/*! \file */ /* ************************************************************************ * Copyright (C) 2025 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 HIPSPARSE_CSRMM_H #define HIPSPARSE_CSRMM_H #ifdef __cplusplus extern "C" { #endif #if(!defined(CUDART_VERSION) || CUDART_VERSION < 11000) /*! \ingroup level3_module * \brief Sparse matrix dense matrix multiplication using the CSR storage format. * * \details * \p hipsparseXcsrmm multiplies the scalar \f$\alpha\f$ with a sparse \f$m \times k\f$ * matrix \f$A\f$, defined in CSR storage format, and the column-oriented dense \f$k \times n\f$ * matrix \f$B\f$ 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 B + \beta \cdot C, * \f] * with * \f[ * op(A) = \left\{ * \begin{array}{ll} * A, & \text{if transA == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\ * A^T, & \text{if transA == HIPSPARSE_OPERATION_TRANSPOSE} \\ * A^H, & \text{if transA == HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE} * \end{array} * \right. * \f] * * \code{.c} * for(i = 0; i < ldc; ++i) * { * for(j = 0; j < n; ++j) * { * C[i][j] = beta * C[i][j]; * * for(k = csrRowPtr[i]; k < csrRowPtr[i + 1]; ++k) * { * C[i][j] += alpha * csrVal[k] * B[csrColInd[k]][j]; * } * } * } * \endcode * * \note * This function is non-blocking and executed asynchronously with respect to the host. * It can return before the actual computation has finished. * * \deprecated * This function is deprecated when using the CUDA backend (CUDA 10.0+) and will be * removed in CUDA 11.0. This deprecation does not apply to the ROCm backend. * * @param[in] * handle handle to the hipSPARSE library context queue. * @param[in] * transA matrix \f$A\f$ operation type. * @param[in] * m number of rows of the sparse CSR matrix \f$A\f$. Must be non-negative. * @param[in] * n number of columns of the dense matrix \f$op(B)\f$ and \f$C\f$. Must be non-negative. * @param[in] * k number of columns of the sparse CSR matrix \f$A\f$. Must be non-negative. * @param[in] * nnz number of non-zero entries of the sparse CSR matrix \f$A\f$. Must be non-negative. * @param[in] * alpha scalar \f$\alpha\f$. * @param[in] * descrA descriptor of the sparse CSR matrix \f$A\f$. Currently, only * \ref HIPSPARSE_MATRIX_TYPE_GENERAL is supported. * @param[in] * csrSortedValA array of \p nnz elements of the sparse CSR matrix \f$A\f$. * @param[in] * csrSortedRowPtrA array of \p m+1 elements that point to the start of every row of the * sparse CSR matrix \f$A\f$. * @param[in] * csrSortedColIndA array of \p nnz elements containing the column indices of the sparse * CSR matrix \f$A\f$. * @param[in] * B array of dimension \p ldb*n (\f$op(B) == B\f$), * \p ldb*k otherwise. * @param[in] * ldb leading dimension of \f$B\f$, must be at least \f$\max{(1, k)}\f$ * (\f$op(B) == B\f$), \f$\max{(1, n)}\f$ otherwise. * @param[in] * beta scalar \f$\beta\f$. * @param[inout] * C array of dimension \p ldc*n. * @param[in] * ldc leading dimension of \f$C\f$, must be at least \f$\max{(1, m)}\f$ * (\f$op(A) == A\f$), \f$\max{(1, k)}\f$ otherwise. * * \retval HIPSPARSE_STATUS_SUCCESS the operation completed successfully. * \retval HIPSPARSE_STATUS_NOT_INITIALIZED \p handle is not initialized. * \retval HIPSPARSE_STATUS_INVALID_VALUE \p handle, \p descrA, \p alpha, or \p beta is nullptr, * \p m, \p n, \p k, or \p nnz is negative, \p ldb or \p ldc is invalid, or * \p csrSortedValA, \p csrSortedRowPtrA, \p csrSortedColIndA, \p B, or \p C is nullptr. * \retval HIPSPARSE_STATUS_ARCH_MISMATCH the