cholesky_tag.c 10 KB

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  1. /* StarPU --- Runtime system for heterogeneous multicore architectures.
  2. *
  3. * Copyright (C) 2008-2020 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 explicit dependency
  20. * declaration through dependency tags.
  21. * It also uses data partitioning to split the matrix into submatrices
  22. */
  23. /* Note: this is using fortran ordering, i.e. column-major ordering, i.e.
  24. * elements with consecutive row number are consecutive in memory */
  25. #include "cholesky.h"
  26. #include <starpu_perfmodel.h>
  27. #if defined(STARPU_USE_CUDA) && defined(STARPU_HAVE_MAGMA)
  28. #include "magma.h"
  29. #endif
  30. /*
  31. * Some useful functions
  32. */
  33. static struct starpu_task *create_task(starpu_tag_t id)
  34. {
  35. struct starpu_task *task = starpu_task_create();
  36. task->cl_arg = NULL;
  37. task->use_tag = 1;
  38. task->tag_id = id;
  39. return task;
  40. }
  41. /*
  42. * Create the codelets
  43. */
  44. static struct starpu_task * create_task_11(starpu_data_handle_t dataA, unsigned k)
  45. {
  46. /* FPRINTF(stdout, "task 11 k = %d TAG = %llx\n", k, (TAG11(k))); */
  47. struct starpu_task *task = create_task(TAG11(k));
  48. task->cl = &cl11;
  49. /* which sub-data is manipulated ? */
  50. task->handles[0] = starpu_data_get_sub_data(dataA, 2, k, k);
  51. /* this is an important task */
  52. if (!noprio_p)
  53. task->priority = STARPU_MAX_PRIO;
  54. /* enforce dependencies ... */
  55. if (k > 0)
  56. {
  57. starpu_tag_declare_deps(TAG11(k), 1, TAG22(k-1, k, k));
  58. }
  59. int n = starpu_matrix_get_nx(task->handles[0]);
  60. task->flops = FLOPS_SPOTRF(n);
  61. return task;
  62. }
  63. static int create_task_21(starpu_data_handle_t dataA, unsigned k, unsigned m)
  64. {
  65. int ret;
  66. struct starpu_task *task = create_task(TAG21(k, m));
  67. task->cl = &cl21;
  68. /* which sub-data is manipulated ? */
  69. task->handles[0] = starpu_data_get_sub_data(dataA, 2, k, k);
  70. task->handles[1] = starpu_data_get_sub_data(dataA, 2, m, k);
  71. if (!noprio_p && (m == k+1))
  72. {
  73. task->priority = STARPU_MAX_PRIO;
  74. }
  75. /* enforce dependencies ... */
  76. if (k > 0)
  77. {
  78. starpu_tag_declare_deps(TAG21(k, m), 2, TAG11(k), TAG22(k-1, m, k));
  79. }
  80. else
  81. {
  82. starpu_tag_declare_deps(TAG21(k, m), 1, TAG11(k));
  83. }
  84. int nx = starpu_matrix_get_nx(task->handles[0]);
  85. task->flops = FLOPS_STRSM(nx, nx);
  86. ret = starpu_task_submit(task);
  87. if (ret != -ENODEV) STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
  88. return ret;
  89. }
  90. static int create_task_22(starpu_data_handle_t dataA, unsigned k, unsigned m, unsigned n)
  91. {
  92. int ret;
  93. /* FPRINTF(stdout, "task 22 k,n,m = %d,%d,%d TAG = %llx\n", k,m,n, TAG22(k,m,n)); */
  94. struct starpu_task *task = create_task(TAG22(k, m, n));
  95. task->cl = &cl22;
  96. /* which sub-data is manipulated ? */
  97. task->handles[0] = starpu_data_get_sub_data(dataA, 2, n, k);
  98. task->handles[1] = starpu_data_get_sub_data(dataA, 2, m, k);
  99. task->handles[2] = starpu_data_get_sub_data(dataA, 2, m, n);
  100. if (!noprio_p && (n == k + 1) && (m == k +1) )
  101. {
  102. task->priority = STARPU_MAX_PRIO;
  103. }
  104. /* enforce dependencies ... */
  105. if (k > 0)
  106. {
  107. starpu_tag_declare_deps(TAG22(k, m, n), 3, TAG22(k-1, m, n), TAG21(k, n), TAG21(k, m));
  108. }
  109. else
  110. {
  111. starpu_tag_declare_deps(TAG22(k, m, n), 2, TAG21(k, n), TAG21(k, m));
  112. }
  113. int nx = starpu_matrix_get_nx(task->handles[0]);
  114. task->flops = FLOPS_SGEMM(nx, nx, nx);
  115. ret = starpu_task_submit(task);
  116. if (ret != -ENODEV) STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
