cusolverMp C API¶

Library Management¶

`cusolverMpCreate`¶

cusolverStatus_t cusolverMpCreate(
        cusolverMpHandle_t *handle,
        int deviceId,
        cudaStream_t stream)

The function initializes the cusolverMp library handle (cusolverMpHandle_t) which holds the cusolverMp library context. It allocates light hardware resources on the host, and must be called prior to making any other cusolverMp library calls.
Calling any cusolverMp function which uses cusolverMpHandle_t without a previous call of cusolverMpCreate() will return an error.
The cusolverMp library context is tied to the CUDA device provided by deviceId and the CUDA stream streamId.
Only one handle per process and per GPU supported. Sharing a device with multiple processes will result in undefined behavior.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle
deviceId	Host	In	Device that will be assigned to the handle.
streamId	Host	In	Stream that will be assigned to the handle.

See cusolverStatus_t for the description of the return status.

`cusolverMpDestroy`¶

cusolverStatus_t cusolverMpDestroy(
        cusolverMpHandle_t handle)

The function destroy the cusolverMp library handle (cusolverMpHandle_t) which holds the cusolverMp library context.

The cusolverMp library context is tied to the CUDA device provided by deviceId. Only one handle per process and per GPU supported.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle

See cusolverStatus_t for the description of the return status.

`cusolverMpGetStream`¶

cusolverStatus_t cusolverMpGetStream(
        cusolverMpHandle_t handle,
        cudaStream_t stream)

The function returns the stream associated to the handle.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle
stream	Host	In	Stream associated with the handle.

See cusolverStatus_t for the description of the return status.

`cusolverMpGetVersion`¶

cusolverStatus_t cusolverMpGetVersion(
        cusolverMpHandle_t handle,
        int *version)

This function returns the version number of the cusolverMp library.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle
version	Host	In	cusolverMp library version.

See cusolverStatus_t for the description of the return status.

Grid Management¶

`cusolverMpCreateDeviceGrid`¶

cusolverStatus_t cusolverMpCreateDeviceGrid(
        cusolverMpHandle_t handle,
        cudaLibMpGrid_t *grid,
        cal_comm_t comm,
        int32_t numRowDevices,
        int32_t numColDevices,
        cudaLibMpGridMapping_t mapping)

The function initializes the grid opaque data structure. It maps the given resources (communicator, grid dimensions and grid layout) to a grid object.

All the processes defined to be in this grid must enter this function.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle
grid	Host	In/Out	Grid object to be initialized.
comm	Host	In	Communicator that will be associated with the grid.
numRowDevices	Host	In	How many of process rows the grid will contain.
numColDevices	Host	In	How many of process columns the grid will contain.
mapping	Host	In	How to map processes to the grid. See description of cudaLibMpGrid_t for further details. Currently, only `CUDALIBMP_GRID_MAPPING_COL_MAJOR` is supported

See cusolverStatus_t for the description of the return status.

`cusolverMpDestroyGrid`¶

cusolverStatus_t cusolverMpDestroyGrid(
        cudaLibMpGrid_t grid)

The function destroys the given grid object.

All the processes defined to be in this grid must enter this function.

Parameter	Memory	In/Out	Description
grid	Host	In/Out	Grid object to be destroyed.

See cusolverStatus_t for the description of the return status.

Matrix Management¶

`cusolverMpCreateMatrixDesc`¶

cusolverStatus_t cusolverMpCreateMatrixDesc(
        cudaLibMpMatrixDesc_t *descr,
        cudaLibMpGrid_t grid,
        cudaDataType dataType,
        int64_t M_A,
        int64_t N_A,
        int64_t MB_A,
        int64_t NB_A,
        uint32_t RSRC_A,
        uint32_t CSRC_A,
        int64_t LLD_A)

The function initializes cudaLibMpMatrixDesc_t object.

Parameter	Memory	In/Out	Description
descr	Host	In/Out	Matrix descriptor object initialized by this function.
dataType	Host	In	Data type of the matrix A.
M_A	Host	In	Number of rows in the global array A.
N_A	Host	In	Number of columns in the global matrix A.
MB_A	Host	In	Blocking factor used to distribute the rows of the global matrix A.
NB_A	Host	In	Blocking factor used to distribute the columns of the global matrix A.
RSRC_A	Host	In	Process row over which the first row of the matrix A is distributed. Only the value of `0` is currently supported.
CSRC_A	Host	In	Process column over which the first row of the matrix A is distributed. Only the value of `0` is currently supported.
LLD_A	Host	In	Leading dimension of the local matrix.