device is not supported. * \retval HIPSPARSE_STATUS_NOT_SUPPORTED \ref hipsparseMatrixType_t is not * \ref HIPSPARSE_MATRIX_TYPE_GENERAL. */ /**@{*/ DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11") HIPSPARSE_EXPORT hipsparseStatus_t hipsparseScsrmm(hipsparseHandle_t handle, hipsparseOperation_t transA, int m, int n, int k, int nnz, const float* alpha, const hipsparseMatDescr_t descrA, const float* csrSortedValA, const int* csrSortedRowPtrA, const int* csrSortedColIndA, const float* B, int ldb, const float* beta, float* C, int ldc); DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11") HIPSPARSE_EXPORT hipsparseStatus_t hipsparseDcsrmm(hipsparseHandle_t handle, hipsparseOperation_t transA, int m, int n, int k, int nnz, const double* alpha, const hipsparseMatDescr_t descrA, const double* csrSortedValA, const int* csrSortedRowPtrA, const int* csrSortedColIndA, const double* B, int ldb, const double* beta, double* C, int ldc); DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11") HIPSPARSE_EXPORT hipsparseStatus_t hipsparseCcsrmm(hipsparseHandle_t handle, hipsparseOperation_t transA, int m, int n, int k, int nnz, const hipComplex* alpha, const hipsparseMatDescr_t descrA, const hipComplex* csrSortedValA, const int* csrSortedRowPtrA, const int* csrSortedColIndA, const hipComplex* B, int ldb, const hipComplex* beta, hipComplex* C, int ldc); DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11") HIPSPARSE_EXPORT hipsparseStatus_t hipsparseZcsrmm(hipsparseHandle_t handle, hipsparseOperation_t transA, int m, int n, int k, int nnz, const hipDoubleComplex* alpha, const hipsparseMatDescr_t descrA, const hipDoubleComplex* csrSortedValA, const int* csrSortedRowPtrA, const int* csrSortedColIndA, const hipDoubleComplex* B, int ldb, const hipDoubleComplex* beta, hipDoubleComplex* C, int ldc); /**@}*/ #endif #if(!defined(CUDART_VERSION) || CUDART_VERSION < 11000) /*! \ingroup level3_module * \brief Sparse matrix dense matrix multiplication using the CSR storage format. * * \details * \p hipsparseXcsrmm2 multiplies the scalar \f$\alpha\f$ with a sparse \f$m \times k\f$ * matrix \f$A\f$, defined in CSR storage format, and the column-oriented dense \f$k \times n\f$ * matrix \f$B\f$ 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 transA == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\ * A^T, & \text{if transA == HIPSPARSE_OPERATION_TRANSPOSE} \\ * A^H, & \text{if transA == HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE} * \end{array} * \right. * \f] * and * \f[ * op(B) = \left\{ * \begin{array}{ll} * B, & \text{if transB == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\ * B^T, & \text{if transB == HIPSPARSE_OPERATION_TRANSPOSE} \\ * B^H, & \text{if transB == HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE} * \end{array} * \right. * \f] * * \code{.c} * for(i = 0; i < ldc; ++i) * { * for(j = 0; j < n; ++j) * { * C[i][j] = beta * C[i][j]; * * for(k = csrRowPtr[i]; k < csrRowPtr[i + 1]; ++k) * { * C[i][j] += alpha * csrVal[k] * B[csrColInd[k]][j]; * } * } * } * \endcode * * \note * This function is non-blocking and executed asynchronously with respect to the host. * It can return before the actual computation has finished. * * @param[in] * handle handle to the hipSPARSE library context queue. * @param[in] * transA matrix \f$A\f$ operation type. * @param[in] * transB matrix \f$B\f$ operation type. * @param[in] * m number of rows of the sparse CSR matrix \f$A\f$. * @param[in] * n number of columns of the