver will converge to that tolerance at each outer iteration of the classical refinement solver. Our goal is to give the user more control such a way he can investigate and control every detail of the IRS solver. Note the, the tolerance value is always in real double precision whatever the Inputs/Outputs datatype is.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure. 
val  host  input  Double precision real value to which the tolerance of the inner refinement solver will be set. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
2.4.1.27. cusolverDnIRSParamsSetMaxIters()
cusolverStatus_t
cusolverDnIRSParamsSetMaxIters(
cusolverDnIRSParams_t params,
int max_iters);
This function sets the total number of allowed refinement iterations after which the solver will stop. Total means any iteration which means the sum of the outer and the inner iterations (inner is meaningful when twolevels refinement solver is set). Default value is set to 50. Our goal is to give the user more control such a way he can investigate and control every detail of the IRS solver.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure. 
max_iters  host  input  Maximum total number of iterations allowed for the refinement solver. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
2.4.1.28. cusolverDnIRSParamsSetMaxItersInner()
cusolverStatus_t cusolverDnIRSParamsSetMaxItersInner( cusolverDnIRSParams_t params, cusolver_int_t maxiters_inner );
This function sets the maximal number of iterations allowed for the inner refinement solver. It is not referenced in case of one level refinement solver such as CUSOLVER_IRS_REFINE_CLASSICAL or CUSOLVER_IRS_REFINE_GMRES. The inner refinement solver will stop after reaching either the inner tolerance or the MaxItersInner value. By default, it is set to 50. Note that this value could not be larger than the MaxIters since MaxIters is the total number of allowed iterations. Note that if the user calls cusolverDnIRSParamsSetMaxIters after calling this function, SetMaxIters has priority and will overwrite MaxItersInner to the minimum value of (MaxIters, MaxItersInner).
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure 
maxiters_inner  host  input  Maximum number of allowed inner iterations for the inner refinement solver. Meaningful when the refinement solver is a twolevels solver such as CUSOLVER_IRS_REFINE_CLASSICAL_GMRES or CUSOLVER_IRS_REFINE_GMRES_GMRES. Value should be less or equal to MaxIters. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
CUSOLVER_STATUS_IRS_PARAMS_INVALID  If the value was larger than MaxIters. 
2.4.1.29. cusolverDnIRSParamsEnableFallback()
cusolverStatus_t cusolverDnIRSParamsEnableFallback( cusolverDnIRSParams_t params );
This function enable the fallback to the main precision in case the Iterative Refinement Solver (IRS) failed to converge. In other term, if the IRS solver failed to converge, the solver will return a no convergence code (e.g., niter < 0), but can either return the nonconvergent solution as it is (e.g., disable fallback) or can fallback (e.g., enable fallback) to the main precision (which is the precision of the Inputs/Outputs data) and solve the problem from scratch returning the good solution. This is the behavior by default, and it will guarantee that the IRS solver always provide the good solution. This function is provided because we provided cusolverDnIRSParamsDisableFallback which allows the user to disable the fallback and thus this function allow the user to reenable it.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
2.4.1.30. cusolverDnIRSParamsDisableFallback()
cusolverStatus_t cusolverDnIRSParamsDisableFallback( cusolverDnIRSParams_t params );
This function disables the fallback to the main precision in case the Iterative Refinement Solver (IRS) failed to converge. In other term, if the IRS solver failed to converge, the solver will return a no convergence code (e.g., niter < 0), but can either return the nonconvergent solution as it is (e.g., disable fallback) or can fallback (e.g., enable fallback) to the main precision (which is the precision of the Inputs/Outputs data) and solve the problem from scratch returning the good solution. This function disables the fallback and the returned solution is whatever the refinement solver was able to reach before it returns. Disabling fallback does not guarantee that the solution is the good one. However, if users want to keep getting the solution of the lower precision in case the IRS did not converge after certain number of iterations, they need to disable the fallback. The user can reenable it by calling cusolverDnIRSParamsEnableFallback.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The cusolverDnIRSParams_t Params structure 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
2.4.1.31. cusolverDnIRSParamsGetMaxIters()
cusolverStatus_t cusolverDnIRSParamsGetMaxIters( cusolverDnIRSParams_t params, cusolver_int_t *maxiters );
This function returns the current setting in the params structure for the maximal allowed number of iterations (e.g., either the default MaxIters, or the one set by the user in case he set it using cusolverDnIRSParamsSetMaxIters). Note that this function returns the current setting in the params configuration and not to be confused with the cusolverDnIRSInfosGetMaxIters which return the maximal allowed number of iterations for a particular call to an IRS solver. To be clearer, the params structure can be used for many calls to an IRS solver. A user can change the allowed MaxIters between calls while the Infos structure in cusolverDnIRSInfosGetMaxIters contains information about a particular call and cannot be reused for different calls, and thus, cusolverDnIRSInfosGetMaxIters returns the allowed MaxIters for that call.
Parameter  Memory  In/out  Meaning 

params  host  in  The cusolverDnIRSParams_t Params structure. 
maxiters  host  output  The maximal number of iterations that is currently set. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The Params structure was not created. 
2.4.1.32. cusolverDnIRSInfosCreate()
cusolverStatus_t cusolverDnIRSInfosCreate( cusolverDnIRSInfos_t* infos )
This function creates and initializes the Infos structure that will hold the refinement information of an Iterative Refinement Solver (IRS) call. Such information includes the total number of iterations that was needed to converge (Niters), the outer number of iterations (meaningful when twolevels preconditioner such as CUSOLVER_IRS_REFINE_CLASSICAL_GMRES is used ), the maximal number of iterations that was allowed for that call, and a pointer to the matrix of the convergence history residual norms. The Infos structure needs to be created before a call to an IRS solver. The Infos structure is valid for only one call to an IRS solver, since it holds info about that solve and thus each solve will requires its own Infos structure.
Parameter  Memory  In/out  Meaning 

info  host  output  Pointer to the cusolverDnIRSInfos_t Infos structure. 
CUSOLVER_STATUS_SUCCESS  The structure was initialized successfully. 
CUSOLVER_STATUS_ALLOC_FAILED  The resources could not be allocated. 
2.4.1.33. cusolverDnIRSInfosDestroy()
cusolverStatus_t cusolverDnIRSInfosDestroy( cusolverDnIRSInfos_t infos );
This function destroys and releases any memory required by the Infos structure. This function destroys all the information (e.g., Niters performed, OuterNiters performed, residual history etc.) about a solver call; thus, this function should only be called after the user is finished with the information.
Parameter  Memory  In/out  Meaning 

info  host  in/out  The cusolverDnIRSInfos_t Infos structure. 
CUSOLVER_STATUS_SUCCESS  The resources are released successfully. 
CUSOLVER_STATUS_IRS_INFOS_NOT_INITIALIZED  The Infos structure was not created. 
2.4.1.34. cusolverDnIRSInfosGetMaxIters()
cusolverStatus_t cusolverDnIRSInfosGetMaxIters( cusolverDnIRSInfos_t infos, cusolver_int_t *maxiters );
This function returns the maximal allowed number of iterations that was set for the corresponding call to the IRS solver. Note that this function returns the setting that was set when that call happened and is not to be confused with the cusolverDnIRSParamsGetMaxIters which returns the current setting in the params configuration structure. To be clearer, the params structure can be used for many calls to an IRS solver. A user can change the allowed MaxIters between calls while the Infos structure in cusolverDnIRSInfosGetMaxIters contains information about a particular call and cannot be reused for different calls, thus cusolverDnIRSInfosGetMaxIters returns the allowed MaxIters for that call.
Parameter  Memory  In/out  Meaning 

infos  host  in  The cusolverDnIRSInfos_t Infos structure. 
maxiters  host  output  The maximal number of iterations that is currently set. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_INFOS_NOT_INITIALIZED  The Infos structure was not created. 
2.4.1.35. cusolverDnIRSInfosGetNiters()
cusolverStatus_t cusolverDnIRSInfosGetNiters( cusolverDnIRSInfos_t infos, cusolver_int_t *niters );
This function returns the total number of iterations performed by the IRS solver. If it was negative, it means that the IRS solver did not converge and if the user did not disable the fallback to full precision, then the fallback to a full precision solution happened and solution is good. Please refer to the description of negative niters values in the corresponding IRS linear solver functions such as cusolverDnXgesv() or cusolverDnXgels().
Parameter  Memory  In/out  Meaning 

infos  host  in  The cusolverDnIRSInfos_t Infos structure. 
niters  host  output  The total number of iterations performed by the IRS solver. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_INFOS_NOT_INITIALIZED  The Infos structure was not created. 
2.4.1.36. cusolverDnIRSInfosGetOuterNiters()
cusolverStatus_t cusolverDnIRSInfosGetOuterNiters( cusolverDnIRSInfos_t infos, cusolver_int_t *outer_niters );
This function returns the number of iterations performed by the outer refinement loop of the IRS solver. When the refinement solver consists of a onelevel solver such as CUSOLVER_IRS_REFINE_CLASSICAL or CUSOLVER_IRS_REFINE_GMRES, it is the same as Niters. When the refinement solver consists of a twolevels solver such as CUSOLVER_IRS_REFINE_CLASSICAL_GMRES or CUSOLVER_IRS_REFINE_GMRES_GMRES, it is the number of iterations of the outer loop. Refer to the description of the cusolverIRSRefinement_t section for more details.
Parameter  Memory  In/out  Meaning 

infos  host  in  The cusolverDnIRSInfos_t Infos structure. 
outer_niters  host  output  The number of iterations of the outer refinement loop of the IRS solver. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_INFOS_NOT_INITIALIZED  The Infos structure was not created. 
2.4.1.37. cusolverDnIRSInfosRequestResidual()
cusolverStatus_t cusolverDnIRSInfosRequestResidual( cusolverDnIRSInfos_t infos );
This function tells the IRS solver to store the convergence history (residual norms) of the refinement phase in a matrix that can be accessed via a pointer returned by the cusolverDnIRSInfosGetResidualHistory() function.
Parameter  Memory  In/out  Meaning 

