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tools/cub-1.8.0/cub/device/device_spmv.cuh
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/******************************************************************************
* 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 "../util_namespace.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
{
/******************************************************************//**
*
ame 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)
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