Supported values for dataType argument:

Data Type of A	computeType	Output Data Type
CUDA_R_64F	64-bit integer values.
CUDA_R_32F	Single precision real values.
CUDA_R_64F	Double precision real values.
CUDA_R_32C	Single precision complex values.
CUDA_R_64C	Double precision complex values

See cusolverStatus_t for the description of the return status.

`cusolverMpDestroyMatrixDesc`¶

cusolverStatus_t cusolverMpDestroyMatrixDesc(
        cudaLibMpMatrixDesc_t descr )

The function destroys cudaLibMpMatrixDesc_t object.

Parameter	Memory	In/Out	Description
descr	Host	In/Out	Matrix descriptor object destroyed by this function.

See cusolverStatus_t for the description of the return status.

Utility¶

`cusolverMpNUMROC`¶

int64_t cusolverMpNUMROC(
        int64_t n,
        int64_t nb,
        uint32_t iproc,
        uint32_t isrcproc,
        uint32_t nprocs)

Computes the number of rows or columns of a distributed matrix owned by the process indicated by iproc argument.

Parameter	Memory	In/Out	Description
n	Host	In	Number of rows or columns in the global distributed matrix.
nb	Host	In	Row or column blocking size of the global matrix.
iproc	Host	In	The coordinate of the process whole local array row or column is to be determined.
isrcproc	Host	In	The coordinate of the process that owns the first row or column of the distributed matrix.
nprocs	Host	In	The total number of row or column processes over which the matrix is distributed.

See cusolverStatus_t for the description of the return status.

`cusolverMpMatrixGatherD2H`¶

cusolverStatus_t cusolverMpMatrixGatherD2H(
        cusolverMpHandle_t handle,
        int64_t M,
        int64_t N,
        void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descrA,
        int root,
        void *h_dst,
        int64_t h_lddst)

Gathers the global distributed matrix A on a buffer provided on process root. The input matrix A is originally distributed using 2D block cyclic format, on output h_dst contains the matrix in column-major format.

Notice that, for this function, the input data is on the device and the output is stored on host memory.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
M	Host	In	Number of rows of the global distributed matrix A.
M	Host	In	Number of columns of the global distributed matrix A.
d_A	Device	In	Number of columns of the global distributed matrix A.
d_A	Device	In	Pointer into the local memory to an array of dimension `(LLD_A, LOCc(JA+N-1))`. On entry, this array contains the local pieces of the M-by-N distributed matrix sub(A).
IA	Host	In	Row index in the global matrix A indicating the first row of sub(A). This function does not make any assuptions on the alignment of `IA`.
JA	Host	In	Column index in the global matrix A indicating the first column of sub(A). This function does not make any assumptions on the alignment of `JA`.
descrA	Host	In	Matrix descriptor of the global matrix A.
root	Host	In	Process ID on which the matrix A will be gathered.
h_dst	Host	In/Out	Destination host buffer on `root` process. On output it contains the global matrix A stored in column-major format. Total size must be at least `M*N` words.
h_lddst	Host	In	Leading dimension of the `h_dst` on `root` process. Must be larger than `M`.

See cusolverStatus_t for the description of the return status.

Warning

This is function is meant as an utility function to verify correctness of the data layouts and it is not intended to achieve high performance on large inputs.

`cusolverMpMatrixScatterH2D`¶

cusolverStatus_t cusolverMpMatrixScatterH2D(
        cusolverMpHandle_t handle,
        int64_t M,
        int64_t N,
        void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descrA,
        int root,
        void *h_src,
        int64_t h_ldsrc)

Scatters the matrix stored in the local buffer h_src from root process to a distributed global matrix A.

The input matrix h_src is stored in column-major format. On ouput, d_A contains the local portions of the global matrix A distributed in 2D block cyclic format.

Notice that, for this function, the input data is on the host and the output is stored on device memory.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
M	Host	In	Number of rows of the global distributed matrix A.
M	Host	In	Number of columns of the global distributed matrix A.
d_A	Device	Out	Pointer into the local memory to an array of dimension `(LLD_A, LOCc(JA+N-1))`. On output, this array contains the local pieces of the M-by-N distributed matrix sub(A).
IA	Host	In	Row index in the global matrix A indicating the first row of sub(A). This function does not make any assuptions on the alignment of `IA`.
JA	Host	In	Column index in the global matrix A indicating the first column of sub(A). This function does not make any assumptions on the alignment of `JA`.
descrA	Host	In	Matrix descriptor of the global matrix A.
root	Host	In	Blocking factor used to distribute the columns of the global matrix A.
h_src	Host	In	Source buffer on `root` process. On input it contains the global `M` by `N` matrix A stored in column-major format.
h_ldsrc	Host	In	Leading dimension of the `h_dst` on `root` process. Must be larger than `M`.

See cusolverStatus_t for the description of the return status.