infos  host  in  The cusolverDnIRSInfos_t Infos structure 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_INFOS_NOT_INITIALIZED  The Infos structure was not created. 
2.4.1.38. cusolverDnIRSInfosGetResidualHistory()
cusolverStatus_t
cusolverDnIRSInfosGetResidualHistory(
cusolverDnIRSInfos_t infos,
void **residual_history );
If the user called cusolverDnIRSInfosRequestResidual() before the call to the IRS function, then the IRS solver will store the convergence history (residual norms) of the refinement phase in a matrix that can be accessed via a pointer returned by this function. The datatype of the residual norms depends on the input and output data type. If the Inputs/Outputs datatype is double precision real or complex (CUSOLVER_R_FP64 or CUSOLVER_C_FP64), this residual will be of type real double precision (FP64) double, otherwise if the Inputs/Outputs datatype is single precision real or complex (CUSOLVER_R_FP32 or CUSOLVER_C_FP32), this residual will be real single precision FP32 float.
The residual history matrix consists of two columns (even for the multiple righthand side case NRHS) of MaxIters+1 row, thus a matrix of size (MaxIters+1,2). Only the first OuterNiters+1 rows contains the residual norms the other (e.g., OuterNiters+2:Maxiters+1) are garbage. On the first column, each row "i" specify the total number of iterations happened till this outer iteration "i" and on the second columns the residual norm corresponding to this outer iteration "i". Thus, the first row (e.g., outer iteration "0") consists of the initial residual (e.g., the residual before the refinement loop start) then the consecutive rows are the residual obtained at each outer iteration of the refinement loop. Note, it only consists of the history of the outer loop.
If the refinement solver was CUSOLVER_IRS_REFINE_CLASSICAL or CUSOLVER_IRS_REFINE_GMRES, then OuterNiters=Niters (Niters is the total number of iterations performed) and there is Niters+1 rows of norms that correspond to the Niters outer iterations.
If the refinement solver was CUSOLVER_IRS_REFINE_CLASSICAL_GMRES or CUSOLVER_IRS_REFINE_GMRES_GMRES, then OuterNiters <= Niters corresponds to the outer iterations performed by the outer refinement loop. Thus, there is OuterNiters+1 residual norms where row "i" correspond to the outer iteration "i" and the first column specify the total number of iterations (outer and inner) that were performed till this step the second columns correspond to the residual norm at this step.
For example, let's say the user specifies CUSOLVER_IRS_REFINE_CLASSICAL_GMRES as a refinement solver and say it needed 3 outer iterations to converge and 4,3,3 inner iterations at each outer, respectively. This consists of 10 total iterations. Row 0 corresponds to the first residual before the refinement start, so it has 0 in its first column. On row 1 which corresponds to the outer iteration 1, it will be 4 (4 is the total number of iterations that were performed till now), on row 2 it will be 7, and on row 3 it will be 10.
In summary, let's define ldh=Maxiters+1, the leading dimension of the residual matrix. then residual_history[i] shows the total number of iterations performed at the outer iteration "i" and residual_history[i+ldh] corresponds to the norm of the residual at this outer iteration.
Parameter  Memory  In/out  Meaning 

infos  host  in  The cusolverDnIRSInfos_t Infos structure. 
residual_history  host  output  Returns a void pointer to the matrix of the convergence history residual norms. See the description above for the relation between the residual norm datatype and the inout datatype. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_IRS_INFOS_NOT_INITIALIZED  The Infos structure was not created. 
CUSOLVER_STATUS_INVALID_VALUE  This function was called without calling cusolverDnIRSInfosRequestResidual() in advance. 
2.4.1.39. cusolverDnCreateParams()
cusolverStatus_t cusolverDnCreateParams( cusolverDnParams_t *params);
This function creates and initializes the structure of 64bit API to default values.
Parameter  Memory  In/out  Meaning 

params  host  output  The pointer to the structure of 64bit API. 
CUSOLVER_STATUS_SUCCESS  The structure was initialized successfully. 
CUSOLVER_STATUS_ALLOC_FAILED  The resources could not be allocated. 
2.4.1.40. cusolverDnDestroyParams()
cusolverStatus_t cusolverDnDestroyParams( cusolverDnParams_t params);
This function destroys and releases any memory required by the structure.
Parameter  Memory  In/out  Meaning 

params  host  input  The structure of 64bit API. 
CUSOLVER_STATUS_SUCCESS  The resources were released successfully. 
2.4.1.41. cusolverDnSetAdvOptions()
cusolverStatus_t cusolverDnSetAdvOptions ( cusolverDnParams_t params, cusolverDnFunction_t function, cusolverAlgMode_t algo );
This function configures algorithm algo of function, a 64bit API routine.
Parameter  Memory  In/out  Meaning 

params  host  in/out  The pointer to the structure of 64bit API. 
function  host  input  The routine to be configured. 
algo  host  input  The algorithm to be configured. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_INVALID_VALUE  Wrong combination of function and algo. 
2.4.2. Dense Linear Solver Reference (legacy)
This section describes linear solver API of cuSolverDN, including Cholesky factorization, LU with partial pivoting, QR factorization and BunchKaufman (LDLT) factorization.
2.4.2.1. cusolverDn<t>potrf()
cusolverStatus_t cusolverDnSpotrf_bufferSize(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, float *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnDpotrf_bufferSize(cusolveDnHandle_t handle, cublasFillMode_t uplo, int n, double *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnCpotrf_bufferSize(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, cuComplex *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnZpotrf_bufferSize(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, cuDoubleComplex *A, int lda, int *Lwork);
cusolverStatus_t cusolverDnSpotrf(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, float *A, int lda, float *Workspace, int Lwork, int *devInfo ); cusolverStatus_t cusolverDnDpotrf(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, double *A, int lda, double *Workspace, int Lwork, int *devInfo );
cusolverStatus_t cusolverDnCpotrf(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, cuComplex *A, int lda, cuComplex *Workspace, int Lwork, int *devInfo ); cusolverStatus_t cusolverDnZpotrf(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, cuDoubleComplex *A, int lda, cuDoubleComplex *Workspace, int Lwork, int *devInfo );
This function computes the Cholesky factorization of a Hermitian positivedefinite matrix.
A is an n×n Hermitian matrix, only the lower or upper part is meaningful. The input parameter uplo indicates which part of the matrix is used. The function would leave other parts untouched.
If input parameter uplo is CUBLAS_FILL_MODE_LOWER, only the lower triangular part of A is processed, and replaced by the lower triangular Cholesky factor L.
$A=L*{L}^{H}$ 
If input parameter uplo is CUBLAS_FILL_MODE_UPPER, only upper triangular part of A is processed, and replaced by upper triangular Cholesky factor U.
$A={U}^{H}*U$ 
The user has to provide working space which is pointed by input parameter Workspace. The input parameter Lwork is size of the working space, and it is returned by potrf_bufferSize().
If Cholesky factorization failed, i.e. some leading minor of A is not positive definite, or equivalently some diagonal elements of L or U is not a real number. The output parameter devInfo would indicate smallest leading minor of A which is not positive definite.
If output parameter devInfo = i (less than zero), the ith parameter is wrong (not counting handle).
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
uplo  host  input  Indicates if matrix A lower or upper part is stored; the other part is not referenced. 
n  host  input  Number of rows and columns of matrix A. 
A  device  in/out  <type> array of dimension lda * n with lda is not less than max(1,n). 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
Workspace  device  in/out  Working space, <type> array of size Lwork. 
Lwork  host  input  Size of Workspace, returned by potrf_bufferSize. 
devInfo  device  output  If devInfo = 0, the Cholesky factorization is successful. if devInfo = i, the ith parameter is wrong (not counting handle). if devInfo = i, the leading minor of order i is not positive definite. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (n<0 or lda<max(1,n)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.2. cusolverDnPotrf()[DEPRECATED]
[[DEPRECATED]] use cusolverDnXpotrf() instead. The routine will be removed in the next major release.
cusolverStatus_t cusolverDnPotrf_bufferSize( cusolverDnHandle_t handle, cusolverDnParams_t params, cublasFillMode_t uplo, int64_t n, cudaDataType dataTypeA, const void *A, int64_t lda, cudaDataType computeType, size_t *workspaceInBytes )
cusolverStatus_t cusolverDnPotrf( cusolverDnHandle_t handle, cusolverDnParams_t params, cublasFillMode_t uplo, int64_t n, cudaDataType dataTypeA, void *A, int64_t lda, cudaDataType computeType, void *pBuffer, size_t workspaceInBytes, int *info )
Computes the Cholesky factorization of a Hermitian positivedefinite matrix using the generic API interfacte.
A is an n×n Hermitian matrix, only lower or upper part is meaningful. The input parameter uplo indicates which part of the matrix is used. The function would leave other part untouched.
If input parameter uplo is CUBLAS_FILL_MODE_LOWER, only lower triangular part of A is processed, and replaced by lower triangular Cholesky factor L.
$A=L*{L}^{H}$ 
If input parameter uplo is CUBLAS_FILL_MODE_UPPER, only upper triangular part of A is processed, and replaced by upper triangular Cholesky factor U.
$A={U}^{H}*U$ 
The user has to provide working space which is pointed by input parameter pBuffer. The input parameter workspaceInBytes is size in bytes of the working space, and it is returned by cusolverDnPotrf_bufferSize().
If Cholesky factorization failed, i.e. some leading minor of A is not positive definite, or equivalently some diagonal elements of L or U is not a real number. The output parameter info would indicate smallest leading minor of A which is not positive definite.
If output parameter info = i (less than zero), the ith parameter is wrong (not counting handle).
Currently, cusolverDnPotrf supports only the default algorithm.
CUSOLVER_ALG_0 or NULL  Default algorithm. 
List of input arguments for cusolverDnPotrf_bufferSize and cusolverDnPotrf:
Parameter  Memory  In/out  Meaning 