dense matrix \f$op(B)\f$ and \f$C\f$. * @param[in] * k number of columns of the sparse CSR matrix \f$A\f$. * @param[in] * nnz number of non-zero entries of the sparse CSR matrix \f$A\f$. * @param[in] * alpha scalar \f$\alpha\f$. * @param[in] * descrA descriptor of the sparse CSR matrix \f$A\f$. Currently, only * \ref HIPSPARSE_MATRIX_TYPE_GENERAL is supported. * @param[in] * csrSortedValA array of \p nnz elements of the sparse CSR matrix \f$A\f$. * @param[in] * csrSortedRowPtrA array of \p m+1 elements that point to the start of every row of the * sparse CSR matrix \f$A\f$. * @param[in] * csrSortedColIndA array of \p nnz elements containing the column indices of the sparse * CSR matrix \f$A\f$. * @param[in] * B array of dimension \p ldb*n (\f$op(B) == B\f$), * \p ldb*k otherwise. * @param[in] * ldb leading dimension of \f$B\f$. Must be at least \f$\max{(1, k)}\f$ * (\f$op(B) == B\f$), \f$\max{(1, n)}\f$ otherwise. * @param[in] * beta scalar \f$\beta\f$. * @param[inout] * C array of dimension \p ldc*n. * @param[in] * ldc leading dimension of \f$C\f$. Must be at least \f$\max{(1, m)}\f$ * (\f$op(A) == A\f$), \f$\max{(1, k)}\f$ otherwise. * * \retval HIPSPARSE_STATUS_SUCCESS the operation completed successfully. * \retval HIPSPARSE_STATUS_INVALID_VALUE \p handle, \p m, \p n, \p k, \p nnz, \p ldb, \p ldc, * \p descrA, \p alpha, \p csrSortedValA, \p csrSortedRowPtrA, \p csrSortedColIndA, * \p B, \p beta, or \p C is invalid. * \retval HIPSPARSE_STATUS_ARCH_MISMATCH the device is not supported. * \retval HIPSPARSE_STATUS_NOT_SUPPORTED * \ref hipsparseMatrixType_t != \ref HIPSPARSE_MATRIX_TYPE_GENERAL. */ /**@{*/ DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11") HIPSPARSE_EXPORT hipsparseStatus_t hipsparseScsrmm2(hipsparseHandle_t handle, hipsparseOperation_t transA, hipsparseOperation_t transB, int m, int n, int k, int nnz, const float* alpha, const hipsparseMatDescr_t descrA, const float* csrSortedValA, const int* csrSortedRowPtrA, const int* csrSortedColIndA, const float* B, int ldb, const float* beta, float* C, int ldc); DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11") HIPSPARSE_EXPORT hipsparseStatus_t hipsparseDcsrmm2(hipsparseHandle_t handle, hipsparseOperation_t transA, hipsparseOperation_t transB, int m, int n, int k, int nnz, const double* alpha, const hipsparseMatDescr_t descrA, const double* csrSortedValA, const int* csrSortedRowPtrA, const int* csrSortedColIndA, const double* B, int ldb, const double* beta, double* C, int ldc); DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11") HIPSPARSE_EXPORT hipsparseStatus_t hipsparseCcsrmm2(hipsparseHandle_t handle, hipsparseOperation_t transA, hipsparseOperation_t transB, int m, int n, int k, int nnz, const hipComplex* alpha, const hipsparseMatDescr_t descrA, const hipComplex* csrSortedValA, const int* csrSortedRowPtrA, const int* csrSortedColIndA, const hipComplex* B, int ldb, const hipComplex* beta, hipComplex* C, int ldc); DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11") HIPSPARSE_EXPORT hipsparseStatus_t hipsparseZcsrmm2(hipsparseHandle_t handle, hipsparseOperation_t transA, hipsparseOperation_t transB, int m, int n, int k, int nnz, const hipDoubleComplex* alpha, const hipsparseMatDescr_t descrA, const hipDoubleComplex* csrSortedValA, const int* csrSortedRowPtrA, const int* csrSortedColIndA, const hipDoubleComplex* B, int ldb, const hipDoubleComplex* beta, hipDoubleComplex* C, int ldc); /**@}*/ #endif #ifdef __cplusplus } #endif #endif /* HIPSPARSE_CSRMM_H */