cholesky_implicit.c 9.8 KB

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  1. /* StarPU --- Runtime system for heterogeneous multicore architectures.
  2. *
  3. * Copyright (C) 2009-2020 Université de Bordeaux, CNRS (LaBRI UMR 5800), Inria
  4. * Copyright (C) 2010 Mehdi Juhoor
  5. * Copyright (C) 2013 Thibaut Lambert
  6. *
  7. * StarPU is free software; you can redistribute it and/or modify
  8. * it under the terms of the GNU Lesser General Public License as published by
  9. * the Free Software Foundation; either version 2.1 of the License, or (at
  10. * your option) any later version.
  11. *
  12. * StarPU is distributed in the hope that it will be useful, but
  13. * WITHOUT ANY WARRANTY; without even the implied warranty of
  14. * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
  15. *
  16. * See the GNU Lesser General Public License in COPYING.LGPL for more details.
  17. */
  18. /*
  19. * This version of the Cholesky factorization uses implicit dependency computation.
  20. * The whole algorithm thus appears clearly in the task submission loop in _cholesky().
  21. */
  22. /* Note: this is using fortran ordering, i.e. column-major ordering, i.e.
  23. * elements with consecutive row number are consecutive in memory */
  24. #include "cholesky.h"
  25. #include "../sched_ctx_utils/sched_ctx_utils.h"
  26. #if defined(STARPU_USE_CUDA) && defined(STARPU_HAVE_MAGMA)
  27. #include "magma.h"
  28. #endif
  29. /*
  30. * code to bootstrap the factorization
  31. * and construct the DAG
  32. */
  33. static void callback_turn_spmd_on(void *arg)
  34. {
  35. (void)arg;
  36. cl22.type = STARPU_SPMD;
  37. }
  38. static int _cholesky(starpu_data_handle_t dataA, unsigned nblocks)
  39. {
  40. double start;
  41. double end;
  42. unsigned k,m,n;
  43. unsigned long nx = starpu_matrix_get_nx(dataA);
  44. unsigned long nn = nx/nblocks;
  45. unsigned unbound_prio = STARPU_MAX_PRIO == INT_MAX && STARPU_MIN_PRIO == INT_MIN;
  46. if (bound_p || bound_lp_p || bound_mps_p)
  47. starpu_bound_start(bound_deps_p, 0);
  48. starpu_fxt_start_profiling();
  49. start = starpu_timing_now();
  50. /* create all the DAG nodes */
  51. for (k = 0; k < nblocks; k++)
  52. {
  53. int ret;
  54. starpu_iteration_push(k);
  55. starpu_data_handle_t sdatakk = starpu_data_get_sub_data(dataA, 2, k, k);
  56. ret = starpu_task_insert(&cl11,
  57. STARPU_PRIORITY, noprio_p ? STARPU_DEFAULT_PRIO : unbound_prio ? (int)(2*nblocks - 2*k) : STARPU_MAX_PRIO,
  58. STARPU_RW, sdatakk,
  59. STARPU_CALLBACK, (k == 3*nblocks/4)?callback_turn_spmd_on:NULL,
  60. STARPU_FLOPS, (double) FLOPS_SPOTRF(nn),
  61. STARPU_TAG_ONLY, TAG11(k),
  62. 0);
  63. if (ret == -ENODEV) return 77;
  64. STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
  65. for (m = k+1; m<nblocks; m++)
  66. {
  67. starpu_data_handle_t sdatamk = starpu_data_get_sub_data(dataA, 2, m, k);
  68. ret = starpu_task_insert(&cl21,
  69. STARPU_PRIORITY, noprio_p ? STARPU_DEFAULT_PRIO : unbound_prio ? (int)(2*nblocks - 2*k - m) : (m == k+1)?STARPU_MAX_PRIO:STARPU_DEFAULT_PRIO,
  70. STARPU_R, sdatakk,
  71. STARPU_RW, sdatamk,
  72. STARPU_FLOPS, (double) FLOPS_STRSM(nn, nn),
  73. STARPU_TAG_ONLY, TAG21(m,k),
  74. 0);
  75. if (ret == -ENODEV) return 77;
  76. STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
  77. }
  78. starpu_data_wont_use(sdatakk);
  79. for (m = k+1; m<nblocks; m++)
  80. {
  81. starpu_data_handle_t sdatamk = starpu_data_get_sub_data(dataA, 2, m, k);
  82. for (n = k+1; n<nblocks; n++)
  83. {
  84. if (n <= m)
  85. {
  86. starpu_data_handle_t sdatank = starpu_data_get_sub_data(dataA, 2, n, k);
  87. starpu_data_handle_t sdatamn = starpu_data_get_sub_data(dataA, 2, m, n);
