ViennaCL - The Vienna Computing Library: viennacl/linalg/cuda/spgemm_rmerge.hpp Source File

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spgemm_rmerge.hpp
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1 #ifndef VIENNACL_LINALG_CUDA_SPGEMM_RMERGE_HPP_
2 #define VIENNACL_LINALG_CUDA_SPGEMM_RMERGE_HPP_
3
4 /* =========================================================================
5 Copyright (c) 2010-2016, Institute for Microelectronics,
6 Institute for Analysis and Scientific Computing,
7 TU Wien.
8 Portions of this software are copyright by UChicago Argonne, LLC.
9
10 -----------------
11 ViennaCL - The Vienna Computing Library
12 -----------------
13
14 Project Head: Karl Rupp rupp@iue.tuwien.ac.at
15
16 (A list of authors and contributors can be found in the manual)
17
18 License: MIT (X11), see file LICENSE in the base directory
19 ============================================================================= */
20
25 #include <stdexcept>
26
27 #include "viennacl/forwards.h"
28 #include "viennacl/scalar.hpp"
29 #include "viennacl/vector.hpp"
30 #include "viennacl/tools/tools.hpp"
31 #include "viennacl/linalg/cuda/common.hpp"
32
33 #include "viennacl/tools/timer.hpp"
34
35 #include "viennacl/linalg/cuda/sparse_matrix_operations_solve.hpp"
36
37 namespace viennacl
38 {
39 namespace linalg
40 {
41 namespace cuda
42 {
43
45 template<typename NumericT>
46 static inline __device__ NumericT load_and_cache(const NumericT *address)
47 {
48 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350
49 return __ldg(address);
50 #else
51 return *address;
52 #endif
53 }
54
55
56 //
57 // Stage 1: Obtain upper bound for number of elements per row in C:
58 //
59 template<typename IndexT>
60 __device__ IndexT round_to_next_power_of_2(IndexT val)
61 {
62 if (val > 32)
63 return 64; // just to indicate that we need to split/factor the matrix!
64 else if (val > 16)
65 return 32;
66 else if (val > 8)
67 return 16;
68 else if (val > 4)
69 return 8;
70 else if (val > 2)
71 return 4;
72 else if (val > 1)
73 return 2;
74 else
75 return 1;
76 }
77
78 template<typename IndexT>
79 __global__ void compressed_matrix_gemm_stage_1(
80 const IndexT * A_row_indices,
81 const IndexT * A_col_indices,
82 IndexT A_size1,
83 const IndexT * B_row_indices,
84 IndexT *subwarpsize_per_group,
85 IndexT *max_nnz_row_A_per_group,
86 IndexT *max_nnz_row_B_per_group)
87 {
88 unsigned int subwarpsize_in_thread = 0;
89 unsigned int max_nnz_row_A = 0;
90 unsigned int max_nnz_row_B = 0;
91
92 unsigned int rows_per_group = (A_size1 - 1) / gridDim.x + 1;
93 unsigned int row_per_group_end = min(A_size1, rows_per_group * (blockIdx.x + 1));
94
95 for (unsigned int row = rows_per_group * blockIdx.x + threadIdx.x; row < row_per_group_end; row += blockDim.x)
96 {
97 unsigned int A_row_start = A_row_indices[row];
98 unsigned int A_row_end = A_row_indices[row+1];
99 unsigned int row_num = A_row_end - A_row_start;
100 subwarpsize_in_thread = max(A_row_end - A_row_start, subwarpsize_in_thread);
101 max_nnz_row_A = max(max_nnz_row_A, row_num);
102 for (unsigned int j = A_row_start; j < A_row_end; ++j)
103 {
104 unsigned int col = A_col_indices[j];
105 unsigned int row_len_B = B_row_indices[col + 1] - B_row_indices[col];
106 max_nnz_row_B = max(row_len_B, max_nnz_row_B);
107 }
108 }
109
110 // reduction to obtain maximum in thread block
111 __shared__ unsigned int shared_subwarpsize[256];
112 __shared__ unsigned int shared_max_nnz_row_A[256];
113 __shared__ unsigned int shared_max_nnz_row_B[256];
114
115 shared_subwarpsize[threadIdx.x] = subwarpsize_in_thread;
116 shared_max_nnz_row_A[threadIdx.x] = max_nnz_row_A;
117 shared_max_nnz_row_B[threadIdx.x] = max_nnz_row_B;
118 for (unsigned int stride = blockDim.x/2; stride > 0; stride /= 2)
119 {
120 __syncthreads();
121 if (threadIdx.x < stride)
122 {
123 shared_subwarpsize[threadIdx.x] = max( shared_subwarpsize[threadIdx.x], shared_subwarpsize[threadIdx.x + stride]);
124 shared_max_nnz_row_A[threadIdx.x] = max(shared_max_nnz_row_A[threadIdx.x], shared_max_nnz_row_A[threadIdx.x + stride]);