Warning

This is function is meant as an utility function to verify correctness of the data layouts and it is not intended to achieve high performance on large inputs.

Logging¶

`cusolverMpLoggerSetCallback`¶

cusolverStatus_t cusolverMpLoggerSetCallback(
        cusolverMpLoggerCallback_t callback)

This function sets the logging callback function.

Parameter	Memory	In/Out	Description
callback	Host	In	Pointer to a callback function. See `cusolverMpLoggerCallback_t`.

See cusolverStatus_t for the description of the return status.

Warning

This is an experimental feature.

`cusolverMpLoggerSetFile`¶

cusolverStatus_t cusolverMpLoggerSetFile(
        FILE *file)

This function sets the logging output file. Note: once registered using this function call, the provided file handle must not be closed unless the function is called again to switch to a different file handle.

Parameter	Memory	In/Out	Description
file	Host	In	Pointer to an open file. File should have write permission

See cusolverStatus_t for the description of the return status.

Warning

This is an experimental feature.

`cusolverMpLoggerOpenFile`¶

cusolverStatus_t cusolverMpLoggerOpenFile(
        const char* logFile)

This function opens a logging output file in the given path.

Parameter	Memory	In/Out	Description
logFile	Host	In	Path of the logging output file.

See cusolverStatus_t for the description of the return status.

Warning

This is an experimental feature.

`cusolverMpLoggerSetLevel`¶

cusolverStatus_t cusolverMpLoggerSetLevel(
        int level)

Complete

Parameter	Memory	In/Out	Description
level	Host	In	Value of the logging level. See `cusolverMp Logging`.

See cusolverStatus_t for the description of the return status.

Warning

This is an experimental feature.

`cusolverMpLoggerSetMask`¶

cusolverStatus_t cusolverMpLoggerSetMask(
        int mask)

This function sets the value of the logging mask.

Parameter	Memory	In/Out	Description
mask	Host	In	Value of the logging mask. See `cusolverMp Logging`.

See cusolverStatus_t for the description of the return status.

Warning

This is an experimental feature.

`cusolverMpLoggerForceDisable`¶

cusolverStatus_t cusolverMpLoggerForceDisable(
        int level)

This function disables logging for the entier run.

See cusolverStatus_t for the description of the return status.

Warning

This is an experimental feature.

Factorization¶

`cusolverMpGetrf`¶

cusolverStatus_t cusolverMpGetrf(
        cusolverMpHandle_t handle,
        int64_t M,
        int64_t N,
        void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descrA,
        int64_t  *d_ipiv,
        cudaDataType_t computeType,
        void *d_work,
        size_t workspaceInBytesOnDevice,
        void *h_work,
        size_t workspaceInBytesOnHost,
        int *info)

This routine computes an LU factorization of a general M-by-N distributed matrix sub(A) using partial pivoting. The user can also disable pivoting by setting d_ipiv=NULL.

The factorization has the form:

$sub(A) = P \cdot L \cdot U$

where $P$ is a permutation matrix, $L$ is lower triangular with unit diagonal elements (lower trapezoidal if $m > n$ ), and $U$ is upper triangular (upper trapezoidal if $m < n$ ). $L$ and $U$ are stored in sub(A).

The user can combine cusolverMpGetrf() and cusolverMpGetrs() to solve a system of linear equations.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
M	Host	In	Number of rows of sub(A).
N	Host	In	Number of columns of sub(A).
d_A	Device	In/Out	Pointer to the first entry of the local portion of the global matrix A. On output, the sub(A) is overwritten with the L and U factors.
IA	Host	In	Row index of the first row of the sub(A). This function does not make any assumptions on the alignment of `IA`.
JA	Host	In	Column index of the first column of the sub(A). This function does not make any assumptions on the alignment of `JA`.
descrA	Host	In	Matrix descriptor associated to the global matrix A
d_ipiv	Device	In/Out	Local array of dimension `(LOCr(M_A)+MB_A)`. If the user set `d_ipiv != NULL`, on output, this array contains the pivoting information. `d_ipiv[i]` indicates the global row local row i was swapped with. This array is tied to the distributed matrix A.
computeType	Host	In	Data type used for computations. See table below for supported combinations.
d_work	Host	In/Out	Device workspace of size `workspaceInBytesOnDevice`.
workspaceInBytesOnDevice	Host	In	The size in bytes of the local device workspace needed by the routine as provided by cusolverMpGetrf_bufferSize().
h_work	Host	In/Out	Host workspace of size `workspaceInBytesOnHost`.
workspaceInBytesOnHost	Host	In	The size in bytes of the local host workspace needed by the routine as provided by cusolverMpGetrf_bufferSize()
info	Device	Out	`info < 0` indicates an incorrect value of the i-th argument of the function. `info > 0` indicates the index of the leading minor in the case of a singular matrix.

