mkl_sparse_?

Developer Reference for Intel® oneAPI Math Kernel Library for C

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ID 766684

Date 11/07/2023

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mkl_sparse_?_dotmv

Computes a sparse matrix-vector product followed by a dot product.

Syntax

sparse_status_t mkl_sparse_s_dotmv (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, float *d);

sparse_status_t mkl_sparse_d_dotmv (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, double *d);

sparse_status_t mkl_sparse_c_dotmv (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, MKL_Complex8 *d);

sparse_status_t mkl_sparse_z_dotmv (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, MKL_Complex16 *d);

Include Files

mkl_spblas.h

Description

The mkl_sparse_?_dotmv routine computes a sparse matrix-vector product and dot product:

y := alpha*op(A)*x + beta*y
d := ∑_ix_i*y_i (real case)
d := ∑_iconj(x_i)*y_i (complex case)

where

alpha and beta are scalars.
x and y are vectors.
A is an m-by-k matrix.
conj represents complex conjugation.
op(A) is a matrix modifier.

Available options for op(A) are A, A^T, or A^H.

NOTE:

For sparse matrices in the BSR format, the supported combinations of (indexing,block_layout) are:

(SPARSE_INDEX_BASE_ZERO, SPARSE_LAYOUT_ROW_MAJOR )
(SPARSE_INDEX_BASE_ONE, SPARSE_LAYOUT_COLUMN_MAJOR )

Input Parameters

operation

Specifies the operation performed on matrix A.

If operation = SPARSE_OPERATION_NON_TRANSPOSE, op(A) = A.

If operation = SPARSE_OPERATION_TRANSPOSE, op(A) = A^T.

If operation = SPARSE_OPERATION_CONJUGATE_TRANSPOSE, op(A) = A^H.

alpha

Specifies the scalar alpha.

A

Handle which contains the sparse matrix A.

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.

x

If operation = SPARSE_OPERATION_NON_TRANSPOSE, array of size at least k, where k is the number of columns of matrix A.

Otherwise, array of size at least m, where m is the number of rows of matrix A.

On entry, the array x must contain the vector x.

beta

Specifies the scalar beta.

y

If operation = SPARSE_OPERATION_NON_TRANSPOSE, array of size at least m, where k is the number of rows of matrix A.

Otherwise, array of size at least k, where k is the number of columns of matrix A.

On entry, the array y must contain the vector y.

Output Parameters

y: Overwritten by the updated vector y.
d: Overwritten by the dot product of x and y.

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.

Parent topic: Inspector-Executor Sparse BLAS Execution Routines

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