derek.holloway
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437 lines
22 KiB
C
437 lines
22 KiB
C
/*! \file */
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/* ************************************************************************
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* Copyright (C) 2025 Advanced Micro Devices, Inc. All rights Reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*
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* ************************************************************************ */
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#ifndef HIPSPARSE_SPSM_H
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#define HIPSPARSE_SPSM_H
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#ifdef __cplusplus
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extern "C" {
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#endif
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/*! \ingroup generic_module
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* \details
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* \p hipsparseSpSM_createDescr creates a sparse matrix triangular solve (with multiple rhs) descriptor. It should be
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* destroyed at the end using \ref hipsparseSpSM_destroyDescr().
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*/
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#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 11031)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpSM_createDescr(hipsparseSpSMDescr_t* descr);
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#endif
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/*! \ingroup generic_module
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* \details
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* \p hipsparseSpSM_destroyDescr destroys a sparse matrix triangular solve (with multiple rhs) descriptor and releases all
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* resources used by the descriptor.
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*/
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#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 11031)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpSM_destroyDescr(hipsparseSpSMDescr_t descr);
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#endif
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/*! \ingroup generic_module
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* \details
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* \p hipsparseSpSM_bufferSize computes the size of the required user-allocated buffer needed when solving the
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* triangular linear system:
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* \f[
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* op(A) \cdot C := \alpha \cdot op(B),
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* \f]
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* where \f$op(A)\f$ is a square sparse matrix in CSR or COO storage format, and \f$B\f$ and \f$C\f$ are dense matrices.
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*
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* \p hipsparseSpSM_bufferSize supports multiple combinations of data types and compute types. See \ref hipsparseSpSM_solve
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* for a complete listing of all the data type and compute type combinations available.
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*
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* @param[in]
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* handle handle to the hipSPARSE library context queue.
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* @param[in]
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* opA matrix operation type for the sparse matrix \f$A\f$.
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* @param[in]
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* opB matrix operation type for the dense matrix \f$B\f$.
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* @param[in]
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* alpha scalar \f$\alpha\f$.
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* @param[in]
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* matA sparse matrix descriptor.
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* @param[in]
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* matB dense matrix descriptor.
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* @param[inout]
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* matC dense matrix descriptor.
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* @param[in]
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* computeType floating point precision for the SpSM computation.
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* @param[in]
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* alg SpSM algorithm for the SpSM computation.
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* @param[in]
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* spsmDescr SpSM descriptor.
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* @param[out]
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* pBufferSizeInBytes number of bytes of the temporary storage buffer.
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*
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* \retval HIPSPARSE_STATUS_SUCCESS the operation completed successfully.
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* \retval HIPSPARSE_STATUS_NOT_INITIALIZED \p handle is not initialized.
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* \retval HIPSPARSE_STATUS_INVALID_VALUE \p handle, \p alpha, \p matA, \p matB, \p matC, \p spsmDescr, or
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* \p pBufferSizeInBytes is nullptr, or \p opA or \p opB is invalid.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \p opA, \p opB, \p computeType, or \p alg is currently not supported.
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*/
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#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 12000)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpSM_bufferSize(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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hipsparseConstSpMatDescr_t matA,
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hipsparseConstDnMatDescr_t matB,
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const hipsparseDnMatDescr_t matC,
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hipDataType computeType,
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hipsparseSpSMAlg_t alg,
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hipsparseSpSMDescr_t spsmDescr,
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size_t* pBufferSizeInBytes);
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#elif(CUDART_VERSION >= 11031)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpSM_bufferSize(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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const hipsparseSpMatDescr_t matA,
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const hipsparseDnMatDescr_t matB,
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const hipsparseDnMatDescr_t matC,
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hipDataType computeType,
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hipsparseSpSMAlg_t alg,
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hipsparseSpSMDescr_t spsmDescr,
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size_t* pBufferSizeInBytes);
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#endif
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/*! \ingroup generic_module
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* \details
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* \p hipsparseSpSM_analysis performs the required analysis needed when solving the
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* triangular linear system:
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* \f[
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* op(A) \cdot C := \alpha \cdot op(B),
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* \f]
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* where \f$A\f$ is a sparse matrix in CSR or COO storage format, and \f$B\f$ and \f$C\f$ are dense vectors.
