forked from NVIDIA/CUDALibrarySamples
-
Notifications
You must be signed in to change notification settings - Fork 0
/
spgemm_reuse_example.c
310 lines (303 loc) · 15 KB
/
spgemm_reuse_example.c
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
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
/*
* Copyright 1993-2022 NVIDIA Corporation. All rights reserved.
*
* NOTICE TO LICENSEE:
*
* This source code and/or documentation ("Licensed Deliverables") are
* subject to NVIDIA intellectual property rights under U.S. and
* international Copyright laws.
*
* These Licensed Deliverables contained herein is PROPRIETARY and
* CONFIDENTIAL to NVIDIA and is being provided under the terms and
* conditions of a form of NVIDIA software license agreement by and
* between NVIDIA and Licensee ("License Agreement") or electronically
* accepted by Licensee. Notwithstanding any terms or conditions to
* the contrary in the License Agreement, reproduction or disclosure
* of the Licensed Deliverables to any third party without the express
* written consent of NVIDIA is prohibited.
*
* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
* OF THESE LICENSED DELIVERABLES.
*
* U.S. Government End Users. These Licensed Deliverables are a
* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
* 1995), consisting of "commercial computer software" and "commercial
* computer software documentation" as such terms are used in 48
* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
* U.S. Government End Users acquire the Licensed Deliverables with
* only those rights set forth herein.
*
* Any use of the Licensed Deliverables in individual and commercial
* software must include, in the user documentation and internal
* comments to the code, the above Disclaimer and U.S. Government End
* Users Notice.
*/
#include <cuda_runtime_api.h> // cudaMalloc, cudaMemcpy, etc.
#include <cusparse.h> // cusparseSpGEMM
#include <stdio.h> // printf
#include <stdlib.h> // EXIT_FAILURE
#define CHECK_CUDA(func) \
{ \
cudaError_t status = (func); \
if (status != cudaSuccess) { \
printf("CUDA API failed at line %d with error: %s (%d)\n", \
__LINE__, cudaGetErrorString(status), status); \
return EXIT_FAILURE; \
} \
}
#define CHECK_CUSPARSE(func) \
{ \
cusparseStatus_t status = (func); \
if (status != CUSPARSE_STATUS_SUCCESS) { \
printf("CUSPARSE API failed at line %d with error: %s (%d)\n", \
__LINE__, cusparseGetErrorString(status), status); \
return EXIT_FAILURE; \
} \
}
int main(void) {
// Host problem definition
#define A_NUM_ROWS 4 // C compatibility
const int A_num_rows = 4;
const int A_num_cols = 4;
const int A_nnz = 9;
const int B_num_rows = 4;
const int B_num_cols = 4;
const int B_nnz = 9;
int hA_csrOffsets[] = { 0, 3, 4, 7, 9 };
int hA_columns[] = { 0, 2, 3, 1, 0, 2, 3, 1, 3 };
float hA_values[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
6.0f, 7.0f, 8.0f, 9.0f };
int hB_csrOffsets[] = { 0, 2, 4, 7, 8 };
int hB_columns[] = { 0, 3, 1, 3, 0, 1, 2, 1 };
float hB_values[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
6.0f, 7.0f, 8.0f };
int hC_csrOffsets[] = { 0, 4, 6, 10, 12 };
int hC_columns[] = { 0, 1, 2, 3, 1, 3, 0, 1, 2, 3, 1, 3 };
float hC_values[] = { 11.0f, 36.0f, 14.0f, 2.0f, 12.0f,
16.0f, 35.0f, 92.0f, 42.0f, 10.0f,
96.0f, 32.0f };
const int C_nnz = 12;
#define C_NUM_NNZ 12 // C compatibility
float alpha = 1.0f;
float beta = 0.0f;
cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
cusparseOperation_t opB = CUSPARSE_OPERATION_NON_TRANSPOSE;
cudaDataType computeType = CUDA_R_32F;
//--------------------------------------------------------------------------
// Device memory management: Allocate and copy A, B
int *dA_csrOffsets, *dA_columns, *dB_csrOffsets, *dB_columns,