handle  host  input  handle to the cuSolverDN library context. 
params  host  input  structure with information collected by cusolverDnSetAdvOptions. 
uplo  host  input  indicates if matrix A lower or upper part is stored, the other part is not referenced. 
n  host  input  number of rows and columns of matrix A. 
dataTypeA  host  in  data type of array A. 
A  device  in/out  array of dimension lda * n with lda is not less than max(1,n). 
lda  host  input  leading dimension of twodimensional array used to store matrix A. 
computeType  host  in  data type of computation. 
pBuffer  device  in/out  Working space. Array of type void of size workspaceInBytes bytes. 
workspaceInBytes  host  input  size in bytes of pBuffer, returned by cusolverDnPotrf_bufferSize. 
info  device  output  if info = 0, the Cholesky factorization is successful. if info = i, the ith parameter is wrong (not counting handle). if info = i, the leading minor of order i is not positive definite. 
The generic API has two different types, dataTypeA is data type of the matrix A, computeType is compute type of the operation. cusolverDnPotrf only supports the following four combinations.
DataTypeA  ComputeType  Meaning 
CUDA_R_32F  CUDA_R_32F  SPOTRF 
CUDA_R_64F  CUDA_R_64F  DPOTRF 
CUDA_C_32F  CUDA_C_32F  CPOTRF 
CUDA_C_64F  CUDA_C_64F  ZPOTRF 
CUSOLVER_STATUS_SUCCESS  the operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  the library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  invalid parameters were passed (n<0 or lda<max(1,n)). 
CUSOLVER_STATUS_ARCH_MISMATCH  the device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  an internal operation failed. 
2.4.2.3. cusolverDn<t>potrs()
cusolverStatus_t cusolverDnSpotrs(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, int nrhs, const float *A, int lda, float *B, int ldb, int *devInfo); cusolverStatus_t cusolverDnDpotrs(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, int nrhs, const double *A, int lda, double *B, int ldb, int *devInfo); cusolverStatus_t cusolverDnCpotrs(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, int nrhs, const cuComplex *A, int lda, cuComplex *B, int ldb, int *devInfo); cusolverStatus_t cusolverDnZpotrs(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, int nrhs, const cuDoubleComplex *A, int lda, cuDoubleComplex *B, int ldb, int *devInfo);
This function solves a system of linear equations
$A*X=B$ 
where A is an n×n Hermitian matrix, only lower or upper part is meaningful. The input parameter uplo indicates which part of the matrix is used. The function would leave other part untouched.
The user has to call potrf first to factorize matrix A. If input parameter uplo is CUBLAS_FILL_MODE_LOWER, A is lower triangular Cholesky factor L correspoding to $A=L*{L}^{H}$ . If input parameter uplo is CUBLAS_FILL_MODE_UPPER, A is upper triangular Cholesky factor U corresponding to $A={U}^{H}*U$ .
The operation is inplace, i.e. matrix X overwrites matrix B with the same leading dimension ldb.
If output parameter devInfo = i (less than zero), the ith parameter is wrong (not counting handle).
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolveDN library context. 
uplo  host  input  Indicates if matrix A lower or upper part is stored, the other part is not referenced. 
n  host  input  Number of rows and columns of matrix A. 
nrhs  host  input  Number of columns of matrix X and B. 
A  device  input  <type> array of dimension lda * n with lda is not less than max(1,n). A is either lower cholesky factor L or upper Cholesky factor U. 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
B  device  in/out  <type> array of dimension ldb * nrhs. ldb is not less than max(1,n). As an input, B is right hand side matrix. As an output, B is the solution matrix. 
devInfo  device  output  If devInfo = 0, the Cholesky factorization is successful. if devInfo = i, the ith parameter is wrong (not counting handle). 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (n<0, nrhs<0, lda<max(1,n) or ldb<max(1,n)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.4. cusolverDnPotrs()[DEPRECATED]
[[DEPRECATED]] use cusolverDnXpotrs() instead. The routine will be removed in the next major release.
cusolverStatus_t cusolverDnPotrs( cusolverDnHandle_t handle, cusolverDnParams_t params, cublasFillMode_t uplo, int64_t n, int64_t nrhs, cudaDataType dataTypeA, const void *A, int64_t lda, cudaDataType dataTypeB, void *B, int64_t ldb, int *info)
This function solves a system of linear equations
$A*X=B$ 
where A is a n×n Hermitian matrix, only lower or upper part is meaningful using the generic API interface. The input parameter uplo indicates which part of the matrix is used. The function would leave other part untouched.
The user has to call cusolverDnPotrf first to factorize matrix A. If input parameter uplo is CUBLAS_FILL_MODE_LOWER, A is lower triangular Cholesky factor L correspoding to $A=L*{L}^{H}$ . If input parameter uplo is CUBLAS_FILL_MODE_UPPER, A is upper triangular Cholesky factor U corresponding to $A={U}^{H}*U$ .
The operation is inplace, i.e. matrix X overwrites matrix B with the same leading dimension ldb.
If output parameter info = i (less than zero), the ith parameter is wrong (not counting handle).
Currently, cusolverDnPotrs supports only the default algorithm.
CUSOLVER_ALG_0 or NULL  Default algorithm. 
List of input arguments for cusolverDnPotrs:
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolveDN library context. 
params  host  input  Structure with information collected by cusolverDnSetAdvOptions. 
uplo  host  input  Indicates if matrix A lower or upper part is stored, the other part is not referenced. 
n  host  input  Number of rows and columns of matrix A. 
nrhs  host  input  Number of columns of matrix X and B. 
dataTypeA  host  in  Data type of array A. 
A  device  input  Array of dimension lda * n with lda is not less than max(1,n). A is either lower cholesky factor L or upper Cholesky factor U. 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
dataTypeB  host  in  Data type of array B. 
B  device  in/out  Array of dimension ldb * nrhs. ldb is not less than max(1,n). As an input, B is right hand side matrix. As an output, B is the solution matrix. 
info  device  output  If info = 0, the Cholesky factorization is successful. if info = i, the ith parameter is wrong (not counting handle). 
The generic API has two different types, dataTypeA is data type of the matrix A, dataTypeB is data type of the matrix B. cusolverDnPotrs only supports the following four combinations.
dataTypeA  dataTypeB  Meaning 
CUDA_R_32F  CUDA_R_32F  SPOTRS 
CUDA_R_64F  CUDA_R_64F  DPOTRS 
CUDA_C_32F  CUDA_C_32F  CPOTRS 
CUDA_C_64F  CUDA_C_64F  ZPOTRS 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (n<0, nrhs<0, lda<max(1,n) or ldb<max(1,n)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.5. cusolverDn<t>potri()
cusolverStatus_t cusolverDnSpotri_bufferSize(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, float *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnDpotri_bufferSize(cusolveDnHandle_t handle, cublasFillMode_t uplo, int n, double *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnCpotri_bufferSize(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, cuComplex *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnZpotri_bufferSize(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, cuDoubleComplex *A, int lda, int *Lwork);
cusolverStatus_t cusolverDnSpotri(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, float *A, int lda, float *Workspace, int Lwork, int *devInfo ); cusolverStatus_t cusolverDnDpotri(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, double *A, int lda, double *Workspace, int Lwork, int *devInfo );
cusolverStatus_t cusolverDnCpotri(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, cuComplex *A, int lda, cuComplex *Workspace, int Lwork, int *devInfo ); cusolverStatus_t cusolverDnZpotri(cusolverDnHandle_t handle, cublasFillMode_t uplo, int n, cuDoubleComplex *A, int lda, cuDoubleComplex *Workspace, int Lwork, int *devInfo );
This function computes the inverse of a positivedefinite matrix A using the Cholesky factorization
$A=L*{L}^{H}={U}^{H}*U$ 
computed by potrf().
A is a n×n matrix containing the triangular factor L or U computed by the Cholesky factorization. Only lower or upper part is meaningful and the input parameter uplo indicates which part of the matrix is used. The function would leave the other part untouched.
If the input parameter uplo is CUBLAS_FILL_MODE_LOWER, only lower triangular part of A is processed, and replaced the by lower triangular part of the inverse of A.
If the input parameter uplo is CUBLAS_FILL_MODE_UPPER, only upper triangular part of A is processed, and replaced by the upper triangular part of the inverse of A.
The user has to provide the working space which is pointed to by input parameter Workspace. The input parameter Lwork is the size of the working space, returned by potri_bufferSize().
If the computation of the inverse fails, i.e. some leading minor of L or U, is null, the output parameter devInfo would indicate the smallest leading minor of L or U which is not positive definite.
If the output parameter devInfo = i (less than zero), the ith parameter is wrong (not counting the handle).
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
uplo  host  input  Indicates if matrix A lower or upper part is stored, the other part is not referenced. 
n  host  input  Number of rows and columns of matrix A. 
A  device  in/out  <type> array of dimension lda * n where lda is not less than max(1,n). 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
Workspace  device  in/out  Working space, <type> array of size Lwork. 
Lwork  host  input  Size of Workspace, returned by potri_bufferSize. 
devInfo  device  output  If devInfo = 0, the computation of the inverse is successful. if devInfo = i, the ith parameter is wrong (not counting handle). if devInfo = i, the leading minor of order i is zero. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (n<0 or lda<max(1,n)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.6. cusolverDn<t>getrf()
cusolverStatus_t cusolverDnSgetrf_bufferSize(cusolverDnHandle_t handle, int m, int n, float *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnDgetrf_bufferSize(cusolverDnHandle_t handle, int m, int n, double *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnCgetrf_bufferSize(cusolverDnHandle_t handle, int m, int n, cuComplex *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnZgetrf_bufferSize(cusolverDnHandle_t handle, int m, int n, cuDoubleComplex *A, int lda, int *Lwork );
cusolverStatus_t cusolverDnSgetrf(cusolverDnHandle_t handle, int m, int n, float *A, int lda, float *Workspace, int *devIpiv, int *devInfo ); cusolverStatus_t cusolverDnDgetrf(cusolverDnHandle_t handle, int m, int n, double *A, int lda, double *Workspace, int *devIpiv, int *devInfo );
cusolverStatus_t cusolverDnCgetrf(cusolverDnHandle_t handle, int m, int n, cuComplex *A, int lda, cuComplex *Workspace, int *devIpiv, int *devInfo ); cusolverStatus_t cusolverDnZgetrf(cusolverDnHandle_t handle, int m, int n, cuDoubleComplex *A, int lda, cuDoubleComplex *Workspace, int *devIpiv, int *devInfo );
This function computes the LU factorization of a m×n matrix
$P*A=L*U$ 
where A is a m×n matrix, P is a permutation matrix, L is a lower triangular matrix with unit diagonal, and U is an upper triangular matrix.
The user has to provide working space which is pointed by input parameter Workspace. The input parameter Lwork is size of the working space, and it is returned by getrf_bufferSize().
If LU factorization failed, i.e. matrix A (U) is singular, The output parameter devInfo=i indicates U(i,i) = 0.
If output parameter devInfo = i (less than zero), the ith parameter is wrong (not counting handle).
If devIpiv is null, no pivoting is performed. The factorization is A=L*U, which is not numerically stable.
No matter LU factorization failed or not, the output parameter devIpiv contains pivoting sequence, row i is interchanged with row devIpiv(i).
The user can combine getrf and getrs to complete a linear solver. Please visit cuSOLVER Library Samples  getrf for a code example.
Remark: getrf uses fastest implementation with large workspace of size m*n. The user can choose the legacy implementation with minimal workspace by Getrf and cusolverDnSetAdvOptions(params, CUSOLVERDN_GETRF, CUSOLVER_ALG_1).
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
m  host  input  Number of rows of matrix A. 
n  host  input  Number of columns of matrix A. 
A  device  in/out  <type> array of dimension lda * n with lda is not less than max(1,m). 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
Workspace  device  in/out  Working space, <type> array of size Lwork. 
devIpiv  device  output  Array of size at least min(m,n), containing pivot indices. 
devInfo  device  output  If devInfo = 0, the LU factorization is successful. if devInfo = i, the ith parameter is wrong (not counting handle). if devInfo = i, the U(i,i) = 0. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (m,n<0 or lda<max(1,m)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.7. cusolverDnGetrf()[DEPRECATED]