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*
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* \p hipsparseSpSM_bufferSize supports multiple combinations of data types and compute types. See \ref hipsparseSpSM_solve
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* for a complete listing of all the data type and compute type combinations available.
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*
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* @param[in]
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* handle handle to the hipSPARSE library context queue.
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* @param[in]
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* opA matrix operation type for the sparse matrix \f$A\f$.
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* @param[in]
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* opB matrix operation type for the dense matrix \f$B\f$.
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* @param[in]
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* alpha scalar \f$\alpha\f$.
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* @param[in]
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* matA sparse matrix descriptor.
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* @param[in]
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* matB dense matrix descriptor.
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* @param[inout]
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* matC dense matrix descriptor.
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* @param[in]
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* computeType floating point precision for the SpSM computation.
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* @param[in]
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* alg SpSM algorithm for the SpSM computation.
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* @param[in]
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* spsmDescr SpSM descriptor.
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* @param[out]
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* externalBuffer temporary storage buffer allocated by the user.
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*
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* \retval HIPSPARSE_STATUS_SUCCESS the operation completed successfully.
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* \retval HIPSPARSE_STATUS_INVALID_VALUE \p handle, \p alpha, \p matA, \p matB, \p matC, \p spsmDescr or
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* \p externalBuffer pointer is invalid.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \p opA, \p opB, \p computeType, or \p alg is
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* currently not supported.
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*/
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#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 12000)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpSM_analysis(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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hipsparseConstSpMatDescr_t matA,
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hipsparseConstDnMatDescr_t matB,
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const hipsparseDnMatDescr_t matC,
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hipDataType computeType,
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hipsparseSpSMAlg_t alg,
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hipsparseSpSMDescr_t spsmDescr,
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void* externalBuffer);
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#elif(CUDART_VERSION >= 11031)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpSM_analysis(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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const hipsparseSpMatDescr_t matA,
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const hipsparseDnMatDescr_t matB,
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const hipsparseDnMatDescr_t matC,
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hipDataType computeType,
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hipsparseSpSMAlg_t alg,
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hipsparseSpSMDescr_t spsmDescr,
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void* externalBuffer);
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#endif
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/*! \ingroup generic_module
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* \brief Sparse triangular system solve
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*
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* \details
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* \p hipsparseSpSM_solve solves a triangular linear system of equations defined by a sparse \f$m \times m\f$ square matrix \f$op(A)\f$,
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* given in CSR or COO storage format, such that
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* \f[
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* op(A) \cdot C = \alpha \cdot op(B),
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* \f]
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* with
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* \f[
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* op(A) = \left\{
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* \begin{array}{ll}
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* A, & \text{if opA == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* A^T, & \text{if opB == HIPSPARSE_OPERATION_TRANSPOSE}
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* \end{array}
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* \right.
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* \f]
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* and
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* \f[
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* op(B) = \left\{
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* \begin{array}{ll}
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* B, & \text{if opA == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* B^T, & \text{if opB == HIPSPARSE_OPERATION_TRANSPOSE}
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* \end{array}
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* \right.
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* \f]
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* and where \f$C\f$ is the dense solution matrix and \f$B\f$ is the dense right-hand side matrix. Both \f$B\f$
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* and \f$C\f$ can be in row or column order.
