Spaces:
Runtime error
Runtime error
File size: 8,515 Bytes
28958dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. 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.
*
******************************************************************************/
/**
* \file
* cub::DeviceSpmv provides device-wide parallel operations for performing sparse-matrix * vector multiplication (SpMV).
*/
#pragma once
#include <stdio.h>
#include <iterator>
#include <limits>
#include "dispatch/dispatch_spmv_orig.cuh"
#include "../config.cuh"
/// Optional outer namespace(s)
CUB_NS_PREFIX
/// CUB namespace
namespace cub {
/**
* \brief DeviceSpmv provides device-wide parallel operations for performing sparse-matrix * dense-vector multiplication (SpMV).
* \ingroup SingleModule
*
* \par Overview
* The [<em>SpMV computation</em>](http://en.wikipedia.org/wiki/Sparse_matrix-vector_multiplication)
* performs the matrix-vector operation
* <em>y</em> = <em>alpha</em>*<b>A</b>*<em>x</em> + <em>beta</em>*<em>y</em>,
* where:
* - <b>A</b> is an <em>m</em>x<em>n</em> sparse matrix whose non-zero structure is specified in
* [<em>compressed-storage-row (CSR) format</em>](http://en.wikipedia.org/wiki/Sparse_matrix#Compressed_row_Storage_.28CRS_or_CSR.29)
* (i.e., three arrays: <em>values</em>, <em>row_offsets</em>, and <em>column_indices</em>)
* - <em>x</em> and <em>y</em> are dense vectors
* - <em>alpha</em> and <em>beta</em> are scalar multiplicands
*
* \par Usage Considerations
* \cdp_class{DeviceSpmv}
*
*/
struct DeviceSpmv
{
/******************************************************************//**
* \name CSR matrix operations
*********************************************************************/
//@{
/**
* \brief This function performs the matrix-vector operation <em>y</em> = <b>A</b>*<em>x</em>.
*
* \par Snippet
* The code snippet below illustrates SpMV upon a 9x9 CSR matrix <b>A</b>
* representing a 3x3 lattice (24 non-zeros).
*
* \par
* \code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_spmv.cuh>
*
* // Declare, allocate, and initialize device-accessible pointers for input matrix A, input vector x,
* // and output vector y
* int num_rows = 9;
* int num_cols = 9;
* int num_nonzeros = 24;
*
* float* d_values; // e.g., [1, 1, 1, 1, 1, 1, 1, 1,
* // 1, 1, 1, 1, 1, 1, 1, 1,
* // 1, 1, 1, 1, 1, 1, 1, 1]
*
* int* d_column_indices; // e.g., [1, 3, 0, 2, 4, 1, 5, 0,
* // 4, 6, 1, 3, 5, 7, 2, 4,
* // 8, 3, 7, 4, 6, 8, 5, 7]
*
* int* d_row_offsets; // e.g., [0, 2, 5, 7, 10, 14, 17, 19, 22, 24]
*
* float* d_vector_x; // e.g., [1, 1, 1, 1, 1, 1, 1, 1, 1]
* float* d_vector_y; // e.g., [ , , , , , , , , ]
* ...
*
* // Determine temporary device storage requirements
* void* d_temp_storage = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceSpmv::CsrMV(d_temp_storage, temp_storage_bytes, d_values,
* d_row_offsets, d_column_indices, d_vector_x, d_vector_y,
* num_rows, num_cols, num_nonzeros, alpha, beta);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run SpMV
* cub::DeviceSpmv::CsrMV(d_temp_storage, temp_storage_bytes, d_values,
* d_row_offsets, d_column_indices, d_vector_x, d_vector_y,
* num_rows, num_cols, num_nonzeros, alpha, beta);
*
* // d_vector_y <-- [2, 3, 2, 3, 4, 3, 2, 3, 2]
*
* \endcode
*
* \tparam ValueT <b>[inferred]</b> Matrix and vector value type (e.g., /p float, /p double, etc.)
*/
template <
typename ValueT>
CUB_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 <b>A</b>.
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 <b>A</b>. (Indices are zero-valued.)
ValueT* d_vector_x, ///< [in] Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
ValueT* d_vector_y, ///< [out] Pointer to the array of \p num_rows values corresponding to the dense output vector <em>y</em>
int num_rows, ///< [in] number of rows of matrix <b>A</b>.
int num_cols, ///< [in] number of columns of matrix <b>A</b>.
int num_nonzeros, ///< [in] number of nonzero elements of matrix <b>A</b>.
cudaStream_t stream = 0, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous = false) ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. May cause significant slowdown. Default is \p false.
{
SpmvParams<ValueT, int> 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;
return DispatchSpmv<ValueT, int>::Dispatch(
d_temp_storage,
temp_storage_bytes,
spmv_params,
stream,
debug_synchronous);
}
//@} end member group
};
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)
|