125 shared_max_nnz_row_B[threadIdx.x] = max(shared_max_nnz_row_B[threadIdx.x], shared_max_nnz_row_B[threadIdx.x + stride]);
126 }
127 }
128
129 if (threadIdx.x == 0)
130 {
131 subwarpsize_per_group[blockIdx.x] = round_to_next_power_of_2(shared_subwarpsize[0]);
132 max_nnz_row_A_per_group[blockIdx.x] = shared_max_nnz_row_A[0];
133 max_nnz_row_B_per_group[blockIdx.x] = shared_max_nnz_row_B[0];
134 }
135 }
136
137 //
138 // Stage 2: Determine sparsity pattern of C
139 //
140
141 // Using warp shuffle routines (CUDA arch 3.5)
142 template<unsigned int SubWarpSizeV, typename IndexT>
143 __device__ IndexT subwarp_minimum_shuffle(IndexT min_index)
144 {
145 for (unsigned int i = SubWarpSizeV/2; i >= 1; i /= 2)
146 min_index = min(min_index, __shfl_xor((int)min_index, (int)i));
147 return min_index;
148 }
149
150 // Using shared memory
151 template<unsigned int SubWarpSizeV, typename IndexT>
152 __device__ IndexT subwarp_minimum_shared(IndexT min_index, IndexT id_in_warp, IndexT *shared_buffer)
153 {
154 shared_buffer[threadIdx.x] = min_index;
155 for (unsigned int i = SubWarpSizeV/2; i >= 1; i /= 2)
156 shared_buffer[threadIdx.x] = min(shared_buffer[threadIdx.x], shared_buffer[(threadIdx.x + i) % 512]);
157 return shared_buffer[threadIdx.x - id_in_warp];
158 }
159
160
161 template<unsigned int SubWarpSizeV, typename IndexT>
162 __global__ void compressed_matrix_gemm_stage_2(
163 const IndexT * A_row_indices,
164 const IndexT * A_col_indices,
165 IndexT A_size1,
166 const IndexT * B_row_indices,
167 const IndexT * B_col_indices,
168 IndexT B_size2,
169 IndexT * C_row_indices)
170 {
171 __shared__ unsigned int shared_buffer[512];
172
173 unsigned int num_warps = blockDim.x / SubWarpSizeV;
174 unsigned int warp_id = threadIdx.x / SubWarpSizeV;
175 unsigned int id_in_warp = threadIdx.x % SubWarpSizeV;
176
177 unsigned int rows_per_group = (A_size1 - 1) / gridDim.x + 1;
178 unsigned int row_per_group_end = min(A_size1, rows_per_group * (blockIdx.x + 1));
179
180 for (unsigned int row = rows_per_group * blockIdx.x + warp_id; row < row_per_group_end; row += num_warps)
181 {
182 unsigned int row_A_start = A_row_indices[row];
183 unsigned int row_A_end = A_row_indices[row+1];
184
185 unsigned int my_row_B = row_A_start + id_in_warp;
186 unsigned int row_B_index = (my_row_B < row_A_end) ? A_col_indices[my_row_B] : 0;
187 unsigned int row_B_start = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index) : 0;
188 unsigned int row_B_end = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index + 1) : 0;
189
190 unsigned int num_nnz = 0;
191 if (row_A_end - row_A_start > 1) // zero or no row can be processed faster
192 {
193 unsigned int current_front_index = (row_B_start < row_B_end) ? load_and_cache(B_col_indices + row_B_start) : B_size2;
194
195 while (1)
196 {
197 // determine current minimum (warp shuffle)
198 unsigned int min_index = current_front_index;
199 min_index = subwarp_minimum_shared<SubWarpSizeV>(min_index, id_in_warp, shared_buffer);
200
201 if (min_index == B_size2)
202 break;
203
204 // update front:
205 if (current_front_index == min_index)
206 {
207 ++row_B_start;
208 current_front_index = (row_B_start < row_B_end) ? load_and_cache(B_col_indices + row_B_start) : B_size2;
209 }
210
211 ++num_nnz;
212 }
213 }
214 else
215 {
216 num_nnz = row_B_end - row_B_start;
217 }
218
219 if (id_in_warp == 0)
220 C_row_indices[row] = num_nnz;
221 }
222
223 }
224
225
226 //
227 // Stage 3: Fill C with values
228 //
229
230 // Using warp shuffle routines (CUDA arch 3.5)
231 template<unsigned int SubWarpSizeV, typename NumericT>
232 __device__ NumericT subwarp_accumulate_shuffle(NumericT output_value)
233 {
234 for (unsigned int i = SubWarpSizeV/2; i >= 1; i /= 2)
235 output_value += __shfl_xor((int)output_value, (int)i);
236 return output_value;
237 }
238
239 // Using shared memory
240 template<unsigned int SubWarpSizeV, typename NumericT>
241 __device__ NumericT subwarp_accumulate_shared(NumericT output_value, unsigned int id_in_warp, NumericT *shared_buffer)
242 {
243 shared_buffer[threadIdx.x] = output_value;
244 for (unsigned int i = SubWarpSizeV/2; i >= 1; i /= 2)