This routine supports the following combinations of data types:

Data Type of A	computeType	Output Data Type
CUDA_R_32F	CUDA_R_32F	CUDA_R_32F
CUDA_R_64F	CUDA_R_64F	CUDA_R_64F
CUDA_R_32C	CUDA_R_32C	CUDA_R_32C
CUDA_R_64C	CUDA_R_64C	CUDA_R_64C

See cusolverStatus_t for the description of the return status.

`cusolverMpGetrf_bufferSize`¶

cusolverStatus_t cusolverMpGetrf_bufferSize(
        cusolverMpHandle_t handle,
        int64_t M,
        int64_t N,
        void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descrA,
        int64_t *d_ipiv,
        cudaDataType_t computeType,
        size_t *workspaceInBytesOnDevice,
        size_t *workspaceInBytesOnHost)

Computes the size in bytes of the host and device working buffers required by cusolverMpGetrf().

The user can set d_ipiv=NULL so cusolverMpGetrf() will compute the LU factorization of the input matrix A without pivoting.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
M	Host	In	Number of rows of sub(A).
N	Host	In	Number of columns of sub(A).
d_A	Device	In/Out	Pointer to the first entry of the local portion of the global matrix A. On output, the sub(A) is overwritten with the L and U factors.
IA	Host	In	Row index of the first row of the sub(A). This function does not make any assumptions on the alignment of `IA`.
JA	Host	In	Column index of the first column of the sub(A). This function does not make any assumptions on the alignment of `JA`.
descrA	Host	In	Matrix descriptor associated to the global matrix A
d_ipiv	Device	In/Out	On output, it contains the distributed integer array containing the pivot indices if `d_ipiv != NULL` on input.
computeType	Host	In	Data type used for computations. See table below for supported combinations.
workspaceInBytesOnDevice	Host	In/Out	On output, contains the size in bytes of the local device workspace needed by cusolverMpGetrf().
workspaceInBytesOnHost	Host	In/Out	On output, contains the size in bytes of the local host workspace needed by cusolverMpGetrf().

This routine supports the following combinations of data types:

Data Type of A	computeType	Output Data Type
CUDA_R_32F	CUDA_R_32F	CUDA_R_32F
CUDA_R_64F	CUDA_R_64F	CUDA_R_64F
CUDA_R_32C	CUDA_R_32C	CUDA_R_32C
CUDA_R_64C	CUDA_R_64C	CUDA_R_64C

See cusolverStatus_t for the description of the return status.

`cusolverMpGetrs`¶

cusolverStatus_t cusolverMpGetrs(
        cusolverMpHandle_t handle,
        cublasOperation_t trans,
        int64_t N,
        int64_t NRHS,
        const void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descrA,
        const int64_t *d_ipiv,
        void *d_B,
        int64_t IB,
        int64_t JB,
        cudaLibMpMatrixDesc_t descrB,
        cudaDataType_t computeType,
        void *d_work,
        size_t workspaceInBytesOnDevice,
        void *h_work,
        size_t workspaceInBytesOnHost,
        int *d_info)

This routine solves a system of distributed linear equations

$op(sub(A)) \cdot X = sub(B)$

with a general N-by-N distributed matrix sub(A) using the LU factorization computed by cusolverMpGetrf().

Where $op$ is defined by the argument trans, which allows to solve linear systems of the form:

trans	Form of the linear system
CUBLAS_OP_N	$sub(A) \cdot X = sub(B)$
CUBLAS_OP_T	$sub(A)^T \cdot X = sub(B)$
CUBLAS_OP_C	$sub(A)^H \cdot X = sub(B)$