[[DEPRECATED]] use cusolverDnXgetrf() instead. The routine will be removed in the next major release.
cusolverStatus_t cusolverDnGetrf_bufferSize( cusolverDnHandle_t handle, cusolverDnParams_t params, int64_t m, int64_t n, cudaDataType dataTypeA, const void *A, int64_t lda, cudaDataType computeType, size_t *workspaceInBytes )
cusolverStatus_t cusolverDnGetrf( cusolverDnHandle_t handle, cusolverDnParams_t params, int64_t m, int64_t n, cudaDataType dataTypeA, void *A, int64_t lda, int64_t *ipiv, cudaDataType computeType, void *pBuffer, size_t workspaceInBytes, int *info )
computes the LU factorization of a m×n matrix
$P*A=L*U$ 
where A is an m×n matrix, P is a permutation matrix, L is a lower triangular matrix with unit diagonal, and U is an upper triangular matrix using the generic API interface.
If LU factorization failed, i.e. matrix A (U) is singular, The output parameter info=i indicates U(i,i) = 0.
If output parameter info = i (less than zero), the ith parameter is wrong (not counting handle).
If ipiv is null, no pivoting is performed. The factorization is A=L*U, which is not numerically stable.
No matter LU factorization failed or not, the output parameter ipiv contains pivoting sequence, row i is interchanged with row ipiv(i).
The user has to provide working space which is pointed by input parameter pBuffer. The input parameter workspaceInBytes is size in bytes of the working space, and it is returned by cusolverDnGetrf_bufferSize().
The user can combine cusolverDnGetrf and cusolverDnGetrs to complete a linear solver.
Currently, cusolverDnGetrf supports two algorithms. To select legacy implementation, the user has to call cusolverDnSetAdvOptions.
CUSOLVER_ALG_0 or NULL  Default algorithm. The fastest, requires a large workspace of m*n elements. 
CUSOLVER_ALG_1  Legacy implementation 
List of input arguments for cusolverDnGetrf_bufferSize and cusolverDnGetrf:
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
params  host  input  Structure with information collected by cusolverDnSetAdvOptions. 
m  host  input  number of rows of matrix A. 
n  host  input  number of columns of matrix A. 
dataTypeA  host  in  data type of array A. 
A  device  in/out  <type> array of dimension lda * n with lda is not less than max(1,m). 
lda  host  input  leading dimension of twodimensional array used to store matrix A. 
ipiv  device  output  array of size at least min(m,n), containing pivot indices. 
computeType  host  in  data type of computation. 
pBuffer  device  in/out  Working space. Array of type void of size workspaceInBytes bytes. 
workspaceInBytes  host  input  size in bytes of pBuffer, returned by cusolverDnGetrf_bufferSize. 
info  device  output  if info = 0, the LU factorization is successful. if info = i, the ith parameter is wrong (not counting handle). if info = i, the U(i,i) = 0. 
The generic API has two different types, dataTypeA is data type of the matrix A, computeType is compute type of the operation. cusolverDnGetrf only supports the following four combinations.
DataTypeA  ComputeType  Meaning 
CUDA_R_32F  CUDA_R_32F  SGETRF 
CUDA_R_64F  CUDA_R_64F  DGETRF 
CUDA_C_32F  CUDA_C_32F  CGETRF 
CUDA_C_64F  CUDA_C_64F  ZGETRF 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (m,n<0 or lda<max(1,m)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.8. cusolverDn<t>getrs()
cusolverStatus_t cusolverDnSgetrs(cusolverDnHandle_t handle, cublasOperation_t trans, int n, int nrhs, const float *A, int lda, const int *devIpiv, float *B, int ldb, int *devInfo ); cusolverStatus_t cusolverDnDgetrs(cusolverDnHandle_t handle, cublasOperation_t trans, int n, int nrhs, const double *A, int lda, const int *devIpiv, double *B, int ldb, int *devInfo ); cusolverStatus_t cusolverDnCgetrs(cusolverDnHandle_t handle, cublasOperation_t trans, int n, int nrhs, const cuComplex *A, int lda, const int *devIpiv, cuComplex *B, int ldb, int *devInfo ); cusolverStatus_t cusolverDnZgetrs(cusolverDnHandle_t handle, cublasOperation_t trans, int n, int nrhs, const cuDoubleComplex *A, int lda, const int *devIpiv, cuDoubleComplex *B, int ldb, int *devInfo );
This function solves a linear system of multiple righthand sides
$\mathrm{op(A)}*X=B$ 
where A is an n×n matrix, and was LUfactored by getrf, that is, lower trianular part of A is L, and upper triangular part (including diagonal elements) of A is U. B is a n×nrhs righthand side matrix.
The input parameter trans is defined by
$\text{op}(A)=\left\{\begin{array}{ll}A& \text{if}\mathsf{\text{trans == CUBLAS\_OP\_N}}\\ {A}^{T}& \text{if}\mathsf{\text{trans == CUBLAS\_OP\_T}}\\ {A}^{H}& \text{if}\mathsf{\text{trans == CUBLAS\_OP\_C}}\end{array}\right.$
The input parameter devIpiv is an output of getrf. It contains pivot indices, which are used to permutate righthand sides.
If output parameter devInfo = i (less than zero), the ith parameter is wrong (not counting handle).
The user can combine getrf and getrs to complete a linear solver. Please visit cuSOLVER Library Samples  getrs for a code example.
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
trans  host  input  Operation op(A) that is non or (conj.) transpose. 
n  host  input  Number of rows and columns of matrix A. 
nrhs  host  input  Number of righthand sides. 
A  device  input  <type> array of dimension lda * n with lda is not less than max(1,n). 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
devIpiv  device  input  Array of size at least n, containing pivot indices. 
B  device  output  <type> array of dimension ldb * nrhs with ldb is not less than max(1,n). 
ldb  host  input  Leading dimension of twodimensional array used to store matrix B. 
devInfo  device  output  If devInfo = 0, the operation is successful. if devInfo = i, the ith parameter is wrong (not counting handle). 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (n<0 or lda<max(1,n) or ldb<max(1,n)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.9. cusolverDnGetrs()[DEPRECATED]
[[DEPRECATED]] use cusolverDnXgetrs() instead. The routine will be removed in the next major release.
cusolverStatus_t cusolverDnGetrs( cusolverDnHandle_t handle, cusolverDnParams_t params, cublasOperation_t trans, int64_t n, int64_t nrhs, cudaDataType dataTypeA, const void *A, int64_t lda, const int64_t *ipiv, cudaDataType dataTypeB, void *B, int64_t ldb, int *info )
This function solves a linear system of multiple righthand sides
$\mathrm{op(A)}*X=B$ 
where A is a n×n matrix, and was LUfactored by cusolverDnGetrf, that is, lower trianular part of A is L, and upper triangular part (including diagonal elements) of A is U. B is a n×nrhs righthand side matrix using the generic API interface.
The input parameter trans is defined by
$\text{op}(A)=\left\{\begin{array}{ll}A& \text{if}\mathsf{\text{trans == CUBLAS\_OP\_N}}\\ {A}^{T}& \text{if}\mathsf{\text{trans == CUBLAS\_OP\_T}}\\ {A}^{H}& \text{if}\mathsf{\text{trans == CUBLAS\_OP\_C}}\end{array}\right.$
The input parameter ipiv is an output of cusolverDnGetrf. It contains pivot indices, which are used to permutate righthand sides.
If output parameter info = i (less than zero), the ith parameter is wrong (not counting handle).
The user can combine cusolverDnGetrf and cusolverDnGetrs to complete a linear solver.
Currently, cusolverDnGetrs supports only the default algorithm.
CUSOLVER_ALG_0 or NULL  Default algorithm. 
List of input arguments for cusolverDnGetrss:
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
params  host  input  Structure with information collected by cusolverDnSetAdvOptions. 
trans  host  input  Operation op(A) that is non or (conj.) transpose. 
n  host  input  Number of rows and columns of matrix A. 
nrhs  host  input  Number of righthand sides. 
dataTypeA  host  in  Data type of array A. 
A  device  input  Array of dimension lda * n with lda is not less than max(1,n). 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
ipiv  device  input  Array of size at least n, containing pivot indices. 
dataTypeB  host  in  Data type of array B. 
B  device  output  <type> array of dimension ldb * nrhs with ldb is not less than max(1,n). 
ldb  host  input  Leading dimension of twodimensional array used to store matrix B. 
info  device  output  If info = 0, the operation is successful. if info = i, the ith parameter is wrong (not counting handle). 
The generic API has two different types, dataTypeA is data type of the matrix A and dataTypeB is data type of the matrix B. cusolverDnGetrs only supports the following four combinations.
DataTypeA  dataTypeB  Meaning 
CUDA_R_32F  CUDA_R_32F  SGETRS 
CUDA_R_64F  CUDA_R_64F  DGETRS 
CUDA_C_32F  CUDA_C_32F  CGETRS 
CUDA_C_64F  CUDA_C_64F  ZGETRS 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (n<0 or lda<max(1,n) or ldb<max(1,n)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.10. cusolverDn<t1><t2>gesv()
These functions are modelled after functions DSGESV and ZCGESV from LAPACK. They compute the solution of a system of linear equations with one or multiple right hand sides using mixed precision iterative refinement techniques based on the LU factorization Xgesv. These functions are similar in term of functionalities to the full precision LU solver (Xgesv, where X denotes Z,C,D,S) but it uses lower precision internally in order to provide faster time to solution, from here cames the name mixed precision. Mixed precision iterative refinement techniques means that the solver compute an LU factorization in lower precision and then iteratively refine the solution to achieve the accuracy of the Inputs/Outputs datatype precision. The <t1> corresponds to the Inputs/Outputs datatype precision while <t2> represent the internal lower precision at which the factorization will be carried on.
$A\times X=B$ 
Where A is nbyn matrix and X and B are nbynrhs matrices.
Functions API are designed to be as close as possible to LAPACK API to be considered as a quick and easy dropin replacement. Parameters and behavior are mostly the same as LAPACK counterparts. Description of these functions and differences from LAPACK is given below. <t1><t2>gesv() functions are designated by two floating point precisions The <t1> corresponds to the main precision (e.g., Inputs/Outputs datatype precision) and the <t2> represent the internal lower precision at which the factorization will be carried on. cusolver<t1><t2>gesv() first attempts to factorize the matrix in lower precision and use this factorization within an iterative refinement procedure to obtain a solution with same normwise backward error as the main precision <t1>. If the approach fails to converge, then the method fallback to the main precision factorization and solve (Xgesv) such a way that there is always a good solution at the output of these functions. If <t2> is equal to <t1>, then it is not a mixed precision process but rather a full one precision factorisation, solve and refinement within the same main precision.
ITER > ITERMAX
or for all the RHS we have:
RNRM < SQRT(N)*XNRM*ANRM*EPS*BWDMAX
where
 ITER is the number of the current iteration in the iterative refinement process
 RNRM is the infinitynorm of the residual
 XNRM is the infinitynorm of the solution
 ANRM is the infinityoperatornorm of the matrix A
 EPS is the machine epsilon that matches LAPACK <t1>LAMCH('Epsilon')
The function returns value describes the results of the solving process. A CUSOLVER_STATUS_SUCCESS indicates that the function finished with success otherwise, it indicates if one of the API arguments is incorrect, or if the function did not finish with success. More details about the error will be in the niters and the dinfo API parameters. See their description below for more details. User should provide the required workspace allocated on device memory. The amount of bytes required can be queried by calling the respective function <t1><t2>gesv_bufferSize().
Note that in addition to the two mixed precision functions available in LAPACK (e.g., dsgesv and zcgesv), we provide a large set of mixed precision functions that include half, bfloat and tensorfloat as a lower precision as well as same precision functions (e.g., main and lowest precision are equal <t2> is equal to <t1>). The following table specifies which precisions will be used for which interface function.
Tensor Float (TF32), introduced with NVIDIA Ampere Architecture GPUs, is the most robust tensor core accelerated compute mode for the iterative refinement solver. It is able to solve the widest range of problems in HPC arising from different applications and provides up to 4X and 5X speedup for real and complex systems, respectively. On Volta and Turing architecture GPUs, half precision tensor core acceleration is recommended. In cases where the iterative refinement solver fails to converge to the desired accuracy (main precision, INOUT data precision), it is recommended to use main precision as internal lowest precision (i.e., cusolverDn[DD,ZZ]gesv for the FP64 case).
Interface function  Main precision (matrix, rhs and solution datatype)  Lowest precision allowed to be used internally 

