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
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
|
/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2022, 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 <cub/config.cuh>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <stdio.h>
#include <iterator>
#include <limits>
#include <cub/config.cuh>
#include <cub/device/dispatch/dispatch_spmv_orig.cuh>
#include <cub/util_deprecated.cuh>
CUB_NAMESPACE_BEGIN
/**
* @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> = <b>A</b>*<em>x</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
*
* @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);
*
* // 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);
*
* // 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.)
*
* @param[in] d_temp_storage
* Device-accessible allocation of temporary storage.
* When NULL, the required allocation size is written to @p temp_storage_bytes
* and no work is done.
*
* @param[in,out] temp_storage_bytes
* Reference to size in bytes of @p d_temp_storage allocation
*
* @param[in] d_values
* Pointer to the array of @p num_nonzeros values of the corresponding nonzero elements
* of matrix <b>A</b>.
*
* @param[in] d_row_offsets
* 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)
*
* @param[in] d_column_indices
* Pointer to the array of @p num_nonzeros column-indices of the corresponding nonzero
* elements of matrix <b>A</b>. (Indices are zero-valued.)
*
* @param[in] d_vector_x
* Pointer to the array of @p num_cols values corresponding to the dense input vector
* <em>x</em>
*
* @param[out] d_vector_y
* Pointer to the array of @p num_rows values corresponding to the dense output vector
* <em>y</em>
*
* @param[in] num_rows
* number of rows of matrix <b>A</b>.
*
* @param[in] num_cols
* number of columns of matrix <b>A</b>.
*
* @param[in] num_nonzeros
* number of nonzero elements of matrix <b>A</b>.
*
* @param[in] stream
* <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
*/
template <typename ValueT>
CUB_RUNTIME_FUNCTION static cudaError_t CsrMV(void *d_temp_storage,
size_t &temp_storage_bytes,
const ValueT *d_values,
const int *d_row_offsets,
const int *d_column_indices,
const ValueT *d_vector_x,
ValueT *d_vector_y,
int num_rows,
int num_cols,
int num_nonzeros,
cudaStream_t stream = 0)
{
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 = ValueT{1};
spmv_params.beta = ValueT{0};
return DispatchSpmv<ValueT, int>::Dispatch(
d_temp_storage,
temp_storage_bytes,
spmv_params,
stream);
}
template <typename ValueT>
CUB_DETAIL_RUNTIME_DEBUG_SYNC_IS_NOT_SUPPORTED
CUB_RUNTIME_FUNCTION static cudaError_t CsrMV(void *d_temp_storage,
size_t &temp_storage_bytes,
const ValueT *d_values,
const int *d_row_offsets,
const int *d_column_indices,
const ValueT *d_vector_x,
ValueT *d_vector_y,
int num_rows,
int num_cols,
int num_nonzeros,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return CsrMV<ValueT>(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,
stream);
}
//@} end member group
};
CUB_NAMESPACE_END
|