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
trans	Host	In	Specifies the form of the linear system. Only `CUBLAS_OP_N` is currently supported.
N	Host	In	Number of rows of sub(A).
NRHS	Host	In	Number of colums of sub(B). Currently, this routine only supports `NRHS=1`.
d_A	Device	In	Pointer into the local memory to an array of dimension `(LLD_A, LOCc(JA+N-1))`. On entry, this array contains the local pieces of the M-by-N distributed L and U factors of sub(A) as computed by cusolverMpGetrf().
IA	Host	In	Row index of the first row of the sub(A). This function does not make any assumptions on the alignment of `IA`.
JA	Host	In	Column index of the first column of the sub(A). This function does not make any assumptions on the alignment of `JA`.
descrA	Host	In	Matrix descriptor associated to the global matrix A
d_ipiv	Device	In	Local array of dimension `(LOCr(M_A)+MB_A)` containing the pivoting information as computed by cusolverMpGetrf().
d_B	Device	In/Out	Pointer into the local memory to an array of dimension `(LLD_B,LOCc(JB+NRHS-1))`. On entry, the right hand sides sub(B). On exit, sub(B) is overwritten by the solution distributed matrix X.
IB	Host	In	Row index of the first row of the sub(B). This function does not make any assumptions on the alignment of `IB`.
JB	Host	In	Column index of the first column of the sub(B). This function does not make any assumptions on the alignment of `JB`.
descrB	Host	In	Matrix descriptor associated to the global matrix B.
computeType	Host	In	Data type used for computations. See table below for supported combinations.
d_work	Device	In/Out	Host workspace of size `workspaceInBytesOnHost`.
workspaceInBytesOnDevice	Host	In	The size in bytes of the local device workspace needed by the routine as provided by cusolverMpGetrs_bufferSize().
h_work	Host	In/Out	Host workspace of size `workspaceInBytesOnHost`.
workspaceInBytesOnHost	Host	In	The size in bytes of the local host workspace needed by the routine as provided by cusolverMpGetrs_bufferSize()
info	Device	Out	`info < 0` indicates an incorrect value of the i-th argument of the function. `info > 0` indicates the index of the leading minor in the case of a singular matrix.

This routine supports the following combinations of data types:

Data Type of A	computeType	Output Data Type
CUDA_R_32F	CUDA_R_32F	CUDA_R_32F
CUDA_R_64F	CUDA_R_64F	CUDA_R_64F
CUDA_R_32C	CUDA_R_32C	CUDA_R_32C
CUDA_R_64C	CUDA_R_64C	CUDA_R_64C

See cusolverStatus_t for the description of the return status.

`cusolverMpGetrs_bufferSize`¶

cusolverStatus_t cusolverMpGetrs_bufferSize(
        cusolverMpHandle_t handle,
        cublasOperation_t trans,
        int64_t N,
        int64_t NRHS,
        const void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descrA,
        const int64_t *d_ipiv,
        void *d_B,
        int64_t IB,
        int64_t JB,
        cudaLibMpMatrixDesc_t descrB,
        cudaDataType_t computeType,
        size_t *workspaceInBytesOnDevice,
        size_t *workspaceInBytesOnHost)

Computes the size in bytes of the host and device working buffers required by cusolverMpGetrs().

If pivoting was disabled during cusolverMpGetrf(), the user must set d_ipiv=NULL.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
trans	Host	In	Specifies the form of the linear system. Only `CUBLAS_OP_N` is currently supported.
N	Host	In	Number of rows of sub(A).
NRHS	Host	In	Number of colums of sub(B). Currently, this routine only supports `NRHS=1`.
d_A	Device	In	Pointer into the local memory to an array of dimension `(LLD_A, LOCc(JA+N-1))`. On entry, this array contains the local pieces of the M-by-N distributed L and U factors of sub(A) as computed by cusolverMpGetrf().
IA	Host	In	Row index of the first row of the sub(A). This function does not make any assumptions on the alignment of `IA`.
JA	Host	In	Column index of the first column of the sub(A). This function does not make any assumptions on the alignment of `JA`.
descrA	Host	In	Matrix descriptor associated to the global matrix A
d_ipiv	Device	In	Local array of dimension `(LOCr(M_A)+MB_A)` containing the pivoting information as computed by cusolverMpGetrf().
d_B	Device	In/Out	Pointer to the first entry of the local portion of the global matrix B. On output, B is overwritten the solution of the linear system.
IB	Host	In	Row index of the first row of the sub(B). This function does not make any assumptions on the alignment of `IB`.
JB	Host	In	Column index of the first column of the sub(B). This function does not make any assumptions on the alignment of `JB`.
descrB	Host	In	Matrix descriptor associated to the global matrix B.
computeType	Host	In	Data type used for computations. See table below for supported combinations.
workspaceInBytesOnDevice	Host	In/Out	On output, contains the size in bytes of the local device workspace needed by cusolverMpGetrs().
workspaceInBytesOnHost	Host	In/Out	On output, contains the size in bytes of the local host workspace needed by cusolverMpGetrs().

This routine supports the following combinations of data types:

Data Type of A	computeType	Output Data Type
CUDA_R_32F	CUDA_R_32F	CUDA_R_32F
CUDA_R_64F	CUDA_R_64F	CUDA_R_64F
CUDA_R_32C	CUDA_R_32C	CUDA_R_32C
CUDA_R_64C	CUDA_R_64C	CUDA_R_64C

See cusolverStatus_t for the description of the return status.

