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/*! \file */
/* ************************************************************************
* Copyright (C) 2023-2024 Advanced Micro Devices, Inc. All rights Reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the Software), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED AS IS, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*
* ************************************************************************ */
#ifndef ROCSPARSE_GEMMI_H
#define ROCSPARSE_GEMMI_H
#include "../../rocsparse-types.h"
#include "rocsparse/rocsparse-export.h"
#ifdef __cplusplus
extern "C" {
#endif
/*! \ingroup level3_module
* \brief Dense matrix sparse matrix multiplication using CSR storage format
*
* \details
* \p rocsparse_gemmi multiplies the scalar \f$\alpha\f$ with a column-oriented dense \f$m \times k\f$
* matrix \f$op(A)\f$ and the sparse \f$k \times n\f$ matrix \f$op(B)\f$, defined in CSR
* storage format and adds the result to the column-oriented dense \f$m \times n\f$ matrix \f$C\f$ that
* is multiplied by the scalar \f$\beta\f$, such that
* \f[
* C := \alpha \cdot op(A) \cdot op(B) + \beta \cdot C
* \f]
* with
* \f[
* op(A) = \left\{
* \begin{array}{ll}
* A, & \text{if trans_A == rocsparse_operation_none} \\
* A^T, & \text{if trans_A == rocsparse_operation_transpose} \\
* A^H, & \text{if trans_A == rocsparse_operation_conjugate_transpose}
* \end{array}
* \right.
* \f]
* and
* \f[
* op(B) = \left\{
* \begin{array}{ll}
* B, & \text{if trans_B == rocsparse_operation_none} \\
* B^T, & \text{if trans_B == rocsparse_operation_transpose} \\
* B^H, & \text{if trans_B == rocsparse_operation_conjugate_transpose}
* \end{array}
* \right.
* \f]
*
* \note
* Currently, only \p trans_A == \ref rocsparse_operation_none is supported.
*
* \note
* Currently, only \p trans_B == \ref rocsparse_operation_transpose is supported.
*
* \note
* This function is non blocking and executed asynchronously with respect to the host.
* It may return before the actual computation has finished.
*
* \note
* This routine supports execution in a hipGraph context.
*
* @param[in]
* handle handle to the rocsparse library context queue.
* @param[in]
* trans_A matrix \f$A\f$ operation type.
* @param[in]
* trans_B matrix \f$B\f$ operation type.
* @param[in]
* m number of rows of the column-oriented dense matrix \f$A\f$.
* @param[in]
* n number of columns of the sparse CSR matrix \f$op(B)\f$ and \f$C\f$.
* @param[in]
* k number of columns of the column-oriented dense matrix \f$A\f$.
* @param[in]
* nnz number of non-zero entries of the sparse CSR matrix \f$B\f$.
* @param[in]
* alpha scalar \f$\alpha\f$.
* @param[in]
* A array of dimension \f$lda \times k\f$ (\f$op(A) == A\f$) or
* \f$lda \times m\f$ (\f$op(A) == A^T\f$ or \f$op(A) == A^H\f$).
* @param[in]
* lda leading dimension of \f$A\f$, must be at least \f$m\f$
* (\f$op(A) == A\f$) or \f$k\f$ (\f$op(A) == A^T\f$ or
* \f$op(A) == A^H\f$).
* @param[in]
* descr descriptor of the sparse CSR matrix \f$B\f$. Currently, only
* \ref rocsparse_matrix_type_general is supported.
* @param[in]
* csr_val array of \p nnz elements of the sparse CSR matrix \f$B\f$.
* @param[in]
* csr_row_ptr array of \p m+1 elements that point to the start of every row of the
* sparse CSR matrix \f$B\f$.
* @param[in]
* csr_col_ind array of \p nnz elements containing the column indices of the sparse CSR
* matrix \f$B\f$.
* @param[in]
* beta scalar \f$\beta\f$.
* @param[inout]
* C column-oriented dense matrix of dimension \f$ldc \times n\f$ that holds the values of \f$C\f$.
* @param[in]
* ldc leading dimension of \f$C\f$, must be at least \f$m\f$.
*
* \retval rocsparse_status_success the operation completed successfully.
* \retval rocsparse_status_invalid_handle the library context was not initialized.
* \retval rocsparse_status_invalid_size \p m, \p n, \p k, \p nnz, \p lda or \p ldc
* is invalid.
* \retval rocsparse_status_invalid_pointer \p alpha, \p A, \p csr_val,
* \p csr_row_ptr, \p csr_col_ind, \p beta or \p C pointer is invalid.
*
* \par Example
* This example multiplies a column-oriented dense matrix with a CSC matrix.