245 shared_buffer[threadIdx.x] += shared_buffer[(threadIdx.x + i) % 512];
246 return shared_buffer[threadIdx.x - id_in_warp];
247 }
248
249
250 template<unsigned int SubWarpSizeV, typename IndexT, typename NumericT>
251 __global__ void compressed_matrix_gemm_stage_3(
252 const IndexT * A_row_indices,
253 const IndexT * A_col_indices,
254 const NumericT * A_elements,
255 IndexT A_size1,
256 const IndexT * B_row_indices,
257 const IndexT * B_col_indices,
258 const NumericT * B_elements,
259 IndexT B_size2,
260 IndexT const * C_row_indices,
261 IndexT * C_col_indices,
262 NumericT * C_elements)
263 {
264 __shared__ unsigned int shared_indices[512];
265 __shared__ NumericT shared_values[512];
266
267 unsigned int num_warps = blockDim.x / SubWarpSizeV;
268 unsigned int warp_id = threadIdx.x / SubWarpSizeV;
269 unsigned int id_in_warp = threadIdx.x % SubWarpSizeV;
270
271 unsigned int rows_per_group = (A_size1 - 1) / gridDim.x + 1;
272 unsigned int row_per_group_end = min(A_size1, rows_per_group * (blockIdx.x + 1));
273
274 for (unsigned int row = rows_per_group * blockIdx.x + warp_id; row < row_per_group_end; row += num_warps)
275 {
276 unsigned int row_A_start = A_row_indices[row];
277 unsigned int row_A_end = A_row_indices[row+1];
278
279 unsigned int my_row_B = row_A_start + ((row_A_end - row_A_start > 1) ? id_in_warp : 0); // special case: single row
280 unsigned int row_B_index = (my_row_B < row_A_end) ? A_col_indices[my_row_B] : 0;
281 unsigned int row_B_start = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index) : 0;
282 unsigned int row_B_end = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index + 1) : 0;
283 NumericT val_A = (my_row_B < row_A_end) ? A_elements[my_row_B] : 0;
284
285 unsigned int index_in_C = C_row_indices[row];
286
287 if (row_A_end - row_A_start > 1)
288 {
289 unsigned int current_front_index = (row_B_start < row_B_end) ? load_and_cache(B_col_indices + row_B_start) : B_size2;
290 NumericT current_front_value = (row_B_start < row_B_end) ? load_and_cache(B_elements + row_B_start) : 0;
291
292 unsigned int index_buffer = 0;
293 NumericT value_buffer = 0;
294 unsigned int buffer_size = 0;
295 while (1)
296 {
297 // determine current minimum:
298 unsigned int min_index = subwarp_minimum_shared<SubWarpSizeV>(current_front_index, id_in_warp, shared_indices);
299
300 if (min_index == B_size2) // done
301 break;
302
303 // compute entry in C:
304 NumericT output_value = (current_front_index == min_index) ? val_A * current_front_value : 0;
305 output_value = subwarp_accumulate_shared<SubWarpSizeV>(output_value, id_in_warp, shared_values);
306
307 // update front:
308 if (current_front_index == min_index)
309 {
310 ++row_B_start;
311 current_front_index = (row_B_start < row_B_end) ? load_and_cache(B_col_indices + row_B_start) : B_size2;
312 current_front_value = (row_B_start < row_B_end) ? load_and_cache(B_elements + row_B_start) : 0;
313 }
314
315 // write current front to register buffer:
316 index_buffer = (id_in_warp == buffer_size) ? min_index : index_buffer;
317 value_buffer = (id_in_warp == buffer_size) ? output_value : value_buffer;
318 ++buffer_size;
319
320 // flush register buffer via a coalesced write once full:
321 if (buffer_size == SubWarpSizeV)
322 {
323 C_col_indices[index_in_C + id_in_warp] = index_buffer;
324 C_elements[index_in_C + id_in_warp] = value_buffer;
325 }
326
327 index_in_C += (buffer_size == SubWarpSizeV) ? SubWarpSizeV : 0;
328 buffer_size = (buffer_size == SubWarpSizeV) ? 0 : buffer_size;
329 }
330
331 // write remaining entries in register buffer to C:
332 if (id_in_warp < buffer_size)
333 {
334 C_col_indices[index_in_C + id_in_warp] = index_buffer;
335 C_elements[index_in_C + id_in_warp] = value_buffer;
336 }
337 }
338 else // write respective row using the full subwarp:
339 {
340 for (unsigned int i = row_B_start + id_in_warp; i < row_B_end; i += SubWarpSizeV)
341 {
342 C_col_indices[index_in_C + id_in_warp] = load_and_cache(B_col_indices + i);
343 C_elements[index_in_C + id_in_warp] = val_A * load_and_cache(B_elements + i);