`cusolverMpPotrf`¶

cusolverStatus_t cusolverMpPotrf(
        cusolverMpHandle_t handle,
        cublasFillMode_t uplo,
        int64_t N,
        void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descA,
        cudaDataType_t computeType,
        void *d_work,
        size_t workspaceInBytesOnDevice,
        void *h_work,
        size_t workspaceInBytesOnHost,
        int *info)

Computes the Cholesky factorization of an N-by-N real symmetric or a complex hermitian positive definite distributed matrix sub(A) denoting A(IA:IA+N-1, JA:JA+N-1).

If A is upper triangular and uplo=CUBLAS_FILL_MODE_UPPER, the factorization has the form

$sub(A) = U^H \cdot U$

where U is upper triangular.

If the matrix is lower triangular and uplo is set to CUBLAS_FILL_MODE_LOWER, the factorization has the form

$sub(A) = L \cdot L^H$

where L is lower triangular.

The user can combine cusolverMpPotrf() and cusolverMpPotrs() to solve a system of linear equations.

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
uplo	Host	In	Specifies if A is upper (`CUBLAS_FILL_MODE_UPPER`) or lower triangular matrix (`CUBLAS_FILL_MODE_LOWER`).
N	Host	In	Number of rows and columns of sub(A).
d_A	Device	In	Pointer into the local memory to an array of dimension `(LLD_A, LOCc(JA+N-1))`. On entry, this array contains the local pieces of the N-by-N distributed matrix sub(A). On output, this array contains the L or U factors of A, depending on the value of `uplo`.
IA	Host	In	Row index of the first row of the sub(A). `IA` must be a multiple of the row blocking dimension `MB_A`.
JA	Host	In	Column index of the first column of the sub(A).`JA` must be a multiple of the column blocking dimension `NB_A`.
descrA	Host	In	Matrix descriptor associated to the global matrix A.
computeType	Host	In	Data type used for computations. See table below for supported combinations.
d_work	Device	In/Out	Device workspace of size `workspaceInBytesOnDevice`.
workspaceInBytesOnDevice	Host	In	The size in bytes of the local device workspace needed by the routine as provided by cusolverMpPotrf_bufferSize().
h_work	Host	In/Out	Host workspace of size `workspaceInBytesOnHost`.
workspaceInBytesOnHost	Host	In	The size in bytes of the local host workspace needed by the routine as provided by cusolverMpPotrf_bufferSize()
info	Device	Out	`info < 0` indicates an incorrect value of the i-th argument of the function. `info > 0` indicates the index of the leading minor in the case of a singular matrix.

This function requires square block size (MB_A == NB_A).

This routine supports the following combinations of data types:

Data Type of A	computeType	Output Data Type
CUDA_R_32F	CUDA_R_32F	CUDA_R_32F
CUDA_R_64F	CUDA_R_64F	CUDA_R_64F
CUDA_R_32C	CUDA_R_32C	CUDA_R_32C
CUDA_R_64C	CUDA_R_64C	CUDA_R_64C

See cusolverStatus_t for the description of the return status.

`cusolverMpPotrf_bufferSize`¶

cusolverStatus_t cusolverMpPotrf_bufferSize(
        cusolverMpHandle_t handle,
        cublasFillMode_t uplo,
        int64_t N,
        const void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descA,
        cudaDataType_t computeType,
        size_t* workspaceInBytesOnDevice,
        size_t* workspaceInBytesOnHost)

Computes the size in bytes of the host and device working buffers required by cusolverMpPotrf().

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
uplo	Host	In	Specifies if A is upper (`CUBLAS_FILL_MODE_UPPER`) or lower triangular matrix (`CUBLAS_FILL_MODE_LOWER`).
N	Host	In	Number of rows and columns of sub(A).
d_A	Device	In	Pointer into the local memory to an array of dimension `(LLD_A, LOCc(JA+N-1))`. On entry, this array contains the local pieces of the N-by-N distributed matrix sub(A). On output, this array contains the L or U factors of A, depending on the value of `uplo`.
IA	Host	In	Row index of the first row of the sub(A). This function does not make any assumptions on the alignment of `IA`.
JA	Host	In	Column index of the first column of the sub(A). This function does not make any assumptions on the alignment of `JA`.
descrA	Host	In	Matrix descriptor associated to the global matrix A
computeType	Host	In	Data type used for computations. See table below for supported combinations.
workspaceInBytesOnDevice	Host	In/Out	On output, contains the size in bytes of the local device workspace needed by cusolverMpPotrf().
workspaceInBytesOnHost	Host	In/Out	On output, contains the size in bytes of the local host workspace needed by cusolverMpPotrf().

This routine supports the following combinations of data types:

Data Type of A	computeType	Output Data Type
CUDA_R_32F	CUDA_R_32F	CUDA_R_32F
CUDA_R_64F	CUDA_R_64F	CUDA_R_64F
CUDA_R_32C	CUDA_R_32C	CUDA_R_32C
CUDA_R_64C	CUDA_R_64C	CUDA_R_64C

See cusolverStatus_t for the description of the return status.

