cholesky_tag.c 10 KB

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  1. /* StarPU --- Runtime system for heterogeneous multicore architectures.
  2. *
  3. * Copyright (C) 2008-2017,2020 Université de Bordeaux
  4. * Copyright (C) 2012,2013 Inria
  5. * Copyright (C) 2010-2013,2015,2017,2020 CNRS
  6. * Copyright (C) 2013 Thibaut Lambert
  7. * Copyright (C) 2010 Mehdi Juhoor
  8. *
  9. * StarPU is free software; you can redistribute it and/or modify
  10. * it under the terms of the GNU Lesser General Public License as published by
  11. * the Free Software Foundation; either version 2.1 of the License, or (at
  12. * your option) any later version.
  13. *
  14. * StarPU is distributed in the hope that it will be useful, but
  15. * WITHOUT ANY WARRANTY; without even the implied warranty of
  16. * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
  17. *
  18. * See the GNU Lesser General Public License in COPYING.LGPL for more details.
  19. */
  20. /*
  21. * This version of the Cholesky factorization uses explicit dependency
  22. * declaration through dependency tags.
  23. * It also uses data partitioning to split the matrix into submatrices
  24. */
  25. /* Note: this is using fortran ordering, i.e. column-major ordering, i.e.
  26. * elements with consecutive row number are consecutive in memory */
  27. #include "cholesky.h"
  28. #include <starpu_perfmodel.h>
  29. #if defined(STARPU_USE_CUDA) && defined(STARPU_HAVE_MAGMA)
  30. #include "magma.h"
  31. #endif
  32. /*
  33. * Some useful functions
  34. */
  35. static struct starpu_task *create_task(starpu_tag_t id)
  36. {
  37. struct starpu_task *task = starpu_task_create();
  38. task->cl_arg = NULL;
  39. task->use_tag = 1;
  40. task->tag_id = id;
  41. return task;
  42. }
  43. /*
  44. * Create the codelets
  45. */
  46. static struct starpu_task * create_task_11(starpu_data_handle_t dataA, unsigned k)
  47. {
  48. /* FPRINTF(stdout, "task 11 k = %d TAG = %llx\n", k, (TAG11(k))); */
  49. struct starpu_task *task = create_task(TAG11(k));
  50. task->cl = &cl11;
  51. /* which sub-data is manipulated ? */
  52. task->handles[0] = starpu_data_get_sub_data(dataA, 2, k, k);
  53. /* this is an important task */
  54. if (!noprio_p)
  55. task->priority = STARPU_MAX_PRIO;
  56. /* enforce dependencies ... */
  57. if (k > 0)
  58. {
  59. starpu_tag_declare_deps(TAG11(k), 1, TAG22(k-1, k, k));
  60. }
  61. int n = starpu_matrix_get_nx(task->handles[0]);
  62. task->flops = FLOPS_SPOTRF(n);
  63. return task;
  64. }
  65. static int create_task_21(starpu_data_handle_t dataA, unsigned k, unsigned m)
  66. {
  67. int ret;
  68. struct starpu_task *task = create_task(TAG21(k, m));
  69. task->cl = &cl21;
  70. /* which sub-data is manipulated ? */
  71. task->handles[0] = starpu_data_get_sub_data(dataA, 2, k, k);
  72. task->handles[1] = starpu_data_get_sub_data(dataA, 2, m, k);
  73. if (!noprio_p && (m == k+1))
  74. {
  75. task->priority = STARPU_MAX_PRIO;
  76. }
  77. /* enforce dependencies ... */
  78. if (k > 0)
  79. {
  80. starpu_tag_declare_deps(TAG21(k, m), 2, TAG11(k), TAG22(k-1, m, k));
  81. }
  82. else
  83. {
  84. starpu_tag_declare_deps(TAG21(k, m), 1, TAG11(k));
  85. }
  86. int nx = starpu_matrix_get_nx(task->handles[0]);
  87. task->flops = FLOPS_STRSM(nx, nx);