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*
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* Performing the above operation requires three steps. First, the user calls \ref hipsparseSpSM_bufferSize to
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* determine the size of the required temporary storage buffer. The user then allocates this buffer and calls
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* \ref hipsparseSpSM_analysis, which will perform analysis on the sparse matrix \f$op(A)\f$. Finally, the user completes
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* the computation by calling \p hipsparseSpSM_solve. The buffer size and analysis routines only need to be called once
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* for a given sparse matrix \f$op(A)\f$, while the computation can be called repeatedly with different \f$B\f$ and \f$C\f$
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* matrices. After all calls to \p hipsparseSpSM_solve are complete, the temporary buffer can be deallocated.
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*
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* As noted above, both \f$B\f$ and \f$C\f$ can be in row or column order (this includes mixing the order so that \f$B\f$ is
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* row order and \f$C\f$ is column order and vice versa). When running on an AMD system with the rocSPARSE backend, the kernels
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* solve the system assuming the matrices \f$B\f$ and \f$C\f$ are in row order, because this provides the best memory access. This
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* means that if the matrix \f$C\f$ is not in row order and/or the matrix \f$B\f$ is not row order (or \f$B^{T}\f$ is not column
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* order as this is equivalent to being in row order), then internal memory copies and/or transposing of data might have to be performed
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* to get them into the correct order (possibly using extra buffer size). After computation is completed, additional memory copies
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* and/or transposing of data might have to be performed to get them back into the user arrays. For the best performance and smallest required
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* temporary storage buffers on an AMD system, use row order for the matrix \f$C\f$ and row order for the matrix \f$B\f$ (or column
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* order if \f$B\f$ is being transposed).
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*
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* \p hipsparseSpSM_solve supports \ref HIPSPARSE_INDEX_32I and \ref HIPSPARSE_INDEX_64I index precisions for storing the
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* row pointer and column indices arrays of the sparse matrices. \p hipsparseSpSM_solve supports the following data types for
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* \f$op(A)\f$, \f$op(B)\f$, \f$C\f$ and compute types for \f$\alpha\f$:
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*
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* \par Uniform Precisions:
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* <table>
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* <caption id="spsm_uniform">Uniform Precisions</caption>
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* <tr><th>A / B / C / compute_type
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* <tr><td>HIP_R_32F
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* <tr><td>HIP_R_64F
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* <tr><td>HIP_C_32F
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* <tr><td>HIP_C_64F
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* </table>
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*
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* \note This routine does not correctly match the parameter list of the corresponding cuSPARSE API, \p cusparseSpSM_solve,
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* which does not take the external buffer parameter. hipSPARSE provides the alternate routine \ref hipsparseSpSM_solve_ex, which
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* correctly matches the parameter list of \p cusparseSpSM_solve.
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*
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* @param[in]
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* handle handle to the hipSPARSE library context queue.
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* @param[in]
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* opA matrix operation type for the sparse matrix \f$A\f$.
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* @param[in]
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* opB matrix operation type for the dense matrix \f$B\f$.
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* @param[in]
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* alpha scalar \f$\alpha\f$.
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* @param[in]
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* matA sparse matrix descriptor.
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* @param[in]
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* matB dense matrix descriptor.
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* @param[inout]
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* matC dense matrix descriptor.
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* @param[in]
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* computeType floating point precision for the SpSM computation.
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* @param[in]
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* alg SpSM algorithm for the SpSM computation.
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* @param[in]
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* spsmDescr SpSM descriptor.
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* @param[out]
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* externalBuffer temporary storage buffer allocated by the user.
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*
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* \retval HIPSPARSE_STATUS_SUCCESS the operation completed successfully.
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* \retval HIPSPARSE_STATUS_INVALID_VALUE \p handle, \p alpha, \p matA, \p matB, \p matC, \p spsmDescr, or
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* \p externalBuffer pointer is invalid.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \p opA, \p opB, \p computeType, or \p alg is
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* currently not supported.