`cusolverMpPotrs`¶

cusolverStatus_t cusolverMpPotrs(
        cusolverMpHandle_t handle,
        cublasFillMode_t uplo,
        int64_t N,
        int64_t NRHS,
        const void *d_A,
        int64_t IA,
        int64_t JA,
        cudaLibMpMatrixDesc_t descA,
        void *d_B,
        int64_t IB,
        int64_t JB,
        cudaLibMpMatrixDesc_t descB,
        cudaDataType_t computeType,
        void *d_work,
        size_t workspaceInBytesOnDevice,
        void *h_work,
        size_t workspaceInBytesOnHost,
        int *info)

Solves a system of linear equations

$sub(A) \cdot X = sub(B)$

where sub(A) denotes A(IA:IA+N-1,JA:JA+N-1) and is a N-by-N symmetric or hermitian positive definite distributed matrix using the Cholesky factorization:

$sub(A) = U^H \cdot U$

or

$sub(B) = L \cdot L^H$

computed by cusolverMpPotrf() and sub(B) denotes the distributed matrix B(IB:IB+N-1,JB:JB+NRHS-1).

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
uplo	Host	In	Specifies if A is upper (`CUBLAS_FILL_MODE_UPPER`) or lower triangular matrix (`CUBLAS_FILL_MODE_LOWER`).
N	Host	In	Number of rows and columns of sub(A).
NRHS	Host	In	Number of colums of sub(B). Currently, this routine only supports `NRHS=1`.
d_A	Device	In	Pointer into the local memory to an array of dimension `(LLD_A, LOCc(JA+N-1))`. Contains the local pieces of the N-by-N distributed L or U factors of sub(A) as computed by cusolverMpPotrf().
IA	Host	In	Row index of the first row of the sub(A). `IA` must be a multiple of the row blocking dimension `NB_A`.
JA	Host	In	Column index of the first column of the sub(A). `JA` must be a multiple of the column blocking dimension `MB_A`.
descrA	Host	In	Matrix descriptor associated to the global matrix A
d_B	Device	In/Out	Pointer into the local memory to an array of dimension `(LLD_B,LOCc(JB+NRHS-1))`. On entry, the right hand sides sub(B). On exit, sub(B) is overwritten by the solution distributed matrix X.
IB	Host	In	Row index of the first row of the sub(B). This function does not make any assumptions on the alignment of `IB`.
JB	Host	In	Column index of the first column of the sub(B). This function does not make any assumptions on the alignment of `JB`.
descrB	Host	In	Matrix descriptor associated to the global matrix B.
computeType	Host	In	Data type used for computations. See table below for supported combinations.
d_work	Device	In/Out	Device workspace of size `workspaceInBytesOnDevice`.
workspaceInBytesOnDevice	Host	In	The size in bytes of the local device workspace needed by the routine as provided by cusolverMpPotrs_bufferSize().
h_work	Host	In/Out	Host workspace of size `workspaceInBytesOnHost`.
workspaceInBytesOnHost	Host	In	The size in bytes of the local host workspace needed by the routine as provided by cusolverMpPotrs_bufferSize()
info	Device	Out	`info < 0` indicates an incorrect value of the i-th argument of the function. `info > 0` indicates the index of the leading minor in the case of a singular matrix.

This function requires square block size (MB_A == NB_A) and alignment of sub(A) and sub(B) matrices, meaning (MB_A == MB_B) and (IA == IB).

This routine supports the following combinations of data types:

Data Type of A	computeType	Output Data Type
CUDA_R_32F	CUDA_R_32F	CUDA_R_32F
CUDA_R_64F	CUDA_R_64F	CUDA_R_64F
CUDA_R_32C	CUDA_R_32C	CUDA_R_32C
CUDA_R_64C	CUDA_R_64C	CUDA_R_64C

See cusolverStatus_t for the description of the return status.

—

`cusolverMpPotrs_bufferSize`¶

cusolverStatus_t cusolverMpPotrs_bufferSize(
        cusolverMpHandle_t handle,
        cublasFillMode_t uplo,
        int64_t n,
        int64_t nrhs,
        const void *a,
        int64_t ia,
        int64_t ja,
        cudaLibMpMatrixDesc_t descA,
        const void *b,
        int64_t ib,
        int64_t jb,
        cudaLibMpMatrixDesc_t descB,
        cudaDataType_t computeType,
        size_t* workspaceInBytesOnDevice,
        size_t* workspaceInBytesOnHost)

Computes the size in bytes of the host and device working buffers required by cusolverMpPotrs().