*dC_csrOffsets, *dC_columns;
float *dA_values, *dB_values, *dC_values;
// allocate A
CHECK_CUDA( cudaMalloc((void**) &dA_csrOffsets,
(A_num_rows + 1) * sizeof(int)) )
CHECK_CUDA( cudaMalloc((void**) &dA_columns, A_nnz * sizeof(int)) )
CHECK_CUDA( cudaMalloc((void**) &dA_values, A_nnz * sizeof(float)) )
// allocate B
CHECK_CUDA( cudaMalloc((void**) &dB_csrOffsets,
(B_num_rows + 1) * sizeof(int)) )
CHECK_CUDA( cudaMalloc((void**) &dB_columns, B_nnz * sizeof(int)) )
CHECK_CUDA( cudaMalloc((void**) &dB_values, B_nnz * sizeof(float)) )
// allocate C offsets
CHECK_CUDA( cudaMalloc((void**) &dC_csrOffsets,
(A_num_rows + 1) * sizeof(int)) )
// copy A
CHECK_CUDA( cudaMemcpy(dA_csrOffsets, hA_csrOffsets,
(A_num_rows + 1) * sizeof(int),
cudaMemcpyHostToDevice) )
CHECK_CUDA( cudaMemcpy(dA_columns, hA_columns, A_nnz * sizeof(int),
cudaMemcpyHostToDevice) )
CHECK_CUDA( cudaMemcpy(dA_values, hA_values,
A_nnz * sizeof(float), cudaMemcpyHostToDevice) )
// copy B
CHECK_CUDA( cudaMemcpy(dB_csrOffsets, hB_csrOffsets,
(B_num_rows + 1) * sizeof(int),
cudaMemcpyHostToDevice) )
CHECK_CUDA( cudaMemcpy(dB_columns, hB_columns, B_nnz * sizeof(int),
cudaMemcpyHostToDevice) )
CHECK_CUDA( cudaMemcpy(dB_values, hB_values,
B_nnz * sizeof(float), cudaMemcpyHostToDevice) )
//--------------------------------------------------------------------------
// CUSPARSE APIs
cusparseHandle_t handle = NULL;
cusparseSpMatDescr_t matA, matB, matC;
void* dBuffer1 = NULL;
void* dBuffer2 = NULL;
void* dBuffer3 = NULL;
void* dBuffer4 = NULL;
void* dBuffer5 = NULL;
size_t bufferSize1 = 0;
size_t bufferSize2 = 0;
size_t bufferSize3 = 0;
size_t bufferSize4 = 0;
size_t bufferSize5 = 0;
CHECK_CUSPARSE( cusparseCreate(&handle) )
// Create sparse matrix A in CSR format
CHECK_CUSPARSE( cusparseCreateCsr(&matA, A_num_rows, A_num_cols, A_nnz,
dA_csrOffsets, dA_columns, dA_values,
CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F) )
CHECK_CUSPARSE( cusparseCreateCsr(&matB, B_num_rows, B_num_cols, B_nnz,
dB_csrOffsets, dB_columns, dB_values,
CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F) )
CHECK_CUSPARSE( cusparseCreateCsr(&matC, A_num_rows, B_num_cols, 0,
NULL, NULL, NULL,
CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F) )
//==========================================================================
// SpGEMM Computation
//==========================================================================
cusparseSpGEMMDescr_t spgemmDesc;
CHECK_CUSPARSE( cusparseSpGEMM_createDescr(&spgemmDesc) )
// ask bufferSize1 bytes for external memory
CHECK_CUSPARSE(
cusparseSpGEMMreuse_workEstimation(handle, opA, opB, matA, matB, matC,
CUSPARSE_SPGEMM_DEFAULT,
spgemmDesc, &bufferSize1, NULL)
)
CHECK_CUDA( cudaMalloc((void**) &dBuffer1, bufferSize1) )
// inspect the matrices A and B to understand the memory requirement for
// the next step
CHECK_CUSPARSE(
cusparseSpGEMMreuse_workEstimation(handle, opA, opB, matA, matB, matC,
CUSPARSE_SPGEMM_DEFAULT,
spgemmDesc, &bufferSize1, dBuffer1)
)
//--------------------------------------------------------------------------
CHECK_CUSPARSE(
cusparseSpGEMMreuse_nnz(handle, opA, opB, matA, matB,
matC, CUSPARSE_SPGEMM_DEFAULT, spgemmDesc,
&bufferSize2, NULL, &bufferSize3, NULL,
&bufferSize4, NULL)
)
CHECK_CUDA( cudaMalloc((void**) &dBuffer2, bufferSize2) )
CHECK_CUDA( cudaMalloc((void**) &dBuffer3, bufferSize3) )
CHECK_CUDA( cudaMalloc((void**) &dBuffer4, bufferSize4) )
CHECK_CUSPARSE(
cusparseSpGEMMreuse_nnz(handle, opA, opB, matA, matB,
matC, CUSPARSE_SPGEMM_DEFAULT, spgemmDesc,
&bufferSize2, dBuffer2, &bufferSize3, dBuffer3,
&bufferSize4, dBuffer4)
)
CHECK_CUDA( cudaFree(dBuffer1) )
CHECK_CUDA( cudaFree(dBuffer2) )
//--------------------------------------------------------------------------
