Developer Reference for Intel® oneAPI Math Kernel Library for C
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mkl_sparse_?_sorv
Computes forward, backward sweeps or a symmetric successive over-relaxation preconditioner operation.
Syntax
sparse_status_t mkl_sparse_s_sorv(
const sparse_sor_type_t type,
const struct matrix_descr descrA,
const sparse_matrix_t A,
float omega,
float alpha,
float* x,
float* b
);
sparse_status_t mkl_sparse_d_sorv(
const sparse_sor_type_t type,
const struct matrix_descr descrA,
const sparse_matrix_t A,
double omega,
double alpha,
double* x,
double* b
);
Include Files
- mkl_spblas.h
Description
The mkl_sparse_?_sorv routine performs one of the following operations:
SPARSE_SOR_FORWARD:
SPARSE_SOR_BACKWARD:
SPARSE_SOR_SYMMETRIC: Performs application of a
preconditioner.
where A = L + D + U and x^0 is an input vector x scaled by input parameter alpha vector and x^1 is an output stored in vector x.
Currently this routine only supports the following configuration:
- CSR format of the input matrix
- SPARSE_SOR_FORWARD operation
- General matrix (descr.type is SPARSE_MATRIX_TYPE_GENERAL) or symmetric matrix with full portrait and unit diagonal (descr.type is SPARSE_MATRIX_TYPE_SYMMETRIC, descr.mode is SPARSE_FILL_MODE_FULL, and descr.diag is SPARSE_DIAG_UNIT)
Currently, this routine is optimized only for sequential threading execution mode.
Product and Performance Information |
|---|
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex. Notice revision #20201201 |
Input Parameters
- type
-
Specifies the operation performed by the SORV preconditioner.
SPARSE_SOR_FORWARD
Performs forward sweep as defined by:
SPARSE_SOR_BACKWARD
Performs backward sweep as defined by:
SPARSE_SOR_SYMMETRIC
Preconditioner matrix could be expressed as:
- descr
-
Structure specifying sparse matrix properties.
sparse_matrix_type_t type
Specifies the type of a sparse matrix:
SPARSE_MATRIX_TYPE_GENERAL
The matrix is processed as-is.
SPARSE_MATRIX_TYPE_SYMMETRIC
The matrix is symmetric (only the requested triangle is processed).
SPARSE_MATRIX_TYPE_HERMITIAN
The matrix is Hermitian (only the requested triangle is processed).
SPARSE_MATRIX_TYPE_TRIANGULAR
The matrix is triangular (only the requested triangle is processed).
SPARSE_MATRIX_TYPE_DIAGONAL
The matrix is diagonal (only diagonal elements are processed).
SPARSE_MATRIX_TYPE_BLOCK_TRIANGULAR
The matrix is block-triangular (only requested triangle is processed). Applies to BSR format only.
SPARSE_MATRIX_TYPE_BLOCK_DIAGONAL
The matrix is block-diagonal (only diagonal blocks are processed). Applies to BSR format only.
sparse_fill_mode_t mode
Specifies the triangular matrix part for symmetric, Hermitian, triangular, and block-triangular matrices:
SPARSE_FILL_MODE_LOWER
The lower triangular matrix part is processed.
SPARSE_FILL_MODE_UPPER
The upper triangular matrix part is processed.
sparse_diag_type_t diag
Specifies diagonal type for non-general matrices:
SPARSE_DIAG_NON_UNIT
Diagonal elements might not be equal to one.
SPARSE_DIAG_UNIT
Diagonal elements are equal to one.
- A
-
Handle containing internal data.
- omega
-
Relaxation factor.
- alpha
-
Parameter that could be used to normalize or set to zero the vector x that holds the initial guess.
- x
-
Initial guess on input.
- b
-
Right-hand side.
Output Parameters
- x
-
Solution vector on output.
Return Values
The function returns a value indicating whether the operation was successful or not, and why.
SPARSE_STATUS_SUCCESS |
The operation was successful. |
SPARSE_STATUS_NOT_INITIALIZED |
The routine encountered an empty handle or matrix array. |
SPARSE_STATUS_ALLOC_FAILED |
Internal memory allocation failed. |
SPARSE_STATUS_INVALID_VALUE |
The input parameters contain an invalid value. |
SPARSE_STATUS_EXECUTION_FAILED |
Execution failed. |
SPARSE_STATUS_INTERNAL_ERROR |
An error in algorithm implementation occurred. |
SPARSE_STATUS_NOT_SUPPORTED |
The requested operation is not supported. |