344 index_in_C += SubWarpSizeV;
345 }
346 }
347
348 }
349
350 }
351
352
353
354 //
355 // Decomposition kernels:
356 //
357 template<typename IndexT>
358 __global__ void compressed_matrix_gemm_decompose_1(
359 const IndexT * A_row_indices,
360 IndexT A_size1,
361 IndexT max_per_row,
362 IndexT *chunks_per_row)
363 {
364 for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x; i < A_size1; i += blockDim.x * gridDim.x)
365 {
366 IndexT num_entries = A_row_indices[i+1] - A_row_indices[i];
367 chunks_per_row[i] = (num_entries < max_per_row) ? 1 : ((num_entries - 1)/ max_per_row + 1);
368 }
369 }
370
371
372 template<typename IndexT, typename NumericT>
373 __global__ void compressed_matrix_gemm_A2(
374 IndexT * A2_row_indices,
375 IndexT * A2_col_indices,
376 NumericT * A2_elements,
377 IndexT A2_size1,
378 IndexT *new_row_buffer)
379 {
380 for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x; i < A2_size1; i += blockDim.x * gridDim.x)
381 {
382 unsigned int index_start = new_row_buffer[i];
383 unsigned int index_stop = new_row_buffer[i+1];
384
385 A2_row_indices[i] = index_start;
386
387 for (IndexT j = index_start; j < index_stop; ++j)
388 {
389 A2_col_indices[j] = j;
390 A2_elements[j] = NumericT(1);
391 }
392 }
393
394 // write last entry in row_buffer with global thread 0:
395 if (threadIdx.x == 0 && blockIdx.x == 0)
396 A2_row_indices[A2_size1] = new_row_buffer[A2_size1];
397 }
398
399 template<typename IndexT, typename NumericT>
400 __global__ void compressed_matrix_gemm_G1(
401 IndexT * G1_row_indices,
402 IndexT * G1_col_indices,
403 NumericT * G1_elements,
404 IndexT G1_size1,
405 IndexT const *A_row_indices,
406 IndexT const *A_col_indices,
407 NumericT const *A_elements,
408 IndexT A_size1,
409 IndexT A_nnz,
410 IndexT max_per_row,
411 IndexT *new_row_buffer)
412 {
413 // Part 1: Copy column indices and entries:
414 for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x; i < A_nnz; i += blockDim.x * gridDim.x)
415 {
416 G1_col_indices[i] = A_col_indices[i];
417 G1_elements[i] = A_elements[i];
418 }
419
420 // Part 2: Derive new row indicies:
421 for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x; i < A_size1; i += blockDim.x * gridDim.x)
422 {
423 unsigned int old_start = A_row_indices[i];
424 unsigned int new_start = new_row_buffer[i];
425 unsigned int row_chunks = new_row_buffer[i+1] - new_start;
426
427 for (IndexT j=0; j<row_chunks; ++j)
428 G1_row_indices[new_start + j] = old_start + j * max_per_row;
429 }
430
431 // write last entry in row_buffer with global thread 0:
432 if (threadIdx.x == 0 && blockIdx.x == 0)
433 G1_row_indices[G1_size1] = A_row_indices[A_size1];
434 }
435
436
437
447 template<class NumericT, unsigned int AlignmentV>
448 void prod_impl(viennacl::compressed_matrix<NumericT, AlignmentV> const & A,
449 viennacl::compressed_matrix<NumericT, AlignmentV> const & B,
450 viennacl::compressed_matrix<NumericT, AlignmentV> & C)
451 {
452 C.resize(A.size1(), B.size2(), false);
453
454 unsigned int blocknum = 256;
455 unsigned int threadnum = 128;
456
457 viennacl::vector<unsigned int> subwarp_sizes(blocknum, viennacl::traits::context(A)); // upper bound for the nonzeros per row encountered for each work group
458 viennacl::vector<unsigned int> max_nnz_row_A(blocknum, viennacl::traits::context(A)); // upper bound for the nonzeros per row encountered for each work group
459 viennacl::vector<unsigned int> max_nnz_row_B(blocknum, viennacl::traits::context(A)); // upper bound for the nonzeros per row encountered for each work group
460
461 //
462 // Stage 1: Determine upper bound for number of nonzeros
463 //
464 compressed_matrix_gemm_stage_1<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()),
465 viennacl::cuda_arg<unsigned int>(A.handle2()),
466 static_cast<unsigned int>(A.size1()),
467 viennacl::cuda_arg<unsigned int>(B.handle1()),
468 viennacl::cuda_arg(subwarp_sizes),
469 viennacl::cuda_arg(max_nnz_row_A),
470 viennacl::cuda_arg(max_nnz_row_B)
471 );
472 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_1");
473
474 subwarp_sizes.switch_memory_context(viennacl::context(MAIN_MEMORY));