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*/
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#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 12000)
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HIPSPARSE_DEPRECATED_MSG(
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"This routine will be removed in a future release. Use hipsparseSpSM_solve_ex going forward")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpSM_solve(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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hipsparseConstSpMatDescr_t matA,
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hipsparseConstDnMatDescr_t matB,
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const hipsparseDnMatDescr_t matC,
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hipDataType computeType,
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hipsparseSpSMAlg_t alg,
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hipsparseSpSMDescr_t spsmDescr,
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void* externalBuffer);
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#elif(CUDART_VERSION >= 11031)
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HIPSPARSE_DEPRECATED_MSG(
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"This routine will be removed in a future release. Use hipsparseSpSM_solve_ex going forward")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpSM_solve(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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const hipsparseSpMatDescr_t matA,
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const hipsparseDnMatDescr_t matB,
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const hipsparseDnMatDescr_t matC,
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hipDataType computeType,
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hipsparseSpSMAlg_t alg,
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hipsparseSpSMDescr_t spsmDescr,
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void* externalBuffer);
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#endif
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/*! \ingroup generic_module
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* \brief Sparse triangular system solve
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*
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* \details
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* \p hipsparseSpSM_solve_ex solves a triangular linear system of equations defined by a sparse \f$m \times m\f$ square matrix \f$op(A)\f$,
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* given in CSR or COO storage format, such that
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* \f[
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* op(A) \cdot C = \alpha \cdot op(B),
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* \f]
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* with
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* \f[
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* op(A) = \left\{
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* \begin{array}{ll}
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* A, & \text{if opA == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* A^T, & \text{if opB == HIPSPARSE_OPERATION_TRANSPOSE}
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* \end{array}
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* \right.
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* \f]
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* and
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* \f[
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* op(B) = \left\{
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* \begin{array}{ll}
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* B, & \text{if opA == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* B^T, & \text{if opB == HIPSPARSE_OPERATION_TRANSPOSE}
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* \end{array}
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* \right.
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* \f]
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* and where \f$C\f$ is the dense solution matrix and \f$B\f$ is the dense right-hand side matrix. Both \f$B\f$
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* and \f$C\f$ can be in row or column order.
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*
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* Performing the above operation requires three steps. First, the user calls \ref hipsparseSpSM_bufferSize to
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* determine the size of the required temporary storage buffer. The user then allocates this buffer and calls
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* \ref hipsparseSpSM_analysis, which will perform analysis on the sparse matrix \f$op(A)\f$. Finally, the user completes
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* the computation by calling \p hipsparseSpSM_solve_ex. The buffer size and analysis routines only need to be called once
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* for a given sparse matrix \f$op(A)\f$, while the computation can be called repeatedly with different \f$B\f$ and \f$C\f$
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* matrices. After all calls to \p hipsparseSpSM_solve_ex are complete, the temporary buffer can be deallocated.
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*
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* As noted above, both \f$B\f$ and \f$C\f$ can be in row or column order (this includes mixing the order so that \f$B\f$ is
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* row order and \f$C\f$ is column order and vice versa). When running on an AMD system with the rocSPARSE backend, the kernels
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* solve the system assuming the matrices \f$B\f$ and \f$C\f$ are in row order because this provides the best memory access. This
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* means that if the matrix \f$C\f$ is not in row order and/or the matrix \f$B\f$ is not row order (or \f$B^{T}\f$ is not column
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* order as this is equivalent to being in row order), then internal memory copies and/or transposing of data might have to be performed
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* to get them into the correct order (possibly using extra buffer size). After computation is completed, additional memory copies
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* and/or transposing of data might have to be performed to get them back into the user arrays. For the best performance and smallest required
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* temporary storage buffers on an AMD system, use row order for the matrix \f$C\f$ and row order for the matrix \f$B\f$ (or column
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* order if \f$B\f$ is being transposed).
