/****************************************************************************** * Copyright (c) 2010-2011, Duane Merrill. All rights reserved. * Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved. * Modifications Copyright (c) 2017-2021, Advanced Micro Devices, Inc. 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IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * ******************************************************************************/ #ifndef HIPCUB_ROCPRIM_DEVICE_DEVICE_SPMV_HPP_ #define HIPCUB_ROCPRIM_DEVICE_DEVICE_SPMV_HPP_ #include "../config.hpp" #include "../iterator/tex_ref_input_iterator.cuh" BEGIN_HIPCUB_NAMESPACE class DeviceSpmv { public: template < typename ValueT, ///< Matrix and vector value type typename OffsetT> ///< Signed integer type for sequence offsets struct SpmvParams { ValueT* d_values; ///< Pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix A. OffsetT* d_row_end_offsets; ///< Pointer to the array of \p m offsets demarcating the end of every row in \p d_column_indices and \p d_values OffsetT* d_column_indices; ///< Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix A. (Indices are zero-valued.) ValueT* d_vector_x; ///< Pointer to the array of \p num_cols values corresponding to the dense input vector x ValueT* d_vector_y; ///< Pointer to the array of \p num_rows values corresponding to the dense output vector y int num_rows; ///< Number of rows of matrix A. int num_cols; ///< Number of columns of matrix A. int num_nonzeros; ///< Number of nonzero elements of matrix A. ValueT alpha; ///< Alpha multiplicand ValueT beta; ///< Beta addend-multiplicand ::cub::TexRefInputIterator t_vector_x; }; static constexpr uint32_t CsrMVKernel_MaxThreads = 256; template static __global__ void CsrMVKernel(SpmvParams spmv_params) { __shared__ ValueT partial; const int32_t row_id = hipBlockIdx_x; if(threadIdx.x == 0) { partial = spmv_params.beta * spmv_params.d_vector_y[row_id]; } __syncthreads(); int32_t row_offset = (row_id == 0) ? (0) : (spmv_params.d_row_end_offsets[row_id - 1]); for(uint32_t thread_offset = 0; thread_offset < spmv_params.num_cols / blockDim.x; thread_offset++) { int32_t offset = row_offset + thread_offset * blockDim.x + threadIdx.x; if(offset < spmv_params.d_row_end_offsets[row_id]) { ValueT t_value = spmv_params.alpha * spmv_params.d_values[offset] * spmv_params.d_vector_x[spmv_params.d_column_indices[offset]]; atomicAdd(&partial, t_value); __syncthreads(); iif(threadIdx.x == 0) { spmv_params.d_vector_y[row_id] = partial; } } } } template HIPCUB_RUNTIME_FUNCTION static cudaError_t CsrMV( void* d_temp_storage, ///< [in] %Device-accessible allocation of temporary storage. When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done. size_t& temp_storage_bytes, ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation ValueT* d_values, ///< [in] Pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix A. int* d_row_offsets, ///< [in] Pointer to the array of \p m + 1 offsets demarcating the start of every row in \p d_column_indices and \p d_values (with the final entry being equal to \p num_nonzeros) int* d_column_indices, ///< [in] Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix A. (Indices are zero-valued.) ValueT* d_vector_x, ///< [in] Pointer to the array of \p num_cols values corresponding to the dense input vector x ValueT* d_vector_y, ///< [out] Pointer to the array of \p num_rows values corresponding to the dense output vector y int num_rows, ///< [in] number of rows of matrix A. int num_cols, ///< [in] number of columns of matrix A. int num_nonzeros, ///< [in] number of nonzero elements of matrix A. cudaStream_t stream = 0, ///< [in] [optional] hip stream to launch kernels within. Default is stream0. bool debug_synchronous = false) ///< [in] [optional] Whether or not to synchronize the stream after every kernel launch to check for errors. May cause significant slowdown. Default is \p false. { SpmvParams spmv_params; spmv_params.d_values = d_values; spmv_params.d_row_end_offsets = d_row_offsets + 1; spmv_params.d_column_indices = d_column_indices; spmv_params.d_vector_x = d_vector_x; spmv_params.d_vector_y = d_vector_y; spmv_params.num_rows = num_rows; spmv_params.num_cols = num_cols; spmv_params.num_nonzeros = num_nonzeros; spmv_params.alpha = 1.0; spmv_params.beta = 0.0; cudaError_t status; if(d_temp_storage == nullptr) { // Make sure user won't try to allocate 0 bytes memory, because // hipMalloc will return nullptr when size is zero. temp_storage_bytes = 4; return cudaError_t(0); } else { size_t block_size = min(num_cols, DeviceSpmv::CsrMVKernel_MaxThreads); size_t grid_size = num_rows; CsrMVKernel<<>>(spmv_params); status = hipGetLastError(); } return status; } }; END_HIPCUB_NAMESPACE #endif // HIPCUB_CUB_DEVICE_DEVICE_SELECT_HPP_