  88. ret = starpu_task_insert(&cl22,
  89. STARPU_PRIORITY, noprio_p ? STARPU_DEFAULT_PRIO : unbound_prio ? (int)(2*nblocks - 2*k - m - n) : ((n == k+1) && (m == k+1))?STARPU_MAX_PRIO:STARPU_DEFAULT_PRIO,
  90. STARPU_R, sdatamk,
  91. STARPU_R, sdatank,
  92. cl22.modes[2], sdatamn,
  93. STARPU_FLOPS, (double) FLOPS_SGEMM(nn, nn, nn),
  94. STARPU_TAG_ONLY, TAG22(k,m,n),
  95. 0);
  96. if (ret == -ENODEV) return 77;
  97. STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
  98. }
  99. }
  100. starpu_data_wont_use(sdatamk);
  101. }
  102. starpu_iteration_pop();
  103. }
  104. starpu_task_wait_for_all();
  105. end = starpu_timing_now();
  106. starpu_fxt_stop_profiling();
  107. if (bound_p || bound_lp_p || bound_mps_p)
  108. starpu_bound_stop();
  109. double timing = end - start;
  110. double flop = FLOPS_SPOTRF(nx);
  111. if(with_ctxs_p || with_noctxs_p || chole1_p || chole2_p)
  112. update_sched_ctx_timing_results((flop/timing/1000.0f), (timing/1000000.0f));
  113. else
  114. {
  115. PRINTF("# size\tms\tGFlops");
  116. if (bound_p)
  117. PRINTF("\tTms\tTGFlops");
  118. PRINTF("\n");
  119. PRINTF("%lu\t%.0f\t%.1f", nx, timing/1000, (flop/timing/1000.0f));
  120. if (bound_lp_p)
  121. {
  122. FILE *f = fopen("cholesky.lp", "w");
  123. starpu_bound_print_lp(f);
  124. fclose(f);
  125. }
  126. if (bound_mps_p)
  127. {
  128. FILE *f = fopen("cholesky.mps", "w");
  129. starpu_bound_print_mps(f);
  130. fclose(f);
  131. }
  132. if (bound_p)
  133. {
  134. double res;
  135. starpu_bound_compute(&res, NULL, 0);
  136. PRINTF("\t%.0f\t%.1f", res, (flop/res/1000000.0f));
  137. }
  138. PRINTF("\n");
  139. }
  140. return 0;
  141. }
  142. static int cholesky(float *matA, unsigned size, unsigned ld, unsigned nblocks)
  143. {
  144. starpu_data_handle_t dataA;
  145. unsigned m, n;
  146. /* monitor and partition the A matrix into blocks :
  147. * one block is now determined by 2 unsigned (m,n) */
  148. starpu_matrix_data_register(&dataA, STARPU_MAIN_RAM, (uintptr_t)matA, ld, size, size, sizeof(float));
  149. /* Split into blocks of complete rows first */
  150. struct starpu_data_filter f =
  151. {
  152. .filter_func = starpu_matrix_filter_block,
  153. .nchildren = nblocks
  154. };
  155. /* Then split rows into tiles */
  156. struct starpu_data_filter f2 =
  157. {
  158. /* Note: here "vertical" is for row-major, we are here using column-major. */
  159. .filter_func = starpu_matrix_filter_vertical_block,
  160. .nchildren = nblocks
  161. };
  162. starpu_data_map_filters(dataA, 2, &f, &f2);
  163. for (m = 0; m < nblocks; m++)
  164. for (n = 0; n < nblocks; n++)
  165. {
  166. starpu_data_handle_t data = starpu_data_get_sub_data(dataA, 2, m, n);
  167. starpu_data_set_coordinates(data, 2, m, n);
  168. }
  169. int ret = _cholesky(dataA, nblocks);
  170. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  171. starpu_data_unregister(dataA);
  172. return ret;
  173. }
  174. static void execute_cholesky(unsigned size, unsigned nblocks)
  175. {
  176. float *mat = NULL;
  177. #ifndef STARPU_SIMGRID
  178. unsigned m,n;
  179. starpu_malloc_flags((void **)&mat, (size_t)size*size*sizeof(float), STARPU_MALLOC_PINNED|STARPU_MALLOC_SIMULATION_FOLDED);
  180. for (n = 0; n < size; n++)
  181. {
  182. for (m = 0; m < size; m++)
  183. {
  184. mat[m +n*size] = (1.0f/(1.0f+m+n)) + ((m == n)?1.0f*size:0.0f);
  185. /* mat[m +n*size] = ((m == n)?1.0f*size:0.0f); */
  186. }
  187. }
  188. /* #define PRINT_OUTPUT */
  189. #ifdef PRINT_OUTPUT
  190. FPRINTF(stdout, "Input :\n");
  191. for (m = 0; m < size; m++)
  192. {
  193. for (n = 0; n < size; n++)
  194. {
  195. if (n <= m)
  196. {
  197. FPRINTF(stdout, "%2.2f\t", mat[m +n*size]);