// get matrix C non-zero entries C_nnz1
int64_t C_num_rows1, C_num_cols1, C_nnz1;
CHECK_CUSPARSE( cusparseSpMatGetSize(matC, &C_num_rows1, &C_num_cols1,
&C_nnz1) )
// allocate matrix C
CHECK_CUDA( cudaMalloc((void**) &dC_columns, C_nnz1 * sizeof(int)) )
CHECK_CUDA( cudaMalloc((void**) &dC_values, C_nnz1 * sizeof(float)) )
CHECK_CUDA( cudaMemset(dC_values, 0x0, C_nnz1 * sizeof(float)) )
// fill dC_values if needed
// update matC with the new pointers
CHECK_CUSPARSE(
cusparseCsrSetPointers(matC, dC_csrOffsets, dC_columns, dC_values) )
//--------------------------------------------------------------------------
CHECK_CUSPARSE(
cusparseSpGEMMreuse_copy(handle, opA, opB, matA, matB, matC,
CUSPARSE_SPGEMM_DEFAULT, spgemmDesc,
&bufferSize5, NULL)
)
CHECK_CUDA( cudaMalloc((void**) &dBuffer5, bufferSize5) )
CHECK_CUSPARSE(
cusparseSpGEMMreuse_copy(handle, opA, opB, matA, matB, matC,
CUSPARSE_SPGEMM_DEFAULT, spgemmDesc,
&bufferSize5, dBuffer5)
)
CHECK_CUDA( cudaFree(dBuffer3) )
//--------------------------------------------------------------------------
// first run
CHECK_CUSPARSE(
cusparseSpGEMMreuse_compute(handle, opA, opB, &alpha, matA, matB, &beta,
matC, computeType, CUSPARSE_SPGEMM_DEFAULT,
spgemmDesc)
)
// update dA_values, dB_values
CHECK_CUDA( cudaMemcpy(dA_values, hA_values,
A_nnz * sizeof(float), cudaMemcpyHostToDevice) )
CHECK_CUDA( cudaMemcpy(dB_values, hB_values,
B_nnz * sizeof(float), cudaMemcpyHostToDevice) )
// second run
CHECK_CUSPARSE(
cusparseSpGEMMreuse_compute(handle, opA, opB, &alpha, matA, matB, &beta,
matC, computeType, CUSPARSE_SPGEMM_DEFAULT,
spgemmDesc)
)
//==========================================================================
//==========================================================================
// destroy matrix/vector descriptors
CHECK_CUSPARSE( cusparseSpGEMM_destroyDescr(spgemmDesc) )
CHECK_CUSPARSE( cusparseDestroySpMat(matA) )
CHECK_CUSPARSE( cusparseDestroySpMat(matB) )
CHECK_CUSPARSE( cusparseDestroySpMat(matC) )
CHECK_CUSPARSE( cusparseDestroy(handle) )
//--------------------------------------------------------------------------
// device result check
int hC_csrOffsets_tmp[A_NUM_ROWS + 1];
int hC_columns_tmp[C_NUM_NNZ];
float hC_values_tmp[C_NUM_NNZ];
CHECK_CUDA( cudaMemcpy(hC_csrOffsets_tmp, dC_csrOffsets,
(A_num_rows + 1) * sizeof(int),
cudaMemcpyDeviceToHost) )
CHECK_CUDA( cudaMemcpy(hC_columns_tmp, dC_columns, C_nnz * sizeof(int),
cudaMemcpyDeviceToHost) )
CHECK_CUDA( cudaMemcpy(hC_values_tmp, dC_values, C_nnz * sizeof(float),
cudaMemcpyDeviceToHost) )
int correct = 1;
for (int i = 0; i < A_num_rows + 1; i++) {
if (hC_csrOffsets_tmp[i] != hC_csrOffsets[i]) {
correct = 0;
break;
}
}
for (int i = 0; i < C_nnz; i++) {
if (hC_columns_tmp[i] != hC_columns[i]) { // direct floating point
correct = 0; // comparison is not reliable
break;
}
}
for (int i = 0; i < C_nnz; i++) {
if (hC_columns_tmp[i] != hC_columns[i] ||
hC_values_tmp[i] != hC_values[i]) { // direct floating point
correct = 0; // comparison is not reliable
break;
}
}
if (correct)
printf("spgemm_reuse_example test PASSED\n");
else {
printf("spgemm_reuse_example test FAILED: wrong result\n");
return EXIT_FAILURE;
}
//--------------------------------------------------------------------------
// device memory deallocation
CHECK_CUDA( cudaFree(dBuffer4) )
CHECK_CUDA( cudaFree(dBuffer5) )
CHECK_CUDA( cudaFree(dA_csrOffsets) )
CHECK_CUDA( cudaFree(dA_columns) )
CHECK_CUDA( cudaFree(dA_values) )
CHECK_CUDA( cudaFree(dB_csrOffsets) )
CHECK_CUDA( cudaFree(dB_columns) )
CHECK_CUDA( cudaFree(dB_values) )
CHECK_CUDA( cudaFree(dC_csrOffsets) )
CHECK_CUDA( cudaFree(dC_columns) )
CHECK_CUDA( cudaFree(dC_values) )
return EXIT_SUCCESS;
}