475 unsigned int * subwarp_sizes_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(subwarp_sizes.handle());
476
477 max_nnz_row_A.switch_memory_context(viennacl::context(MAIN_MEMORY));
478 unsigned int const * max_nnz_row_A_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(max_nnz_row_A.handle());
479
480 max_nnz_row_B.switch_memory_context(viennacl::context(MAIN_MEMORY));
481 unsigned int const * max_nnz_row_B_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(max_nnz_row_B.handle());
482
483 unsigned int max_subwarp_size = 0;
484 //std::cout << "Scratchpad offsets: " << std::endl;
485 for (std::size_t i=0; i<subwarp_sizes.size(); ++i)
486 max_subwarp_size = std::max(max_subwarp_size, subwarp_sizes_ptr[i]);
487 unsigned int A_max_nnz_per_row = 0;
488 for (std::size_t i=0; i<max_nnz_row_A.size(); ++i)
489 A_max_nnz_per_row = std::max(A_max_nnz_per_row, max_nnz_row_A_ptr[i]);
490
491 if (max_subwarp_size > 32)
492 {
493 // determine augmented size:
494 unsigned int max_entries_in_G = 32;
495 if (A_max_nnz_per_row <= 256)
496 max_entries_in_G = 16;
497 if (A_max_nnz_per_row <= 64)
498 max_entries_in_G = 8;
499
500 viennacl::vector<unsigned int> exclusive_scan_helper(A.size1() + 1, viennacl::traits::context(A));
501 compressed_matrix_gemm_decompose_1<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()),
502 static_cast<unsigned int>(A.size1()),
503 static_cast<unsigned int>(max_entries_in_G),
504 viennacl::cuda_arg(exclusive_scan_helper)
505 );
506 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_decompose_1");
507
508 viennacl::linalg::exclusive_scan(exclusive_scan_helper);
509 unsigned int augmented_size = exclusive_scan_helper[A.size1()];
510
511 // split A = A2 * G1
512 viennacl::compressed_matrix<NumericT, AlignmentV> A2(A.size1(), augmented_size, augmented_size, viennacl::traits::context(A));
513 viennacl::compressed_matrix<NumericT, AlignmentV> G1(augmented_size, A.size2(), A.nnz(), viennacl::traits::context(A));
514
515 // fill A2:
516 compressed_matrix_gemm_A2<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A2.handle1()),
517 viennacl::cuda_arg<unsigned int>(A2.handle2()),
518 viennacl::cuda_arg<NumericT>(A2.handle()),
519 static_cast<unsigned int>(A2.size1()),
520 viennacl::cuda_arg(exclusive_scan_helper)
521 );
522 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_A2");
523
524 // fill G1:
525 compressed_matrix_gemm_G1<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(G1.handle1()),
526 viennacl::cuda_arg<unsigned int>(G1.handle2()),
527 viennacl::cuda_arg<NumericT>(G1.handle()),
528 static_cast<unsigned int>(G1.size1()),
529 viennacl::cuda_arg<unsigned int>(A.handle1()),
530 viennacl::cuda_arg<unsigned int>(A.handle2()),
531 viennacl::cuda_arg<NumericT>(A.handle()),
532 static_cast<unsigned int>(A.size1()),
533 static_cast<unsigned int>(A.nnz()),
534 static_cast<unsigned int>(max_entries_in_G),
535 viennacl::cuda_arg(exclusive_scan_helper)
536 );
537 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_G1");
538
539 // compute tmp = G1 * B;
540 // C = A2 * tmp;
541 viennacl::compressed_matrix<NumericT, AlignmentV> tmp(G1.size1(), B.size2(), 0, viennacl::traits::context(A));
542 prod_impl(G1, B, tmp); // this runs a standard RMerge without decomposition of G1
543 prod_impl(A2, tmp, C); // this may split A2 again
544 return;
545 }
546
547 //std::cout << "Running RMerge with subwarp size " << max_subwarp_size << std::endl;
548
549 subwarp_sizes.switch_memory_context(viennacl::traits::context(A));
550 max_nnz_row_A.switch_memory_context(viennacl::traits::context(A));
551 max_nnz_row_B.switch_memory_context(viennacl::traits::context(A));
552
553 //
554 // Stage 2: Determine pattern of C
555 //
556
557 if (max_subwarp_size == 32)
558 {
559 compressed_matrix_gemm_stage_2<32><<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()),
560 viennacl::cuda_arg<unsigned int>(A.handle2()),
561 static_cast<unsigned int>(A.size1()),
562 viennacl::cuda_arg<unsigned int>(B.handle1()),
563 viennacl::cuda_arg<unsigned int>(B.handle2()),
564 static_cast<unsigned int>(B.size2()),
