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344 lines
16 KiB
C
344 lines
16 KiB
C
/*! \file */
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/* ************************************************************************
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* Copyright (C) 2025-2026 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_SPMV_H
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#define HIPSPARSE_SPMV_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 hipsparseSpMV_bufferSize computes the user-allocated buffer size required when computing the
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* sparse matrix multiplication with a dense vector:
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* \f[
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* y := \alpha \cdot op(A) \cdot x + \beta \cdot y,
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* \f]
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* where \f$op(A)\f$ is a sparse \f$m \times n\f$ matrix in CSR, CSC, COO, COO (AoS), BSR, or SELL format, \f$x\f$ is
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* a dense vector of length \f$n\f$, and \f$y\f$ is a dense vector of length \f$m\f$.
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*
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* \p hipsparseSpMV_bufferSize supports multiple combinations of data types and compute types. See \ref hipsparseSpMV for a complete
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* 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.
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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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* vecX dense vector descriptor.
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* @param[in]
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* beta scalar \f$\beta\f$.
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* @param[inout]
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* vecY dense vector descriptor.
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* @param[in]
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* computeType floating point precision for the SpMV computation.
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* @param[in]
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* alg SpMV algorithm for the SpMV computation.
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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 vecX, \p beta,
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* \p vecY, or \p pBufferSizeInBytes is nullptr, \p opA is invalid, or the matrix
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* or vector dimensions are incompatible.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \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 hipsparseSpMV_bufferSize(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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const void* alpha,
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hipsparseConstSpMatDescr_t matA,
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hipsparseConstDnVecDescr_t vecX,
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const void* beta,
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const hipsparseDnVecDescr_t vecY,
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hipDataType computeType,
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hipsparseSpMVAlg_t alg,
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size_t* pBufferSizeInBytes);
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#elif(CUDART_VERSION > 10010 || (CUDART_VERSION == 10010 && CUDART_10_1_UPDATE_VERSION == 1))
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpMV_bufferSize(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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const void* alpha,
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const hipsparseSpMatDescr_t matA,
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const hipsparseDnVecDescr_t vecX,
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const void* beta,
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const hipsparseDnVecDescr_t vecY,
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hipDataType computeType,
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hipsparseSpMVAlg_t alg,
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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 hipsparseSpMV_preprocess performs analysis on the sparse matrix \f$op(A)\f$ when computing the
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* sparse matrix multiplication with a dense vector:
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* \f[
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* y := \alpha \cdot op(A) \cdot x + \beta \cdot y,
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* \f]
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* where \f$op(A)\f$ is a sparse \f$m \times n\f$ matrix in CSR, CSC, COO, COO (AoS), BSR, or SELL format, \f$x\f$
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* is a dense vector of length \f$n\f$, and \f$y\f$ is a dense vector of length \f$m\f$. This step is
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* optional but it might result in better performance.
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*
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* \p hipsparseSpMV_preprocess supports multiple combinations of data types and compute types. See \ref hipsparseSpMV for
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* 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.
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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 matrix descriptor.
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* @param[in]
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* vecX vector descriptor.
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* @param[in]
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* beta scalar \f$\beta\f$.
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* @param[inout]
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* vecY vector descriptor.
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* @param[in]
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* computeType floating-point precision for the SpMV computation.
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* @param[in]
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* alg SpMV algorithm for the SpMV computation.
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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 x, \p beta, \p y, or
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* \p externalBuffer pointer is invalid or if \p opA, \p computeType, or \p alg is incorrect.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \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 hipsparseSpMV_preprocess(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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const void* alpha,
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hipsparseConstSpMatDescr_t matA,
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hipsparseConstDnVecDescr_t vecX,
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const void* beta,
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const hipsparseDnVecDescr_t vecY,
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hipDataType computeType,
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hipsparseSpMVAlg_t alg,
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void* externalBuffer);
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#elif(CUDART_VERSION > 10010 || (CUDART_VERSION == 10010 && CUDART_10_1_UPDATE_VERSION == 1))
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpMV_preprocess(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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const void* alpha,
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const hipsparseSpMatDescr_t matA,
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const hipsparseDnVecDescr_t vecX,
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const void* beta,
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const hipsparseDnVecDescr_t vecY,
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hipDataType computeType,
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hipsparseSpMVAlg_t alg,
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void* externalBuffer);
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#endif
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/*! \ingroup generic_module
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* \brief Compute the sparse matrix multiplication with a dense vector.