  198. }
  199. else
  200. {
  201. FPRINTF(stdout, ".\t");
  202. }
  203. }
  204. FPRINTF(stdout, "\n");
  205. }
  206. #endif
  207. #endif
  208. cholesky(mat, size, size, nblocks);
  209. #ifndef STARPU_SIMGRID
  210. #ifdef PRINT_OUTPUT
  211. FPRINTF(stdout, "Results :\n");
  212. for (m = 0; m < size; m++)
  213. {
  214. for (n = 0; n < size; n++)
  215. {
  216. if (n <= m)
  217. {
  218. FPRINTF(stdout, "%2.2f\t", mat[m +n*size]);
  219. }
  220. else
  221. {
  222. FPRINTF(stdout, ".\t");
  223. }
  224. }
  225. FPRINTF(stdout, "\n");
  226. }
  227. #endif
  228. if (check_p)
  229. {
  230. FPRINTF(stderr, "compute explicit LLt ...\n");
  231. for (m = 0; m < size; m++)
  232. {
  233. for (n = 0; n < size; n++)
  234. {
  235. if (n > m)
  236. {
  237. mat[m+n*size] = 0.0f; /* debug */
  238. }
  239. }
  240. }
  241. float *test_mat = malloc(size*size*sizeof(float));
  242. STARPU_ASSERT(test_mat);
  243. STARPU_SSYRK("L", "N", size, size, 1.0f,
  244. mat, size, 0.0f, test_mat, size);
  245. FPRINTF(stderr, "comparing results ...\n");
  246. #ifdef PRINT_OUTPUT
  247. for (m = 0; m < size; m++)
  248. {
  249. for (n = 0; n < size; n++)
  250. {
  251. if (n <= m)
  252. {
  253. FPRINTF(stdout, "%2.2f\t", test_mat[m +n*size]);
  254. }
  255. else
  256. {
  257. FPRINTF(stdout, ".\t");
  258. }
  259. }
  260. FPRINTF(stdout, "\n");
  261. }
  262. #endif
  263. for (m = 0; m < size; m++)
  264. {
  265. for (n = 0; n < size; n++)
  266. {
  267. if (n <= m)
  268. {
  269. float orig = (1.0f/(1.0f+m+n)) + ((m == n)?1.0f*size:0.0f);
  270. float err = fabsf(test_mat[m +n*size] - orig) / orig;
  271. if (err > 0.0001)
  272. {
  273. FPRINTF(stderr, "Error[%u, %u] --> %2.6f != %2.6f (err %2.6f)\n", m, n, test_mat[m +n*size], orig, err);
  274. assert(0);
  275. }
  276. }
  277. }
  278. }
  279. free(test_mat);
  280. }
  281. starpu_free_flags(mat, (size_t)size*size*sizeof(float), STARPU_MALLOC_PINNED|STARPU_MALLOC_SIMULATION_FOLDED);
  282. #endif
  283. }
  284. int main(int argc, char **argv)
  285. {
  286. /* create a simple definite positive symetric matrix example
  287. *
  288. * Hilbert matrix : h(i,j) = 1/(i+j+1)
  289. * */
  290. #ifdef STARPU_HAVE_MAGMA
  291. magma_init();
  292. #endif
  293. int ret;
  294. ret = starpu_init(NULL);
  295. if (ret == -ENODEV) return 77;
  296. STARPU_CHECK_RETURN_VALUE(ret, "starpu_init");
  297. //starpu_fxt_stop_profiling();
  298. init_sizes();
  299. parse_args(argc, argv);
  300. if(with_ctxs_p || with_noctxs_p || chole1_p || chole2_p)
  301. parse_args_ctx(argc, argv);
  302. #ifdef STARPU_USE_CUDA
  303. initialize_chol_model(&chol_model_11,"chol_model_11",cpu_chol_task_11_cost,cuda_chol_task_11_cost);
  304. initialize_chol_model(&chol_model_21,"chol_model_21",cpu_chol_task_21_cost,cuda_chol_task_21_cost);
  305. initialize_chol_model(&chol_model_22,"chol_model_22",cpu_chol_task_22_cost,cuda_chol_task_22_cost);
  306. #else
  307. initialize_chol_model(&chol_model_11,"chol_model_11",cpu_chol_task_11_cost,NULL);
  308. initialize_chol_model(&chol_model_21,"chol_model_21",cpu_chol_task_21_cost,NULL);
  309. initialize_chol_model(&chol_model_22,"chol_model_22",cpu_chol_task_22_cost,NULL);
  310. #endif
  311. starpu_cublas_init();
  312. if(with_ctxs_p)
  313. {
  314. construct_contexts();
  315. start_2benchs(execute_cholesky);
  316. }
  317. else if(with_noctxs_p)
  318. start_2benchs(execute_cholesky);
  319. else if(chole1_p)
  320. start_1stbench(execute_cholesky);
  321. else if(chole2_p)
  322. start_2ndbench(execute_cholesky);
  323. else
  324. execute_cholesky(size_p, nblocks_p);
  325. starpu_cublas_shutdown();
  326. starpu_shutdown();
  327. return 0;
  328. }