565 viennacl::cuda_arg<unsigned int>(C.handle1())
566 );
567 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_2");
568 }
569 else if (max_subwarp_size == 16)
570 {
571 compressed_matrix_gemm_stage_2<16><<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()),
572 viennacl::cuda_arg<unsigned int>(A.handle2()),
573 static_cast<unsigned int>(A.size1()),
574 viennacl::cuda_arg<unsigned int>(B.handle1()),
575 viennacl::cuda_arg<unsigned int>(B.handle2()),
576 static_cast<unsigned int>(B.size2()),
577 viennacl::cuda_arg<unsigned int>(C.handle1())
578 );
579 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_2");
580 }
581 else
582 {
583 compressed_matrix_gemm_stage_2<8><<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()),
584 viennacl::cuda_arg<unsigned int>(A.handle2()),
585 static_cast<unsigned int>(A.size1()),
586 viennacl::cuda_arg<unsigned int>(B.handle1()),
587 viennacl::cuda_arg<unsigned int>(B.handle2()),
588 static_cast<unsigned int>(B.size2()),
589 viennacl::cuda_arg<unsigned int>(C.handle1())
590 );
591 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_2");
592 }
593
594 // exclusive scan on C.handle1(), ultimately allowing to allocate remaining memory for C
595 viennacl::backend::typesafe_host_array<unsigned int> row_buffer(C.handle1(), C.size1() + 1);
596 viennacl::backend::memory_read(C.handle1(), 0, row_buffer.raw_size(), row_buffer.get());
597 unsigned int current_offset = 0;
598 for (std::size_t i=0; i<C.size1(); ++i)
599 {
600 unsigned int tmp = row_buffer[i];
601 row_buffer.set(i, current_offset);
602 current_offset += tmp;
603 }
604 row_buffer.set(C.size1(), current_offset);
605 viennacl::backend::memory_write(C.handle1(), 0, row_buffer.raw_size(), row_buffer.get());
606
607
608 //
609 // Stage 3: Compute entries in C
610 //
611 C.reserve(current_offset, false);
612
613 if (max_subwarp_size == 32)
614 {
615 compressed_matrix_gemm_stage_3<32><<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()),
616 viennacl::cuda_arg<unsigned int>(A.handle2()),
617 viennacl::cuda_arg<NumericT>(A.handle()),
618 static_cast<unsigned int>(A.size1()),
619 viennacl::cuda_arg<unsigned int>(B.handle1()),
620 viennacl::cuda_arg<unsigned int>(B.handle2()),
621 viennacl::cuda_arg<NumericT>(B.handle()),
622 static_cast<unsigned int>(B.size2()),
623 viennacl::cuda_arg<unsigned int>(C.handle1()),
624 viennacl::cuda_arg<unsigned int>(C.handle2()),
625 viennacl::cuda_arg<NumericT>(C.handle())
626 );
627 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_3");
628 }
629 else if (max_subwarp_size == 16)
630 {
631 compressed_matrix_gemm_stage_3<16><<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()),
632 viennacl::cuda_arg<unsigned int>(A.handle2()),
633 viennacl::cuda_arg<NumericT>(A.handle()),
634 static_cast<unsigned int>(A.size1()),
635 viennacl::cuda_arg<unsigned int>(B.handle1()),
636 viennacl::cuda_arg<unsigned int>(B.handle2()),
637 viennacl::cuda_arg<NumericT>(B.handle()),
638 static_cast<unsigned int>(B.size2()),
639 viennacl::cuda_arg<unsigned int>(C.handle1()),
640 viennacl::cuda_arg<unsigned int>(C.handle2()),
641 viennacl::cuda_arg<NumericT>(C.handle())
642 );
643 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_3");
644 }
645 else
646 {
647 compressed_matrix_gemm_stage_3<8><<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()),
648 viennacl::cuda_arg<unsigned int>(A.handle2()),
649 viennacl::cuda_arg<NumericT>(A.handle()),
650 static_cast<unsigned int>(A.size1()),
651 viennacl::cuda_arg<unsigned int>(B.handle1()),
652 viennacl::cuda_arg<unsigned int>(B.handle2()),
653 viennacl::cuda_arg<NumericT>(B.handle()),
654 static_cast<unsigned int>(B.size2()),
655 viennacl::cuda_arg<unsigned int>(C.handle1()),
656 viennacl::cuda_arg<unsigned int>(C.handle2()),
657 viennacl::cuda_arg<NumericT>(C.handle())
658 );
659 VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_3");
660 }
661
662 }
663
664 } // namespace cuda
665 } //namespace linalg
666 } //namespace viennacl
667
668
669 #endif
viennacl::compressed_matrix::size2
const vcl_size_t & size2() const
Returns the number of columns.