Parameter	Memory	In/Out	Description
handle	Host	In	cusolverMp library handle.
uplo	Host	In	Specifies if A is upper (`CUBLAS_FILL_MODE_UPPER`) or lower triangular matrix (`CUBLAS_FILL_MODE_LOWER`).
N	Host	In	Number of rows and columns of sub(A).
NRHS	Host	In	Number of colums of sub(B). Currently, this routine only supports `NRHS=1`.
d_A	Device	In	Pointer into the local memory to an array of dimension `(LLD_A, LOCc(JA+N-1))`. Contains the local pieces of the N-by-N distributed L or U factors of sub(A) as computed by cusolverMpPotrf().
IA	Host	In	Row index of the first row of the sub(A). `IA` must be a multiple of the row blocking dimension `NB_A`.
JA	Host	In	Column index of the first column of the sub(A). `JA` must be a multiple of the column blocking dimension `MB_A`.
descrA	Host	In	Matrix descriptor associated to the global matrix A
d_B	Device	In/Out	Pointer into the local memory to an array of dimension `(LLD_B,LOCc(JB+NRHS-1))`. On entry, the right hand sides sub(B). On exit, sub(B) is overwritten by the solution distributed matrix X.
IB	Host	In	Row index of the first row of the sub(B). This function does not make any assumptions on the alignment of `IB`.
JB	Host	In	Column index of the first column of the sub(B). This function does not make any assumptions on the alignment of `JB`.
descrB	Host	In	Matrix descriptor associated to the global matrix B.
computeType	Host	In	Data type used for computations. See table below for supported combinations.
workspaceInBytesOnDevice	Host	In/Out	On output, contains the size in bytes of the local device workspace needed by cusolverMpPotrs().
workspaceInBytesOnHost	Host	In/Out	On output, contains the size in bytes of the local host workspace needed by cusolverMpPotrs().

This function requires square block size (MB_A == NB_A) and alignment of sub(A) and sub(B) matrices, meaning (MB_A == MB_B) and (IA == IB).

This routine supports the following combinations of data types:

Data Type of A	computeType	Output Data Type
CUDA_R_32F	CUDA_R_32F	CUDA_R_32F
CUDA_R_64F	CUDA_R_64F	CUDA_R_64F
CUDA_R_32C	CUDA_R_32C	CUDA_R_32C
CUDA_R_64C	CUDA_R_64C	CUDA_R_64C

See cusolverStatus_t for the description of the return status.

cusolverMp C API¶

Library Management¶

cusolverMpCreate¶

cusolverMpDestroy¶

cusolverMpGetStream¶

cusolverMpGetVersion¶

Grid Management¶

cusolverMpCreateDeviceGrid¶

cusolverMpDestroyGrid¶

Matrix Management¶

cusolverMpCreateMatrixDesc¶

cusolverMpDestroyMatrixDesc¶

Utility¶

cusolverMpNUMROC¶

cusolverMpMatrixGatherD2H¶

cusolverMpMatrixScatterH2D¶

Logging¶

cusolverMpLoggerSetCallback¶

cusolverMpLoggerSetFile¶

cusolverMpLoggerOpenFile¶

cusolverMpLoggerSetLevel¶

cusolverMpLoggerSetMask¶

cusolverMpLoggerForceDisable¶

Factorization¶

cusolverMpGetrf¶

cusolverMpGetrf_bufferSize¶

cusolverMpGetrs¶

cusolverMpGetrs_bufferSize¶

cusolverMpPotrf¶

cusolverMpPotrf_bufferSize¶

cusolverMpPotrs¶

cusolverMpPotrs_bufferSize¶

`cusolverMpCreate`¶

`cusolverMpDestroy`¶

`cusolverMpGetStream`¶

`cusolverMpGetVersion`¶

`cusolverMpCreateDeviceGrid`¶

`cusolverMpDestroyGrid`¶

`cusolverMpCreateMatrixDesc`¶

`cusolverMpDestroyMatrixDesc`¶

`cusolverMpNUMROC`¶

`cusolverMpMatrixGatherD2H`¶

`cusolverMpMatrixScatterH2D`¶

`cusolverMpLoggerSetCallback`¶

`cusolverMpLoggerSetFile`¶

`cusolverMpLoggerOpenFile`¶

`cusolverMpLoggerSetLevel`¶

`cusolverMpLoggerSetMask`¶

`cusolverMpLoggerForceDisable`¶

`cusolverMpGetrf`¶

`cusolverMpGetrf_bufferSize`¶

`cusolverMpGetrs`¶

`cusolverMpGetrs_bufferSize`¶

`cusolverMpPotrf`¶

`cusolverMpPotrf_bufferSize`¶

`cusolverMpPotrs`¶

`cusolverMpPotrs_bufferSize`¶