  88. ret = starpu_task_submit(task);
  89. if (ret != -ENODEV) STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
  90. return ret;
  91. }
  92. static int create_task_22(starpu_data_handle_t dataA, unsigned k, unsigned m, unsigned n)
  93. {
  94. int ret;
  95. /* FPRINTF(stdout, "task 22 k,n,m = %d,%d,%d TAG = %llx\n", k,m,n, TAG22(k,m,n)); */
  96. struct starpu_task *task = create_task(TAG22(k, m, n));
  97. task->cl = &cl22;
  98. /* which sub-data is manipulated ? */
  99. task->handles[0] = starpu_data_get_sub_data(dataA, 2, n, k);
  100. task->handles[1] = starpu_data_get_sub_data(dataA, 2, m, k);
  101. task->handles[2] = starpu_data_get_sub_data(dataA, 2, m, n);
  102. if (!noprio_p && (n == k + 1) && (m == k +1) )
  103. {
  104. task->priority = STARPU_MAX_PRIO;
  105. }
  106. /* enforce dependencies ... */
  107. if (k > 0)
  108. {
  109. starpu_tag_declare_deps(TAG22(k, m, n), 3, TAG22(k-1, m, n), TAG21(k, n), TAG21(k, m));
  110. }
  111. else
  112. {
  113. starpu_tag_declare_deps(TAG22(k, m, n), 2, TAG21(k, n), TAG21(k, m));
  114. }
  115. int nx = starpu_matrix_get_nx(task->handles[0]);
  116. task->flops = FLOPS_SGEMM(nx, nx, nx);
  117. ret = starpu_task_submit(task);
  118. if (ret != -ENODEV) STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
  119. return ret;
  120. }
  121. /*
  122. * code to bootstrap the factorization
  123. * and construct the DAG
  124. */
  125. static int _cholesky(starpu_data_handle_t dataA, unsigned nblocks)
  126. {
  127. int ret;
  128. double start;
  129. double end;
  130. struct starpu_task *entry_task = NULL;
  131. /* create all the DAG nodes */
  132. unsigned k, m, n;
  133. start = starpu_timing_now();
  134. for (k = 0; k < nblocks; k++)
  135. {
  136. starpu_iteration_push(k);
  137. struct starpu_task *task = create_task_11(dataA, k);
  138. /* we defer the launch of the first task */
  139. if (k == 0)
  140. {
  141. entry_task = task;
  142. }
  143. else
  144. {
  145. ret = starpu_task_submit(task);
  146. if (ret == -ENODEV)
  147. {
  148. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  149. return 77;
  150. }
  151. STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
  152. }
  153. for (m = k+1; m<nblocks; m++)
  154. {
  155. ret = create_task_21(dataA, k, m);
  156. if (ret == -ENODEV)
  157. {
  158. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  159. return 77;
  160. }
  161. for (n = k+1; n<nblocks; n++)
  162. {
  163. if (n <= m)
  164. {
  165. ret = create_task_22(dataA, k, m, n);
  166. if (ret == -ENODEV)
  167. {
  168. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  169. return 77;
  170. }
  171. }
  172. }
  173. }
  174. starpu_iteration_pop();
  175. }
  176. /* schedule the codelet */
  177. ret = starpu_task_submit(entry_task);
  178. if (ret == -ENODEV) return 77;
  179. STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
  180. /* stall the application until the end of computations */
  181. starpu_tag_wait(TAG11(nblocks-1));
  182. starpu_data_unpartition(dataA, STARPU_MAIN_RAM);
  183. end = starpu_timing_now();
  184. double timing = end - start;
  185. unsigned nx = starpu_matrix_get_nx(dataA);
  186. double flop = (1.0f*nx*nx*nx)/3.0f;
  187. PRINTF("# size\tms\tGFlops\n");
  188. PRINTF("%u\t%.0f\t%.1f\n", nx, timing/1000, (flop/timing/1000.0f));
  189. return 0;