  117. return ret;
  118. }
  119. /*
  120. * code to bootstrap the factorization
  121. * and construct the DAG
  122. */
  123. static int _cholesky(starpu_data_handle_t dataA, unsigned nblocks)
  124. {
  125. int ret;
  126. double start;
  127. double end;
  128. struct starpu_task *entry_task = NULL;
  129. /* create all the DAG nodes */
  130. unsigned k, m, n;
  131. start = starpu_timing_now();
  132. for (k = 0; k < nblocks; k++)
  133. {
  134. starpu_iteration_push(k);
  135. struct starpu_task *task = create_task_11(dataA, k);
  136. /* we defer the launch of the first task */
  137. if (k == 0)
  138. {
  139. entry_task = task;
  140. }
  141. else
  142. {
  143. ret = starpu_task_submit(task);
  144. if (ret == -ENODEV)
  145. {
  146. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  147. return 77;
  148. }
  149. STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
  150. }
  151. for (m = k+1; m<nblocks; m++)
  152. {
  153. ret = create_task_21(dataA, k, m);
  154. if (ret == -ENODEV)
  155. {
  156. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  157. return 77;
  158. }
  159. for (n = k+1; n<nblocks; n++)
  160. {
  161. if (n <= m)
  162. {
  163. ret = create_task_22(dataA, k, m, n);
  164. if (ret == -ENODEV)
  165. {
  166. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  167. return 77;
  168. }
  169. }
  170. }
  171. }
  172. starpu_iteration_pop();
  173. }
  174. /* schedule the codelet */
  175. ret = starpu_task_submit(entry_task);
  176. if (ret == -ENODEV) return 77;
  177. STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
  178. /* stall the application until the end of computations */
  179. starpu_tag_wait(TAG11(nblocks-1));
  180. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  181. end = starpu_timing_now();
  182. double timing = end - start;
  183. unsigned nx = starpu_matrix_get_nx(dataA);
  184. double flop = (1.0f*nx*nx*nx)/3.0f;
  185. PRINTF("# size\tms\tGFlops\n");
  186. PRINTF("%u\t%.0f\t%.1f\n", nx, timing/1000, (flop/timing/1000.0f));
  187. return 0;
  188. }
  189. static int initialize_system(int argc, char **argv, float **A, unsigned pinned)
  190. {
  191. int ret;
  192. int flags = STARPU_MALLOC_SIMULATION_FOLDED;
  193. #ifdef STARPU_HAVE_MAGMA
  194. magma_init();
  195. #endif
  196. ret = starpu_init(NULL);
  197. if (ret == -ENODEV)
  198. return 77;
  199. STARPU_CHECK_RETURN_VALUE(ret, "starpu_init");
  200. init_sizes();
  201. parse_args(argc, argv);
  202. #ifdef STARPU_USE_CUDA
  203. initialize_chol_model(&chol_model_11,"chol_model_11",cpu_chol_task_11_cost,cuda_chol_task_11_cost);
  204. initialize_chol_model(&chol_model_21,"chol_model_21",cpu_chol_task_21_cost,cuda_chol_task_21_cost);
  205. initialize_chol_model(&chol_model_22,"chol_model_22",cpu_chol_task_22_cost,cuda_chol_task_22_cost);
  206. #else
  207. initialize_chol_model(&chol_model_11,"chol_model_11",cpu_chol_task_11_cost,NULL);
  208. initialize_chol_model(&chol_model_21,"chol_model_21",cpu_chol_task_21_cost,NULL);
  209. initialize_chol_model(&chol_model_22,"chol_model_22",cpu_chol_task_22_cost,NULL);
  210. #endif
  211. starpu_cublas_init();
  212. if (pinned)
  213. flags |= STARPU_MALLOC_PINNED;
  214. starpu_malloc_flags((void **)A, size_p*size_p*sizeof(float), flags);
  215. return 0;
  216. }
  217. static int cholesky(float *matA, unsigned size, unsigned ld, unsigned nblocks)
  218. {
  219. starpu_data_handle_t dataA;
  220. int ret;
  221. /* monitor and partition the A matrix into blocks :
  222. * one block is now determined by 2 unsigned (m,n) */
  223. starpu_matrix_data_register(&dataA, STARPU_MAIN_RAM, (uintptr_t)matA, ld, size, size, sizeof(float));
  224. starpu_data_set_sequential_consistency_flag(dataA, 0);