* \code{.c}
* rocsparse_int m = 2;
* rocsparse_int n = 5;
* rocsparse_int k = 3;
* rocsparse_int nnz = 8;
* rocsparse_int lda = m;
* rocsparse_int ldc = m;
*
* // Matrix A (m x k)
* // ( 9.0 10.0 11.0 )
* // ( 12.0 13.0 14.0 )
*
* // Matrix B (k x n)
* // ( 1.0 2.0 0.0 3.0 0.0 )
* // ( 0.0 4.0 5.0 0.0 0.0 )
* // ( 6.0 0.0 0.0 7.0 8.0 )
*
* // Matrix C (m x n)
* // ( 15.0 16.0 17.0 18.0 19.0 )
* // ( 20.0 21.0 22.0 23.0 24.0 )
*
* A[lda * k] = {9.0, 12.0, 10.0, 13.0, 11.0, 14.0}; // device memory
* csc_col_ptr_B[n + 1] = {0, 2, 4, 5, 7, 8}; // device memory
* csc_row_ind_B[nnz] = {0, 0, 1, 1, 2, 3, 3, 4}; // device memory
* csc_val_B[nnz] = {1.0, 6.0, 2.0, 4.0, 5.0, 3.0, 7.0, 8.0}; // device memory
* C[ldc * n] = {15.0, 20.0, 16.0, 21.0, 17.0, 22.0, // device memory
* 18.0, 23.0, 19.0, 24.0};
*
* // alpha and beta
* float alpha = 1.0f;
* float beta = 0.0f;
*
* // Perform the matrix multiplication
* rocsparse_sgemmi(handle,
* rocsparse_operation_none,
* rocsparse_operation_transpose,
* m,
* n,
* k,
* nnz,
* &alpha,
* A,
* lda,
* descr_B,
* csc_val_B,
* csc_col_ptr_B,
* csc_row_ind_B,
* &beta,
* C,
* ldc);
* \endcode
*/
/**@{*/
ROCSPARSE_EXPORT
rocsparse_status rocsparse_sgemmi(rocsparse_handle handle,
rocsparse_operation trans_A,
rocsparse_operation trans_B,
rocsparse_int m,
rocsparse_int n,
rocsparse_int k,
rocsparse_int nnz,
const float* alpha,
const float* A,
rocsparse_int lda,
const rocsparse_mat_descr descr,
const float* csr_val,
const rocsparse_int* csr_row_ptr,
const rocsparse_int* csr_col_ind,
const float* beta,
float* C,
rocsparse_int ldc);
ROCSPARSE_EXPORT
rocsparse_status rocsparse_dgemmi(rocsparse_handle handle,
rocsparse_operation trans_A,
rocsparse_operation trans_B,
rocsparse_int m,
rocsparse_int n,
rocsparse_int k,
rocsparse_int nnz,
const double* alpha,
const double* A,
rocsparse_int lda,
const rocsparse_mat_descr descr,
const double* csr_val,
const rocsparse_int* csr_row_ptr,
const rocsparse_int* csr_col_ind,
const double* beta,
double* C,
rocsparse_int ldc);
ROCSPARSE_EXPORT
rocsparse_status rocsparse_cgemmi(rocsparse_handle handle,
rocsparse_operation trans_A,
rocsparse_operation trans_B,
rocsparse_int m,
rocsparse_int n,
rocsparse_int k,
rocsparse_int nnz,
const rocsparse_float_complex* alpha,
const rocsparse_float_complex* A,
rocsparse_int lda,
const rocsparse_mat_descr descr,
const rocsparse_float_complex* csr_val,
const rocsparse_int* csr_row_ptr,
const rocsparse_int* csr_col_ind,
const rocsparse_float_complex* beta,
rocsparse_float_complex* C,
rocsparse_int ldc);
ROCSPARSE_EXPORT
rocsparse_status rocsparse_zgemmi(rocsparse_handle handle,
rocsparse_operation trans_A,
rocsparse_operation trans_B,
rocsparse_int m,
rocsparse_int n,
rocsparse_int k,
rocsparse_int nnz,
const rocsparse_double_complex* alpha,
const rocsparse_double_complex* A,
rocsparse_int lda,
const rocsparse_mat_descr descr,
const rocsparse_double_complex* csr_val,
const rocsparse_int* csr_row_ptr,
const rocsparse_int* csr_col_ind,
const rocsparse_double_complex* beta,
rocsparse_double_complex* C,
rocsparse_int ldc);
/**@}*/
#ifdef __cplusplus
}
#endif
#endif /* ROCSPARSE_GEMMI_H */
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