Definition: compressed_matrix.hpp:929
viennacl::backend::typesafe_host_array
Helper class implementing an array on the host. Default case: No conversion necessary.
Definition: util.hpp:92
viennacl::backend::memory_write
void memory_write(mem_handle &dst_buffer, vcl_size_t dst_offset, vcl_size_t bytes_to_write, const void *ptr, bool async=false)
Writes data from main RAM identified by 'ptr' to the buffer identified by 'dst_buffer'.
Definition: memory.hpp:220
viennacl::linalg::cuda::subwarp_minimum_shared
__device__ IndexT subwarp_minimum_shared(IndexT min_index, IndexT id_in_warp, IndexT *shared_buffer)
Definition: spgemm_rmerge.hpp:152
viennacl::compressed_matrix::size1
const vcl_size_t & size1() const
Returns the number of rows.
Definition: compressed_matrix.hpp:927
viennacl::linalg::cuda::compressed_matrix_gemm_decompose_1
__global__ void compressed_matrix_gemm_decompose_1(const IndexT *A_row_indices, IndexT A_size1, IndexT max_per_row, IndexT *chunks_per_row)
Definition: spgemm.hpp:469
tools.hpp
Various little tools used here and there in ViennaCL.
viennacl::traits::stride
result_of::size_type< viennacl::vector_base< T > >::type stride(viennacl::vector_base< T > const &s)
Definition: stride.hpp:45
forwards.h
This file provides the forward declarations for the main types used within ViennaCL.
viennacl::backend::memory_read
void memory_read(mem_handle const &src_buffer, vcl_size_t src_offset, vcl_size_t bytes_to_read, void *ptr, bool async=false)
Reads data from a buffer back to main RAM.
Definition: memory.hpp:261
viennacl::linalg::detail::max
T max(const T &lhs, const T &rhs)
Maximum.
Definition: util.hpp:59
viennacl::linalg::cuda::prod_impl
void prod_impl(const matrix_base< NumericT > &mat, bool mat_transpose, const vector_base< NumericT > &vec, vector_base< NumericT > &result)
Carries out matrix-vector multiplication.
Definition: matrix_operations.hpp:1464
viennacl::compressed_matrix::handle
const handle_type & handle() const
Returns the OpenCL handle to the matrix entry array.
Definition: compressed_matrix.hpp:942
viennacl::compressed_matrix::handle1
const handle_type & handle1() const
Returns the OpenCL handle to the row index array.
Definition: compressed_matrix.hpp:936
viennacl::compressed_matrix::nnz
const vcl_size_t & nnz() const
Returns the number of nonzero entries.
Definition: compressed_matrix.hpp:931
viennacl::linalg::cuda::compressed_matrix_gemm_stage_3
__global__ void compressed_matrix_gemm_stage_3(const IndexT *A_row_indices, const IndexT *A_col_indices, const NumericT *A_elements, IndexT A_size1, const IndexT *B_row_indices, const IndexT *B_col_indices, const NumericT *B_elements, IndexT B_size2, IndexT const *C_row_indices, IndexT *C_col_indices, NumericT *C_elements, unsigned int *subwarpsize_array, unsigned int *max_row_size_A, unsigned int *max_row_size_B, unsigned int *scratchpad_offsets, unsigned int *scratchpad_indices, NumericT *scratchpad_values)
Definition: spgemm.hpp:365
NumericT
float NumericT
Definition: bisect.cpp:40
viennacl::context
Represents a generic 'context' similar to an OpenCL context, but is backend-agnostic and thus also su...