  225. /* Split into blocks of complete rows first */
  226. struct starpu_data_filter f =
  227. {
  228. .filter_func = starpu_matrix_filter_block,
  229. .nchildren = nblocks
  230. };
  231. /* Then split rows into tiles */
  232. struct starpu_data_filter f2 =
  233. {
  234. /* Note: here "vertical" is for row-major, we are here using column-major. */
  235. .filter_func = starpu_matrix_filter_vertical_block,
  236. .nchildren = nblocks
  237. };
  238. starpu_data_map_filters(dataA, 2, &f, &f2);
  239. ret = _cholesky(dataA, nblocks);
  240. starpu_data_unregister(dataA);
  241. return ret;
  242. }
  243. static void shutdown_system(float **matA, unsigned dim, unsigned pinned)
  244. {
  245. int flags = STARPU_MALLOC_SIMULATION_FOLDED;
  246. if (pinned)
  247. flags |= STARPU_MALLOC_PINNED;
  248. starpu_free_flags(*matA, dim*dim*sizeof(float), flags);
  249. starpu_cublas_shutdown();
  250. starpu_shutdown();
  251. }
  252. int main(int argc, char **argv)
  253. {
  254. /* create a simple definite positive symetric matrix example
  255. *
  256. * Hilbert matrix : h(i,j) = 1/(i+j+1)
  257. * */
  258. float *mat = NULL;
  259. int ret = initialize_system(argc, argv, &mat, pinned_p);
  260. if (ret) return ret;
  261. #ifndef STARPU_SIMGRID
  262. unsigned m,n;
  263. for (n = 0; n < size_p; n++)
  264. {
  265. for (m = 0; m < size_p; m++)
  266. {
  267. mat[m +n*size_p] = (1.0f/(1.0f+n+m)) + ((n == m)?1.0f*size_p:0.0f);
  268. /* mat[m +n*size_p] = ((n == m)?1.0f*size_p:0.0f); */
  269. }
  270. }
  271. /* #define PRINT_OUTPUT */
  272. #ifdef PRINT_OUTPUT
  273. FPRINTF(stdout, "Input :\n");
  274. for (m = 0; m < size_p; m++)
  275. {
  276. for (n = 0; n < size_p; n++)
  277. {
  278. if (n <= m)
  279. {
  280. FPRINTF(stdout, "%2.2f\t", mat[m +n*size_p]);
  281. }
  282. else
  283. {
  284. FPRINTF(stdout, ".\t");
  285. }
  286. }
  287. FPRINTF(stdout, "\n");
  288. }
  289. #endif
  290. #endif
  291. ret = cholesky(mat, size_p, size_p, nblocks_p);
  292. #ifndef STARPU_SIMGRID
  293. #ifdef PRINT_OUTPUT
  294. FPRINTF(stdout, "Results :\n");
  295. for (m = 0; m < size_p; m++)
  296. {
  297. for (n = 0; n < size_p; n++)
  298. {
  299. if (n <= m)
  300. {
  301. FPRINTF(stdout, "%2.2f\t", mat[m +n*size_p]);
  302. }
  303. else
  304. {
  305. FPRINTF(stdout, ".\t");
  306. }
  307. }
  308. FPRINTF(stdout, "\n");
  309. }
  310. #endif
  311. if (check_p)
  312. {
  313. FPRINTF(stderr, "compute explicit LLt ...\n");
  314. for (m = 0; m < size_p; m++)
  315. {
  316. for (n = 0; n < size_p; n++)
  317. {
  318. if (n > m)
  319. {
  320. mat[m+n*size_p] = 0.0f; /* debug */
  321. }
  322. }
  323. }
  324. float *test_mat = malloc(size_p*size_p*sizeof(float));
  325. STARPU_ASSERT(test_mat);
  326. STARPU_SSYRK("L", "N", size_p, size_p, 1.0f,
  327. mat, size_p, 0.0f, test_mat, size_p);
  328. FPRINTF(stderr, "comparing results ...\n");
  329. #ifdef PRINT_OUTPUT
  330. for (m = 0; m < size_p; m++)
  331. {
  332. for (n = 0; n < size_p; n++)
  333. {
  334. if (n <= m)
  335. {
  336. FPRINTF(stdout, "%2.2f\t", test_mat[m +n*size_p]);
  337. }
  338. else
  339. {
  340. FPRINTF(stdout, ".\t");
  341. }
  342. }
  343. FPRINTF(stdout, "\n");
  344. }
  345. #endif
  346. for (m = 0; m < size_p; m++)
  347. {
  348. for (n = 0; n < size_p; n++)
  349. {
  350. if (n <= m)
  351. {
  352. float orig = (1.0f/(1.0f+m+n)) + ((m == n)?1.0f*size_p:0.0f);
  353. float err = fabsf(test_mat[m +n*size_p] - orig) / orig;
  354. if (err > 0.0001)
  355. {
  356. FPRINTF(stderr, "Error[%u, %u] --> %2.6f != %2.6f (err %2.6f)\n", m, n, test_mat[m +n*size_p], orig, err);
  357. assert(0);
  358. }
  359. }
  360. }
  361. }
  362. free(test_mat);
  363. }
  364. #endif
  365. shutdown_system(&mat, size_p, pinned_p);
  366. return ret;
  367. }