cusolverDnZZgesv  cuDoubleComplex  double complex 
cusolverDnZCgesv *has LAPACK counterparts  cuDoubleComplex  single complex 
cusolverDnZKgesv  cuDoubleComplex  half complex 
cusolverDnZEgesv  cuDoubleComplex  bfloat complex 
cusolverDnZYgesv  cuDoubleComplex  tensorfloat complex 
cusolverDnCCgesv  cuComplex  single complex 
cusolverDnCKgesv  cuComplex  half complex 
cusolverDnCEgesv  cuComplex  bfloat complex 
cusolverDnCYgesv  cuComplex  tensorfloat complex 
cusolverDnDDgesv  double  double 
cusolverDnDSgesv *has LAPACK counterparts  double  single 
cusolverDnDHgesv  double  half 
cusolverDnDBgesv  double  bfloat 
cusolverDnDXgesv  double  tensorfloat 
cusolverDnSSgesv  float  single 
cusolverDnSHgesv  float  half 
cusolverDnSBgesv  float  bfloat 
cusolverDnSXgesv  float  tensorfloat 
cusolverStatus_t cusolverDnZZgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnZCgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnZKgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnZEgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnZYgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnCCgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuComplex * dA, int ldda, int * dipiv, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnCKgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuComplex * dA, int ldda, int * dipiv, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnCEgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuComplex * dA, int ldda, int * dipiv, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnCYgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, cuComplex * dA, int ldda, int * dipiv, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDDgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDSgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDHgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDBgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDXgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnSSgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, float * dA, int ldda, int * dipiv, float * dB, int lddb, float * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnSHgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, float * dA, int ldda, int * dipiv, float * dB, int lddb, float * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnSBgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, float * dA, int ldda, int * dipiv, float * dB, int lddb, float * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnSXgesv_bufferSize( cusolverHandle_t handle, int n, int nrhs, float * dA, int ldda, int * dipiv, float * dB, int lddb, float * dX, int lddx, void * dwork, size_t * lwork_bytes);
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cusolverDN library context. 
n  host  input  Number of rows and columns of square matrix A. Should be nonnegative. 
nrhs  host  input  Number of right hand sides to solve. Should be nonnegative. 
dA  device  None  Matrix A with size nbyn. Can be NULL. 
ldda  host  input  Leading dimension of twodimensional array used to store matrix A. lda >= n. 
dipiv  device  None  Pivoting sequence. Not used and can be NULL. 
dB  device  None  Set of right hand sides B of size nbynrhs. Can be NULL. 
lddb  host  input  Leading dimension of twodimensional array used to store matrix of right hand sides B. ldb >= n. 
dX  device  None  Set of soultion vectors X of size nbynrhs. Can be NULL. 
lddx  host  input  Leading dimension of twodimensional array used to store matrix of solution vectors X. ldx >= n. 
dwork  device  none  Pointer to device workspace. Not used and can be NULL. 
lwork_bytes  host  output  Pointer to a variable where required size of temporary workspace in bytes will be stored. Can't be NULL. 
cusolverStatus_t cusolverDnZZgesv( cusolverDnHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnZCgesv( cusolverDnHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnZKgesv( cusolverDnHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnZEgesv( cusolverDnHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnZYgesv( cusolverDnHandle_t handle, int n, int nrhs, cuDoubleComplex * dA, int ldda, int * dipiv, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnCCgesv( cusolverDnHandle_t handle, int n, int nrhs, cuComplex * dA, int ldda, int * dipiv, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnCKgesv( cusolverDnHandle_t handle, int n, int nrhs, cuComplex * dA, int ldda, int * dipiv, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnCEgesv( cusolverDnHandle_t handle, int n, int nrhs, cuComplex * dA, int ldda, int * dipiv, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnCYgesv( cusolverDnHandle_t handle, int n, int nrhs, cuComplex * dA, int ldda, int * dipiv, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDDgesv( cusolverDnHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDSgesv( cusolverDnHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDHgesv( cusolverDnHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDBgesv( cusolverDnHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDXgesv( cusolverDnHandle_t handle, int n, int nrhs, double * dA, int ldda, int * dipiv, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnSSgesv( cusolverDnHandle_t handle, int n, int nrhs, float * dA, int ldda, int * dipiv, float * dB, int lddb, float * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnSHgesv( cusolverDnHandle_t handle, int n, int nrhs, float * dA, int ldda, int * dipiv, float * dB, int lddb, float * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnSBgesv( cusolverDnHandle_t handle, int n, int nrhs, float * dA, int ldda, int * dipiv, float * dB, int lddb, float * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnSXgesv( cusolverDnHandle_t handle, int n, int nrhs, float * dA, int ldda, int * dipiv, float * dB, int lddb, float * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo);
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cusolverDN library context. 
n  host  input  Number of rows and columns of square matrix A. Should be nonnegative. 
nrhs  host  input  Number of right hand sides to solve. Should be nonnegative. 
dA  device  in/out  Matrix A with size nbyn. Can't be NULL. On return  unchanged if the iterative refinement process converged. If not  will contains the factorization of the matrix A in the main precision <T1> (A = P * L * U, where P  permutation matrix defined by vector ipiv, L and U  lower and upper triangular matrices). 
ldda  host  input  Leading dimension of twodimensional array used to store matrix A. lda >= n. 
dipiv  device  output  Vector that defines permutation for the factorization  row i was interchanged with row ipiv[i] 
dB  device  input  Set of right hand sides B of size nbynrhs . Can't be NULL. 
lddb  host  input  Leading dimension of twodimensional array used to store matrix of right hand sides B. ldb >= n. 
dX  device  output  Set of soultion vectors X of size nbynrhs . Can't be NULL. 
lddx  host  input  Leading dimension of twodimensional array used to store matrix of solution vectors X. ldx >= n. 
dWorkspace  device  input  Pointer to an allocated workspace in device memory of size lwork_bytes. 
lwork_bytes  host  input  Size of the allocated device workspace. Should be at least what was returned by cusolverDn<T1><T2>gesv_bufferSize() function. 
niters  host  output  If iter is