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*
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* \details
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* \p hipsparseSpMV multiplies the scalar \f$\alpha\f$ with a sparse \f$m \times n\f$ matrix \f$op(A)\f$, defined in CSR,
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* CSC, COO, COO (AoS), BSR, or SELL format, with the dense vector \f$x\f$ and adds the result to the dense vector \f$y\f$
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* that is multiplied by the scalar \f$\beta\f$, such that
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* \f[
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* y := \alpha \cdot op(A) \cdot x + \beta \cdot y,
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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 trans == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* A^T, & \text{if trans == HIPSPARSE_OPERATION_TRANSPOSE} \\
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* A^H, & \text{if trans == HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE}
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* \end{array}
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* \right.
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* \f]
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*
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* Performing the above operation involves multiple steps. First, the user calls \ref hipsparseSpMV_bufferSize to determine the
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* size of the required temporary storage buffer. The user then allocates this buffer and calls \ref hipsparseSpMV_preprocess.
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* Depending on the algorithm and sparse matrix format, this will perform analysis on the sparsity pattern of \f$op(A)\f$. Finally,
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* the user completes the operation by calling \p hipsparseSpMV. The buffer size and preprecess routines only need to be called
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* once for a given sparse matrix \f$op(A)\f$, while the computation can be repeatedly used with different \f$x\f$ and \f$y\f$
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* vectors. After all calls to \p hipsparseSpMV are complete, the temporary buffer can be deallocated.
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*
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* \p hipsparseSpMV supports multiple different algorithms. These algorithms have different trade offs depending on the sparsity
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* pattern of the matrix, whether or not the results need to be deterministic, and how many times the sparse-vector product will
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* be performed.
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*
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* <table>
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* <caption id="spmv_csr_algorithms">CSR/CSC Algorithms</caption>
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* <tr><th>CSR/CSC Algorithms
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* <tr><td>HIPSPARSE_SPMV_CSR_ALG1</td>
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* <tr><td>HIPSPARSE_SPMV_CSR_ALG2</td>
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* <tr><td>HIPSPARSE_SPMV_CSR_ALG3</td>
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* </table>
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*
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* <table>
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* <caption id="spmv_coo_algorithms">COO Algorithms</caption>
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* <tr><th>COO Algorithms
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* <tr><td>HIPSPARSE_SPMV_COO_ALG1</td>
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* <tr><td>HIPSPARSE_SPMV_COO_ALG2</td>
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* </table>
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*
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* <table>
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* <caption id="spmv_bsr_algorithms">BSR Algorithms</caption>
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* <tr><th>BSR Algorithms
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* <tr><td>HIPSPARSE_SPMV_BSR_ALG1</td>
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* </table>
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*
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* <table>
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* <caption id="spmv_sell_algorithms">SELL Algorithms</caption>
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* <tr><th>SELL Algorithms
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* <tr><td>HIPSPARSE_SPMV_SELL_ALG1</td>
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* </table>
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*
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* \p hipsparseSpMV supports multiple combinations of data types and compute types. The tables below indicate the currently
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* supported data types that can be used for the sparse matrix \f$op(A)\f$ and the dense vectors \f$x\f$ and \f$y\f$ and the
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* compute type for \f$\alpha\f$ and \f$\beta\f$. The advantage of using different data types is to save on memory bandwidth
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* and storage when a user application allows while performing the actual computation in a higher precision.