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*
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* \p hipsparseSpSM_solve_ex supports \ref HIPSPARSE_INDEX_32I and \ref HIPSPARSE_INDEX_64I index precisions for storing the
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* row pointer and column indices arrays of the sparse matrices. \p hipsparseSpSM_solve_ex supports the following data types for
|
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* \f$op(A)\f$, \f$op(B)\f$, \f$C\f$ and compute types for \f$\alpha\f$:
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*
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* \par Uniform Precisions:
|
|
* <table>
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|
* <caption id="spsm_ex_uniform">Uniform Precisions</caption>
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* <tr><th>A / B / C / compute_type
|
|
* <tr><td>HIP_R_32F
|
|
* <tr><td>HIP_R_64F
|
|
* <tr><td>HIP_C_32F
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|
* <tr><td>HIP_C_64F
|
|
* </table>
|
|
*
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|
* \note This routine correctly matches the parameter list of the corresponding cuSPARSE API \p cusparseSpSM_solve. The routine
|
|
* \ref hipsparseSpSM_solve incorrectly passed the external buffer as the last parameter. Users looking for an exact match in the
|
|
* parameters should use \ref hipsparseSpSM_solve_ex instead of \ref hipsparseSpSM_solve.
|
|
*
|
|
* @param[in]
|
|
* handle handle to the hipSPARSE library context queue.
|
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* @param[in]
|
|
* opA matrix operation type for the sparse matrix \f$A\f$.
|
|
* @param[in]
|
|
* opB matrix operation type for the dense matrix \f$B\f$.
|
|
* @param[in]
|
|
* alpha scalar \f$\alpha\f$.
|
|
* @param[in]
|
|
* matA sparse matrix descriptor.
|
|
* @param[in]
|
|
* matB dense matrix descriptor.
|
|
* @param[inout]
|
|
* matC dense matrix descriptor.
|
|
* @param[in]
|
|
* computeType floating point precision for the SpSM computation.
|
|
* @param[in]
|
|
* alg SpSM algorithm for the SpSM computation.
|
|
* @param[in]
|
|
* spsmDescr SpSM descriptor.
|
|
*
|
|
* \retval HIPSPARSE_STATUS_SUCCESS the operation completed successfully.
|
|
* \retval HIPSPARSE_STATUS_INVALID_VALUE \p handle, \p alpha, \p matA, \p matB, \p matC, or \p spsmDescr is invalid.
|
|
* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \p opA, \p opB, \p computeType, or \p alg is
|
|
* currently not supported.
|
|
*/
|
|
#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 12000)
|
|
HIPSPARSE_EXPORT
|
|
hipsparseStatus_t hipsparseSpSM_solve_ex(hipsparseHandle_t handle,
|
|
hipsparseOperation_t opA,
|
|
hipsparseOperation_t opB,
|
|
const void* alpha,
|
|
hipsparseConstSpMatDescr_t matA,
|
|
hipsparseConstDnMatDescr_t matB,
|
|
const hipsparseDnMatDescr_t matC,
|
|
hipDataType computeType,
|
|
hipsparseSpSMAlg_t alg,
|
|
hipsparseSpSMDescr_t spsmDescr);
|
|
#elif(CUDART_VERSION >= 11031)
|
|
HIPSPARSE_EXPORT
|
|
hipsparseStatus_t hipsparseSpSM_solve_ex(hipsparseHandle_t handle,
|
|
hipsparseOperation_t opA,
|
|
hipsparseOperation_t opB,
|
|
const void* alpha,
|
|
const hipsparseSpMatDescr_t matA,
|
|
const hipsparseDnMatDescr_t matB,
|
|
const hipsparseDnMatDescr_t matC,
|
|
hipDataType computeType,
|
|
hipsparseSpSMAlg_t alg,
|
|
hipsparseSpSMDescr_t spsmDescr);
|
|
#endif
|
|
|
|
#ifdef __cplusplus
|
|
}
|
|
#endif
|
|
|
|
#endif /* HIPSPARSE_SPSM_H */
|