dinfo  device  output  Status of the IRS solver on the return. If 0  solve was successful. If dinfo = i then ith argument is not valid. If dinfo = i, then U(i,i) computed in main precision is exactly zero. The factorization has been completed, but the factor U is exactly singular, so the solution could not be computed. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed, for example:

CUSOLVER_STATUS_ARCH_MISMATCH  The IRS solver supports compute capability 7.0 and above. The lowest precision options CUSOLVER_[CR]_16BF and CUSOLVER_[CR]_TF32 are only available on compute capability 8.0 and above. 
CUSOLVER_STATUS_INVALID_WORKSPACE  lwork_bytes is smaller than the required workspace. 
CUSOLVER_STATUS_IRS_OUT_OF_RANGE  Numerical error related to niters <0, see niters description for more details. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal error occured, check the dinfo and the niters arguments for more details. 
2.4.2.11. cusolverDnIRSXgesv()
 the main precision (Inputs/Outputs precision) of the solver
 the lowest precision to be used internally by the solver
 the refinement solver type
 the maximum allowed number of iterations in the refinement phase
 the tolerance of the refinement solver
 the fallback to main precision
 and more
The function returns value describes the results of the solving process. A CUSOLVER_STATUS_SUCCESS indicates that the function finished with success otherwise, it indicates if one of the API arguments is incorrect, or if the configurations of params/infos structure is incorrect or if the function did not finish with success. More details about the error can be found by checking the niters and the dinfo API parameters. See their description below for further details. User should provide the required workspace allocated on device for the cusolverDnIRSXgesv() function. The amount of bytes required for the function can be queried by calling the respective function cusolverDnIRSXgesv_bufferSize(). Note that, if the user would like a praticular configuration to be set via the params structure, it should be set before the call to cusolverDnIRSXgesv_bufferSize() to get the size of the required workspace.
Tensor Float (TF32), introduced with NVIDIA Ampere Architecture GPUs, is the most robust tensor core accelerated compute mode for the iterative refinement solver. It is able to solve the widest range of problems in HPC arising from different applications and provides up to 4X and 5X speedup for real and complex systems, respectively. On Volta and Turing architecture GPUs, half precision tensor core acceleration is recommended. In cases where the iterative refinement solver fails to converge to the desired accuracy (main precision, INOUT data precision), it is recommended to use main precision as internal lowest precision.
The following table provides all possible combinations values for the lowest precision corresponding to the Inputs/Outputs data type. Note that if the lowest precision matches the Inputs/Outputs datatype, then the main precision factorization will be used.
Inputs/Outputs Data Type (e.g., main precision)  Supported values for the lowest precision 

CUSOLVER_C_64F  CUSOLVER_C_64F, CUSOLVER_C_32F, CUSOLVER_C_16F, CUSOLVER_C_16BF, CUSOLVER_C_TF32 
CUSOLVER_C_32F  CUSOLVER_C_32F, CUSOLVER_C_16F, CUSOLVER_C_16BF, CUSOLVER_C_TF32 
CUSOLVER_R_64F  CUSOLVER_R_64F, CUSOLVER_R_32F, CUSOLVER_R_16F, CUSOLVER_R_16BF, CUSOLVER_R_TF32 
CUSOLVER_R_32F  CUSOLVER_R_32F, CUSOLVER_R_16F, CUSOLVER_R_16BF, CUSOLVER_R_TF32 
cusolverStatus_t cusolverDnIRSXgesv_bufferSize( cusolverDnHandle_t handle, cusolverDnIRSParams_t gesv_irs_params, cusolver_int_t n, cusolver_int_t nrhs, size_t * lwork_bytes);
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cusolverDn library context. 
params  host  input  Xgesv configuration parameters 
n  host  input  Number of rows and columns of the square matrix A. Should be nonnegative. 
nrhs  host  input  Number of right hand sides to solve. Should be nonnegative. Note that nrhs is limited to 1 if the selected IRS refinement solver is CUSOLVER_IRS_REFINE_GMRES, CUSOLVER_IRS_REFINE_GMRES_GMRES, CUSOLVER_IRS_REFINE_CLASSICAL_GMRES. 
lwork_bytes  host  out  Pointer to a variable, where the required size in bytes, of the workspace will be stored after a call to cusolverDnIRSXgesv_bufferSize. Can't be NULL. 
cusolverStatus_t cusolverDnIRSXgesv( cusolverDnHandle_t handle, cusolverDnIRSParams_t gesv_irs_params, cusolverDnIRSInfos_t gesv_irs_infos, int n, int nrhs, void * dA, int ldda, void * dB, int lddb, void * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * dinfo);
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cusolverDn library context. 
gesv_irs_params  host  input  Configuration parameters structure, can serve one or more calls to any IRS solver 
gesv_irs_infos  host  in/out  Info structure, where information about a particular solve will be stored. The gesv_irs_infos structure correspond to a particular call. Thus different calls requires different gesv_irs_infos structure otherwise, it will be overwritten. 
n  host  input  Number of rows and columns of square matrix A. Should be nonnegative. 
nrhs  host  input  Number of right hand sides to solve. Should be nonnegative. Note that, nrhs is limited to 1 if the selected IRS refinement solver is CUSOLVER_IRS_REFINE_GMRES, CUSOLVER_IRS_REFINE_GMRES_GMRES, CUSOLVER_IRS_REFINE_CLASSICAL_GMRES. 
dA  device  in/out  Matrix A with size nbyn. Can't be NULL. On return  will contain the factorization of the matrix A in the main precision (A = P * L * U, where P  permutation matrix defined by vector ipiv, L and U  lower and upper triangular matrices) if the iterative refinement solver was set to CUSOLVER_IRS_REFINE_NONE and the lowest precision is equal to the main precision (Inputs/Ouputs datatype), or if the iterative refinement solver did not converge and the fallback to main precision was enabled (fallback enabled is the default setting); unchanged otherwise. 
ldda  host  input  Leading dimension of twodimensional array used to store matrix A. lda >= n. 
dB  device  input  Set of right hand sides B of size nbynrhs . Can't be NULL. 
lddb  host  input  Leading dimension of twodimensional array used to store matrix of right hand sides B. ldb >= n. 
dX  device  output  Set of soultion vectors X of size nbynrhs . Can't be NULL. 
lddx  host  input  Leading dimension of twodimensional array used to store matrix of solution vectors X. ldx >= n. 
dWorkspace  device  input  Pointer to an allocated workspace in device memory of size lwork_bytes. 
lwork_bytes  host  input  Size of the allocated device workspace. Should be at least what was returned by cusolverDnIRSXgesv_bufferSize() function 
niters  host  output  If iter is

dinfo  device  output  Status of the IRS solver on the return. If 0  solve was successful. If dinfo = i then ith argument is not valid. If dinfo = i, then U(i,i) computed in main precision is exactly zero. The factorization has been completed, but the factor U is exactly singular, so the solution could not be computed. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed, for example:

CUSOLVER_STATUS_ARCH_MISMATCH  The IRS solver supports compute capability 7.0 and above. The lowest precision options CUSOLVER_[CR]_16BF and CUSOLVER_[CR]_TF32 are only available on compute capability 8.0 and above. 
CUSOLVER_STATUS_INVALID_WORKSPACE  lwork_bytes is smaller than the required workspace. Could happen if the users called cusolverDnIRSXgesv_bufferSize() function, then changed some of the configurations setting such as the lowest precision. 
CUSOLVER_STATUS_IRS_OUT_OF_RANGE  Numerical error related to niters <0, see niters description for more details. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal error occured, check the dinfo and the niters arguments for more details. 
CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED  The configuration parameter gesv_irs_params structure was not created. 
CUSOLVER_STATUS_IRS_PARAMS_INVALID  One of the configuration parameter in the gesv_irs_params structure is not valid. 
CUSOLVER_STATUS_IRS_PARAMS_INVALID_PREC  The main and/or the lowest precision configuration parameter in the gesv_irs_params structure is not valid, check the table above for the supported combinations. 
CUSOLVER_STATUS_IRS_PARAMS_INVALID_MAXITER  The maxiter configuration parameter in the gesv_irs_params structure is not valid. 
CUSOLVER_STATUS_IRS_PARAMS_INVALID_REFINE  The refinement solver configuration parameter in the gesv_irs_params structure is not valid. 
CUSOLVER_STATUS_IRS_NOT_SUPPORTED  One of the configuration parameter in the gesv_irs_params structure is not supported. For example if nrhs >1, and refinement solver was set to CUSOLVER_IRS_REFINE_GMRES. 
CUSOLVER_STATUS_IRS_INFOS_NOT_INITIALIZED  The information structure gesv_irs_infos was not created. 
CUSOLVER_STATUS_ALLOC_FAILED  CPU memory allocation failed, most likely during the allocation of the residual array that store the residual norms. 
2.4.2.12. cusolverDn<t>geqrf()
cusolverStatus_t cusolverDnSgeqrf_bufferSize(cusolverDnHandle_t handle, int m, int n, float *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnDgeqrf_bufferSize(cusolverDnHandle_t handle, int m, int n, double *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnCgeqrf_bufferSize(cusolverDnHandle_t handle, int m, int n, cuComplex *A, int lda, int *Lwork ); cusolverStatus_t cusolverDnZgeqrf_bufferSize(cusolverDnHandle_t handle, int m, int n, cuDoubleComplex *A, int lda, int *Lwork );
cusolverStatus_t cusolverDnSgeqrf(cusolverDnHandle_t handle, int m, int n, float *A, int lda, float *TAU, float *Workspace, int Lwork, int *devInfo ); cusolverStatus_t cusolverDnDgeqrf(cusolverDnHandle_t handle, int m, int n, double *A, int lda, double *TAU, double *Workspace, int Lwork, int *devInfo );
cusolverStatus_t cusolverDnCgeqrf(cusolverDnHandle_t handle, int m, int n, cuComplex *A, int lda, cuComplex *TAU, cuComplex *Workspace, int Lwork, int *devInfo ); cusolverStatus_t cusolverDnZgeqrf(cusolverDnHandle_t handle, int m, int n, cuDoubleComplex *A, int lda, cuDoubleComplex *TAU, cuDoubleComplex *Workspace, int Lwork, int *devInfo );
This function computes the QR factorization of a m×n matrix
$A=Q*R$ 
where A is an m×n matrix, Q is an m×n matrix, and R is a n×n upper triangular matrix.
The user has to provide working space which is pointed by input parameter Workspace. The input parameter Lwork is size of the working space, and it is returned by geqrf_bufferSize().
The matrix R is overwritten in upper triangular part of A, including diagonal elements.
The matrix Q is not formed explicitly, instead, a sequence of householder vectors are stored in lower triangular part of A. The leading nonzero element of householder vector is assumed to be 1 such that output parameter TAU contains the scaling factor τ. If v is original householder vector, q is the new householder vector corresponding to τ, satisying the following relation
$I2*v*{v}^{H}=I\tau *q*{q}^{H}$ 
If output parameter devInfo = i (less than zero), the ith parameter is wrong (not counting handle).
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
m  host  input  Number of rows of matrix A. 
n  host  input  Number of columns of matrix A. 
A  device  in/out  <type> array of dimension lda * n with lda is not less than max(1,m). 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
TAU  device  output  <type> array of dimension at least min(m,n). 
Workspace  device  in/out  Working space, <type> array of size Lwork. 
Lwork  host  input  Size of working array Workspace. 
devInfo  device  output  If devInfo = 0, the LU factorization is successful. if devInfo = i, the ith parameter is wrong (not counting handle). 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (m,n<0 or lda<max(1,m)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.13. cusolverDnGeqrf()[DEPRECATED]
[[DEPRECATED]] use cusolverDnXgeqrf() instead. The routine will be removed in the next major release.
cusolverStatus_t cusolverDnGeqrf_bufferSize( cusolverDnHandle_t handle, cusolverDnParams_t params, int64_t m, int64_t n, cudaDataType dataTypeA, const void *A, int64_t lda, cudaDataType dataTypeTau, const void *tau, cudaDataType computeType, size_t *workspaceInBytes )
cusolverStatus_t cusolverDnGeqrf( cusolverDnHandle_t handle, cusolverDnParams_t params, int64_t m, int64_t n, cudaDataType dataTypeA, void *A, int64_t lda, cudaDataType dataTypeTau, void *tau, cudaDataType computeType, void *pBuffer, size_t workspaceInBytes, int *info )
computes the QR factorization of an m×n matrix
$A=Q*R$ 
where A is a m×n matrix, Q is an m×n matrix, and R is an n×n upper triangular matrix using the generic API interface.
The user has to provide working space which is pointed by input parameter pBuffer. The input parameter workspaceInBytes is size in bytes of the working space, and it is returned by cusolverDnGeqrf_bufferSize().
The matrix R is overwritten in upper triangular part of A, including diagonal elements.
The matrix Q is not formed explicitly, instead, a sequence of householder vectors are stored in lower triangular part of A. The leading nonzero element of householder vector is assumed to be 1 such that output parameter TAU contains the scaling factor τ. If v is original householder vector, q is the new householder vector corresponding to τ, satisying the following relation
$I2*v*{v}^{H}=I\tau *q*{q}^{H}$ 
If output parameter info = i (less than zero), the ith parameter is wrong (not counting handle).
Currently, cusolverDnGeqrf supports only the default algorithm.
CUSOLVER_ALG_0 or NULL  Default algorithm. 
List of input arguments for cusolverDnGeqrf_bufferSize and cusolverDnGeqrf:
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cuSolverDN library context. 
params  host  input  Structure with information collected by cusolverDnSetAdvOptions. 
m  host  input  Number of rows of matrix A. 
n  host  input  Number of columns of matrix A. 
dataTypeA  host  in  Data type of array A. 
A  device  in/out  Array of dimension lda * n with lda is not less than max(1,m). 
lda  host  input  Leading dimension of twodimensional array used to store matrix A. 
TAU  device  output  Array of dimension at least min(m,n). 
computeType  host  in  Data type of computation. 
pBuffer  device  in/out  Working space. Array of type void of size workspaceInBytes bytes. 
workspaceInBytes  host  input  Size in bytes of working array pBuffer. 
info  device  output  If info = 0, the LU factorization is successful. if info = i, the ith parameter is wrong (not counting handle). 
The generic API has two different types, dataTypeA is data type of the matrix A and array tau and computeType is compute type of the operation. cusolverDnGeqrf only supports the following four combinations.
DataTypeA  ComputeType  Meaning 
CUDA_R_32F  CUDA_R_32F  SGEQRF 
CUDA_R_64F  CUDA_R_64F  DGEQRF 
CUDA_C_32F  CUDA_C_32F  CGEQRF 
CUDA_C_64F  CUDA_C_64F  ZGEQRF 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed (m,n<0 or lda<max(1,m)). 
CUSOLVER_STATUS_ARCH_MISMATCH  The device only supports compute capability 2.0 and above. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal operation failed. 
2.4.2.14. cusolverDn<t1><t2>gels()
These functions compute the solution of a system of linear equations with one or multiple right hand sides using mixed precision iterative refinement techniques based on the QR factorization Xgels. These functions are similar in term of functionalities to the full precision LAPACK QR (least squares) solver (Xgels, where X denotes Z,C,D,S) but it uses lower precision internally in order to provide faster time to solution, from here cames the name mixed precision. Mixed precision iterative refinement techniques means that the solver compute an QR factorization in lower precision and then iteratively refine the solution to achieve the accuracy of the Inputs/Outputs datatype precision. The <t1> corresponds to the Inputs/Outputs datatype precision while <t2> represent the internal lower precision at which the factorization will be carried on.
$A\times X=B$ 
Where A is mbyn matrix and X is nbynrhs and B is mbynrhs matrices.
Functions API are designed to be as close as possible to LAPACK API to be considered as a quick and easy dropin replacement. Description of these functions is given below. <t1><t2>gels() functions are designated by two floating point precisions The <t1> corresponds to the main precision (e.g., Inputs/Outputs datatype precision) and the <t2> represent the internal lower precision at which the factorization will be carried on. cusolver<t1><t2>gels() first attempts to factorize the matrix in lower precision and use this factorization within an iterative refinement procedure to obtain a solution with same normwise backward error as the main precision <t1>. If the approach fails to converge, then the method fallback to the main precision factorization and solve (Xgels) such a way that there is always a good solution at the output of these functions. If <t2> is equal to <t1>, then it is not a mixed precision process but rather a full one precision factorisation, solve and refinement within the same main precision.
ITER > ITERMAX
or for all the RHS we have:
RNRM < SQRT(N)*XNRM*ANRM*EPS*BWDMAX
where
 ITER is the number of the current iteration in the iterative refinement process
 RNRM is the infinitynorm of the residual
 XNRM is the infinitynorm of the solution
 ANRM is the infinityoperatornorm of the matrix A
 EPS is the machine epsilon that matches LAPACK <t1>LAMCH('Epsilon')
The function returns value describes the results of the solving process. A CUSOLVER_STATUS_SUCCESS indicates that the function finished with success otherwise, it indicates if one of the API arguments is incorrect, or if the function did not finish with success. More details about the error will be in the niters and the dinfo API parameters. See their description below for more details. User should provide the required workspace allocated on device memory. The amount of bytes required can be queried by calling the respective function <t1><t2>gels_bufferSize().
We provide a large set of mixed precision functions that include half, bfloat and tensorfloat as a lower precision as well as same precision functions (e.g., main and lowest precision are equal <t2> is equal to <t1>). The following table specifies which precisions will be used for which interface function:
Tensor Float (TF32), introduced with NVIDIA Ampere Architecture GPUs, is the most robust tensor core accelerated compute mode for the iterative refinement solver. It is able to solve the widest range of problems in HPC arising from different applications and provides up to 4X and 5X speedup for real and complex systems, respectively. On Volta and Turing architecture GPUs, half precision tensor core acceleration is recommended. In cases where the iterative refinement solver fails to converge to the desired accuracy (main precision, INOUT data precision), it is recommended to use main precision as internal lowest precision (i.e., cusolverDn[DD,ZZ]gels for the FP64 case).
Interface function  Main precision (matrix, rhs and solution datatype)  Lowest precision allowed to be used internally 