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*
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* \par Uniform Precisions:
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* <table>
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* <caption id="spmv_uniform">Uniform Precisions</caption>
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* <tr><th>A / X / Y / 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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* \par Mixed Precisions:
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* <table>
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* <caption id="spmv_mixed">Mixed Precisions</caption>
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* <tr><th>A / X <th>Y <th>compute_type
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* <tr><td>HIP_R_8I <td>HIP_R_32I <td>HIP_R_32I
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* <tr><td>HIP_R_8I <td>HIP_R_32F <td>HIP_R_32F
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* <tr><td>HIP_R_16F <td>HIP_R_32F <td>HIP_R_32F
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* <tr><td>HIP_R_16F <td>HIP_R_16F <td>HIP_R_32F
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* <tr><td>HIP_R_16BF <td>HIP_R_32F <td>HIP_R_32F
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* <tr><td>HIP_R_16BF <td>HIP_R_16BF <td>HIP_R_32F
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* </table>
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*
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* \par Mixed-regular Real Precisions
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* <table>
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* <caption id="spmv_mixed_regular_real">Mixed-regular real precisions</caption>
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* <tr><th>A <th>X / Y / compute_type
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* <tr><td>HIP_R_32F <td>HIP_R_64F
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* <tr><td>HIP_C_32F <td>HIP_C_64F
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* </table>
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*
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* \par Mixed-regular Complex Precisions
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* <table>
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* <caption id="spmv_mixed_regular_complex">Mixed-regular Complex precisions</caption>
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* <tr><th>A <th>X / Y / compute_type
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* <tr><td>HIP_R_32F <td>HIP_C_32F
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* <tr><td>HIP_R_64F <td>HIP_C_64F
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* </table>
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*
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* \p hipsparseSpMV supports \ref HIPSPARSE_INDEX_32I and \ref HIPSPARSE_INDEX_64I index precisions
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* for storing the row pointer and row/column indices arrays of the sparse matrices.
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*
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* \note
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* None of the algorithms above are deterministic when \f$A\f$ is transposed.
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*
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* \note
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* The sparse matrix formats currently supported are: \ref HIPSPARSE_FORMAT_COO, \ref HIPSPARSE_FORMAT_COO_AOS,
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* \ref HIPSPARSE_FORMAT_CSR, \ref HIPSPARSE_FORMAT_CSC, \ref HIPSPARSE_FORMAT_BSR, and \ref HIPSPARSE_FORMAT_SLICED_ELL.
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*
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* \note
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* Only the \ref hipsparseSpMV_bufferSize and \ref hipsparseSpMV routines are non-blocking and executed asynchronously
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* with respect to the host. They can return before the actual computation has finished. The \ref hipsparseSpMV_preprocess
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* routine is blocking with respect to the host.
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*
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* \note
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* Only the \ref hipsparseSpMV_bufferSize and the \ref hipsparseSpMV routines support execution in a hipGraph context.
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* The \ref hipsparseSpMV_preprocess stage does not support hipGraph.
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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.
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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 matrix descriptor.
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* @param[in]
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* vecX vector descriptor.
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* @param[in]
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* beta scalar \f$\beta\f$.
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* @param[inout]
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* vecY vector descriptor.
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* @param[in]
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* computeType floating point precision for the SpMV computation.
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* @param[in]
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* alg SpMV algorithm for the SpMV computation.
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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 x, \p beta, \p y, or
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* \p externalBuffer pointer is invalid or if \p opA, \p computeType, \p alg is incorrect.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \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 hipsparseSpMV(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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const void* alpha,
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hipsparseConstSpMatDescr_t matA,
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hipsparseConstDnVecDescr_t vecX,
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const void* beta,
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const hipsparseDnVecDescr_t vecY,
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hipDataType computeType,
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hipsparseSpMVAlg_t alg,
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void* externalBuffer);
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#elif(CUDART_VERSION > 10010 || (CUDART_VERSION == 10010 && CUDART_10_1_UPDATE_VERSION == 1))
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSpMV(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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const void* alpha,
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const hipsparseSpMatDescr_t matA,
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const hipsparseDnVecDescr_t vecX,
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const void* beta,
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const hipsparseDnVecDescr_t vecY,
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hipDataType computeType,
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hipsparseSpMVAlg_t alg,
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void* externalBuffer);
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#endif
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#ifdef __cplusplus
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}
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#endif
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#endif /* HIPSPARSE_SPMV_H */
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