Intel® oneAPI Math Kernel Library Developer Reference - C
Computes a sparse matrix- vector product.
sparse_status_t mkl_sparse_s_mv (const sparse_operation_t operation, const float alpha, const sparse_matrix_t A, const struct matrix_descr descr, const float *x, const float beta, float *y);
sparse_status_t mkl_sparse_d_mv (const sparse_operation_t operation, const double alpha, const sparse_matrix_t A, const struct matrix_descr descr, const double *x, const double beta, double *y);
sparse_status_t mkl_sparse_c_mv (const sparse_operation_t operation, const MKL_Complex8 alpha, const sparse_matrix_t A, const struct matrix_descr descr, const MKL_Complex8 *x, const MKL_Complex8 beta, MKL_Complex8 *y);
sparse_status_t mkl_sparse_z_mv (const sparse_operation_t operation, const MKL_Complex16 alpha, const sparse_matrix_t A, const struct matrix_descr descr, const MKL_Complex16 *x, const MKL_Complex16 beta, MKL_Complex16 *y);
The mkl_sparse_?_mv routine computes a sparse matrix-dense vector product defined as
y := alpha*op(A)*x + beta*y
where:
alpha and beta are scalars, x and y are vectors, and A is a sparse matrix handle of a matrix with m rows and k columns, and op is a matrix modifier for matrix A.
Specifies operation op() on input matrix.
SPARSE_OPERATION_NON_TRANSPOSE |
Non-transpose, op(A) = A. |
SPARSE_OPERATION_TRANSPOSE |
Transpose, op(A) = AT. |
SPARSE_OPERATION_CONJUGATE_TRANSPOSE |
Conjugate transpose, op(A) = AH. |
Specifies the scalar alpha.
Handle which contains the input matrix A.
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. |
Array of size equal to the number of columns, k of A if operation = SPARSE_OPERATION_NON_TRANSPOSE and at least the number of rows, m, of A otherwise. On entry, the array must contain the vector x.
Specifies the scalar beta.
Array with size at least m if operation=SPARSE_OPERATION_NON_TRANSPOSE and at least k otherwise. On entry, the array y must contain the vector y. Array of size equal to the number of rows, m of A if operation = SPARSE_OPERATION_NON_TRANSPOSE and at least the number of columns, k, of A otherwise. On entry, the array y must contain the vector y.
Overwritten by the updated vector y.
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. |