Definition: context.hpp:39
viennacl::linalg::cuda::round_to_next_power_of_2
__device__ IndexT round_to_next_power_of_2(IndexT val)
Definition: spgemm.hpp:63
viennacl::linalg::cuda::compressed_matrix_gemm_stage_1
__global__ void compressed_matrix_gemm_stage_1(const IndexT *A_row_indices, const IndexT *A_col_indices, IndexT A_size1, const IndexT *B_row_indices, IndexT *subwarpsize_per_group, IndexT *max_nnz_row_A_per_group, IndexT *max_nnz_row_B_per_group)
Definition: spgemm.hpp:82
viennacl::linalg::cuda::subwarp_minimum_shuffle
__device__ IndexT subwarp_minimum_shuffle(IndexT min_index)
Definition: spgemm_rmerge.hpp:143
viennacl::linalg::cuda::compressed_matrix_gemm_A2
__global__ void compressed_matrix_gemm_A2(IndexT *A2_row_indices, IndexT *A2_col_indices, NumericT *A2_elements, IndexT A2_size1, IndexT *new_row_buffer)
Definition: spgemm.hpp:484
viennacl::linalg::cuda::subwarp_accumulate_shared
__device__ NumericT subwarp_accumulate_shared(NumericT output_value, unsigned int id_in_warp, NumericT *shared_buffer)
Definition: spgemm_rmerge.hpp:241
viennacl::compressed_matrix::handle2
const handle_type & handle2() const
Returns the OpenCL handle to the column index array.
Definition: compressed_matrix.hpp:938
viennacl::vector< unsigned int >
viennacl::row
vector_expression< const matrix_base< NumericT, F >, const unsigned int, op_row > row(const matrix_base< NumericT, F > &A, unsigned int i)
Definition: matrix.hpp:910
sparse_matrix_operations_solve.hpp
Implementations of direct triangular solvers for sparse matrices using CUDA.
viennacl::MAIN_MEMORY
Definition: forwards.h:348
viennacl::compressed_matrix::reserve
void reserve(vcl_size_t new_nonzeros, bool preserve=true)
Allocate memory for the supplied number of nonzeros in the matrix. Old values are preserved...
Definition: compressed_matrix.hpp:794
timer.hpp
A simple, yet (mostly) sufficiently accurate timer for benchmarking and profiling.
viennacl::traits::context
viennacl::context context(T const &t)
Returns an ID for the currently active memory domain of an object.
Definition: context.hpp:40
common.hpp
Common routines for CUDA execution.
vector.hpp
The vector type with operator-overloads and proxy classes is defined here. Linear algebra operations ...
viennacl::linalg::max
NumericT max(std::vector< NumericT > const &v1)
Definition: maxmin.hpp:47
viennacl::backend::typesafe_host_array::set
void set(vcl_size_t index, U value)
Definition: util.hpp:115
viennacl::linalg::cuda::compressed_matrix_gemm_G1
__global__ void compressed_matrix_gemm_G1(IndexT *G1_row_indices, IndexT *G1_col_indices, NumericT *G1_elements, IndexT G1_size1, IndexT const *A_row_indices, IndexT const *A_col_indices, NumericT const *A_elements, IndexT A_size1, IndexT A_nnz, IndexT max_per_row, IndexT *new_row_buffer)
Definition: spgemm.hpp:511
viennacl::backend::mem_handle::raw_size
vcl_size_t raw_size() const
Returns the number of bytes of the currently active buffer.
Definition: mem_handle.hpp:230
viennacl::compressed_matrix
A sparse square matrix in compressed sparse rows format.
Definition: compressed_matrix.hpp:559
viennacl::linalg::exclusive_scan
void exclusive_scan(vector_base< NumericT > &vec1, vector_base< NumericT > &vec2)
This function implements an exclusive scan.
Definition: vector_operations.hpp:1240
VIENNACL_CUDA_LAST_ERROR_CHECK
#define VIENNACL_CUDA_LAST_ERROR_CHECK(message)
Definition: common.hpp:30
viennacl::cuda_arg
NumericT * cuda_arg(scalar< NumericT > &obj)
Convenience helper function for extracting the CUDA handle from a ViennaCL scalar. Non-const version.
Definition: common.hpp:39
scalar.hpp
Implementation of the ViennaCL scalar class.
viennacl::compressed_matrix::resize
void resize(vcl_size_t new_size1, vcl_size_t new_size2, bool preserve=true)
Resize the matrix.
Definition: compressed_matrix.hpp:829
viennacl::linalg::cuda::subwarp_accumulate_shuffle
__device__ NumericT subwarp_accumulate_shuffle(NumericT output_value)
Definition: spgemm_rmerge.hpp:232
viennacl::linalg::cuda::compressed_matrix_gemm_stage_2
__global__ void compressed_matrix_gemm_stage_2(const IndexT *A_row_indices, const IndexT *A_col_indices, IndexT A_size1, const IndexT *B_row_indices, const IndexT *B_col_indices, IndexT B_size2, IndexT *C_row_indices, unsigned int *subwarpsize_array, unsigned int *max_row_size_A, unsigned int *max_row_size_B, unsigned int *scratchpad_offsets, unsigned int *scratchpad_indices)
Definition: spgemm.hpp:217
viennacl::linalg::min
NumericT min(std::vector< NumericT > const &v1)
Definition: maxmin.hpp:91