cusolverDnZZgels  cuDoubleComplex  double complex 
cusolverDnZCgels  cuDoubleComplex  single complex 
cusolverDnZKgels  cuDoubleComplex  half complex 
cusolverDnZEgels  cuDoubleComplex  bfloat complex 
cusolverDnZYgels  cuDoubleComplex  tensorfloat complex 
cusolverDnCCgels  cuComplex  single complex 
cusolverDnCKgels  cuComplex  half complex 
cusolverDnCEgels  cuComplex  bfloat complex 
cusolverDnCYgels  cuComplex  tensorfloat complex 
cusolverDnDDgels  double  double 
cusolverDnDSgels  double  single 
cusolverDnDHgels  double  half 
cusolverDnDBgels  double  bfloat 
cusolverDnDXgels  double  tensorfloat 
cusolverDnSSgels  float  single 
cusolverDnSHgels  float  half 
cusolverDnSBgels  float  bfloat 
cusolverDnSXgels  float  tensorfloat 
cusolverStatus_t cusolverDnZZgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnZCgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnZKgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnZEgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnZYgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnCCgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuComplex * dA, int ldda, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnCKgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuComplex * dA, int ldda, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnCEgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuComplex * dA, int ldda, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnCYgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, cuComplex * dA, int ldda, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDDgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDSgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDHgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDBgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnDXgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnSSgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, float * dA, int ldda, float * dB, int lddb, float * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnSHgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, float * dA, int ldda, float * dB, int lddb, float * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnSBgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, float * dA, int ldda, float * dB, int lddb, float * dX, int lddx, void * dwork, size_t * lwork_bytes); cusolverStatus_t cusolverDnSXgels_bufferSize( cusolverHandle_t handle, int m, int n, int nrhs, float * dA, int ldda, float * dB, int lddb, float * dX, int lddx, void * dwork, size_t * lwork_bytes);
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cusolverDN library context. 
m  host  input  Number of rows of the matrix A. Should be nonnegative and n<=m 
n  host  input  Number of columns of the matrix A. Should be nonnegative and n<=m. 
nrhs  host  input  Number of right hand sides to solve. Should be nonnegative. 
dA  device  None  Matrix A with size mbyn. Can be NULL. 
ldda  host  input  Leading dimension of twodimensional array used to store matrix A. ldda >= m. 
dB  device  None  Set of right hand sides B of size mbynrhs. Can be NULL. 
lddb  host  input  Leading dimension of twodimensional array used to store matrix of right hand sides B. lddb >= max(1,m). 
dX  device  None  Set of soultion vectors X of size nbynrhs. Can be NULL. 
lddx  host  input  Leading dimension of twodimensional array used to store matrix of solution vectors X. lddx >= max(1,n). 
dwork  device  none  Pointer to device workspace. Not used and can be NULL. 
lwork_bytes  host  output  Pointer to a variable where required size of temporary workspace in bytes will be stored. Can't be NULL. 
cusolverStatus_t cusolverDnZZgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnZCgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnZKgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnZEgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnZYgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuDoubleComplex * dA, int ldda, cuDoubleComplex * dB, int lddb, cuDoubleComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnCCgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuComplex * dA, int ldda, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnCKgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuComplex * dA, int ldda, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnCEgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuComplex * dA, int ldda, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnCYgels( cusolverDnHandle_t handle, int m, int n, int nrhs, cuComplex * dA, int ldda, cuComplex * dB, int lddb, cuComplex * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDDgels( cusolverDnHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDSgels( cusolverDnHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDHgels( cusolverDnHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDBgels( cusolverDnHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnDXgels( cusolverDnHandle_t handle, int m, int n, int nrhs, double * dA, int ldda, double * dB, int lddb, double * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnSSgels( cusolverDnHandle_t handle, int m, int n, int nrhs, float * dA, int ldda, float * dB, int lddb, float * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnSHgels( cusolverDnHandle_t handle, int m, int n, int nrhs, float * dA, int ldda, float * dB, int lddb, float * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnSBgels( cusolverDnHandle_t handle, int m, int n, int nrhs, float * dA, int ldda, float * dB, int lddb, float * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo); cusolverStatus_t cusolverDnSXgels( cusolverDnHandle_t handle, int m, int n, int nrhs, float * dA, int ldda, float * dB, int lddb, float * dX, int lddx, void * dWorkspace, size_t lwork_bytes, int * niter, int * dinfo);
Parameter  Memory  In/out  Meaning 

handle  host  input  Handle to the cusolverDN library context. 
m  host  input  Number of rows of the matrix A. Should be nonnegative and n<=m 
n  host  input  Number of columns of the matrix A. Should be nonnegative and n<=m. 
nrhs  host  input  Number of right hand sides to solve. Should be nonnegative. 
dA  device  in/out  Matrix A with size mbyn. Can't be NULL. On return  unchanged if the lowest precision is not equal to the main precision and the iterative refinement solver converged,  garbage otherwise. 
ldda  host  input  Leading dimension of twodimensional array used to store matrix A. ldda >= m. 
dB  device  input  Set of right hand sides B of size mbynrhs . Can't be NULL. 
lddb  host  input  Leading dimension of twodimensional array used to store matrix of right hand sides B. lddb >= max(1,m). 
dX  device  output  Set of soultion vectors X of size nbynrhs . Can't be NULL. 
lddx  host  input  Leading dimension of twodimensional array used to store matrix of solution vectors X. lddx >= max(1,n). 
dWorkspace  device  input  Pointer to an allocated workspace in device memory of size lwork_bytes. 
lwork_bytes  host  input  Size of the allocated device workspace. Should be at least what was returned by cusolverDn<T1><T2>gels_bufferSize() function 
niters  host  output  If iter is

dinfo  device  output  Status of the IRS solver on the return. If 0  solve was successful. If dinfo = i then ith argument is not valid. 
CUSOLVER_STATUS_SUCCESS  The operation completed successfully. 
CUSOLVER_STATUS_NOT_INITIALIZED  The library was not initialized. 
CUSOLVER_STATUS_INVALID_VALUE  Invalid parameters were passed, for example:

CUSOLVER_STATUS_ARCH_MISMATCH  The IRS solver supports compute capability 7.0 and above. The lowest precision options CUSOLVER_[CR]_16BF and CUSOLVER_[CR]_TF32 are only available on compute capability 8.0 and above. 
CUSOLVER_STATUS_INVALID_WORKSPACE  lwork_bytes is smaller than the required workspace. 
CUSOLVER_STATUS_IRS_OUT_OF_RANGE  Numerical error related to niters <0, see niters description for more details. 
CUSOLVER_STATUS_INTERNAL_ERROR  An internal error occurred; check the dinfo and the niters arguments for more details. 
2.4.2.15. cusolverDnIRSXgels()
 the main precision (Inputs/Outputs precision) of the solver,
 the lowest precision to be used internally by the solver,
 the refinement solver type
 the maximum allowed number of iterations in the refinement phase
 the tolerance of the refinement solver
 the fallback to main precision
 and others
The function returns value describes the results of the solving process. A CUSOLVER_STATUS_SUCCESS indicates that the function finished with success otherwise, it indicates if one of the API arguments is incorrect, or if the configurations of params/infos structure is incorrect or if the function did not finish with success. More details about the error can be found by checking the niters and the dinfo API parameters. See their description below for further details. Users should provide the required workspace allocated on device for the cusolverDnIRSXgels() function. The amount of bytes required for the function can be queried by calling the respective function cusolverDnIRSXgels_bufferSize(). Note that, if the user would like a praticular configuration to be set via the params structure, it should be set before the call to cusolverDnIRSXgels_bufferSize() to get the size of the required workspace.
The following table provides all possible combinations values for the lowest precision corresponding to the Inputs/Outputs data type. Note that if the lowest precision matches the Inputs/Outputs datatype, then main precision factorization will be used
Tensor Float (TF32), introduced with NVIDIA Ampere Architecture GPUs, is the most robust tensor core accelerated compute mode for the iterative refinement solver. It is able to solve the widest range of problems in HPC arising from different applications and provides up to 4X and 5X speedup for real and complex systems, respectively. On Volta and Turing architecture GPUs, half precision tensor core acceleration is recommended. In cases where the iterative refinement solver fails to converge to the desired accuracy (main precision, INOUT data precision), it is recommended to use main precision as internal lowest precision.
Inputs/Outputs Data Type (e.g., main precision)  Supported values for the lowest precision 

CUSOLVER_C_64F  CUSOLVER_C_64F, CUSOLVER_C_32F, CUSOLVER_C_16F, CUSOLVER_C_16BF, CUSOLVER_C_TF32 
CUSOLVER_C_32F  CUSOLVER_C_32F, CUSOLVER_C_16F, CUSOLVER_C_16BF, CUSOLVER_C_TF32 
CUSOLVER_R_64F  CUSOLVER_R_64F, CUSOLVER_R_32F, CUSOLVER_R_16F, CUSOLVER_R_16BF, CUSOLVER_R_TF32 
CUSOLVER_R_32F  CUSOLVER_R_32F, CUSOLVER_R_16F, CUSOLVER_R_16BF, CUSOLVER_R_TF32 
cusolverStatus_t cusolverDnIRSXgels_bufferSize( cusolverDnHandle_t handle, cusolverDnIRSParams_t gels_irs_params, cusolver_int_t m, cusolver_int_t n, cusolver_int_t nrhs, size_t * lwork_bytes);