  190. }
  191. static int initialize_system(int argc, char **argv, float **A, unsigned pinned)
  192. {
  193. int ret;
  194. int flags = STARPU_MALLOC_SIMULATION_FOLDED;
  195. #ifdef STARPU_HAVE_MAGMA
  196. magma_init();
  197. #endif
  198. ret = starpu_init(NULL);
  199. if (ret == -ENODEV)
  200. return 77;
  201. STARPU_CHECK_RETURN_VALUE(ret, "starpu_init");
  202. init_sizes();
  203. parse_args(argc, argv);
  204. #ifdef STARPU_USE_CUDA
  205. initialize_chol_model(&chol_model_11,"chol_model_11",cpu_chol_task_11_cost,cuda_chol_task_11_cost);
  206. initialize_chol_model(&chol_model_21,"chol_model_21",cpu_chol_task_21_cost,cuda_chol_task_21_cost);
  207. initialize_chol_model(&chol_model_22,"chol_model_22",cpu_chol_task_22_cost,cuda_chol_task_22_cost);
  208. #else
  209. initialize_chol_model(&chol_model_11,"chol_model_11",cpu_chol_task_11_cost,NULL);
  210. initialize_chol_model(&chol_model_21,"chol_model_21",cpu_chol_task_21_cost,NULL);
  211. initialize_chol_model(&chol_model_22,"chol_model_22",cpu_chol_task_22_cost,NULL);
  212. #endif
  213. starpu_cublas_init();
  214. if (pinned)
  215. flags |= STARPU_MALLOC_PINNED;
  216. starpu_malloc_flags((void **)A, size_p*size_p*sizeof(float), flags);
  217. return 0;
  218. }
  219. static int cholesky(float *matA, unsigned size, unsigned ld, unsigned nblocks)
  220. {
  221. starpu_data_handle_t dataA;
  222. int ret;
  223. /* monitor and partition the A matrix into blocks :
  224. * one block is now determined by 2 unsigned (m,n) */
  225. starpu_matrix_data_register(&dataA, STARPU_MAIN_RAM, (uintptr_t)matA, ld, size, size, sizeof(float));
  226. starpu_data_set_sequential_consistency_flag(dataA, 0);
  227. /* Split into blocks of complete rows first */
  228. struct starpu_data_filter f =
  229. {
  230. .filter_func = starpu_matrix_filter_block,
  231. .nchildren = nblocks
  232. };
  233. /* Then split rows into tiles */
  234. struct starpu_data_filter f2 =
  235. {
  236. /* Note: here "vertical" is for row-major, we are here using column-major. */
  237. .filter_func = starpu_matrix_filter_vertical_block,
  238. .nchildren = nblocks
  239. };
  240. starpu_data_map_filters(dataA, 2, &f, &f2);
  241. ret = _cholesky(dataA, nblocks);
  242. starpu_data_unregister(dataA);
  243. return ret;
  244. }
  245. static void shutdown_system(float **matA, unsigned dim, unsigned pinned)
  246. {
  247. int flags = STARPU_MALLOC_SIMULATION_FOLDED;
  248. if (pinned)
  249. flags |= STARPU_MALLOC_PINNED;
  250. starpu_free_flags(*matA, dim*dim*sizeof(float), flags);
  251. starpu_cublas_shutdown();
  252. starpu_shutdown();
  253. }
  254. int main(int argc, char **argv)
  255. {
  256. /* create a simple definite positive symetric matrix example
  257. *
  258. * Hilbert matrix : h(i,j) = 1/(i+j+1)
  259. * */
  260. float *mat = NULL;
  261. int ret = initialize_system(argc, argv, &mat, pinned_p);
  262. if (ret) return ret;
  263. #ifndef STARPU_SIMGRID
  264. unsigned m,n;
  265. for (n = 0; n < size_p; n++)
  266. {
  267. for (m = 0; m < size_p; m++)
  268. {
  269. mat[m +n*size_p] = (1.0f/(1.0f+n+m)) + ((n == m)?1.0f*size_p:0.0f);
  270. /* mat[m +n*size_p] = ((n == m)?1.0f*size_p:0.0f); */
  271. }
  272. }
  273. /* #define PRINT_OUTPUT */
  274. #ifdef PRINT_OUTPUT
  275. FPRINTF(stdout, "Input :\n");
  276. for (m = 0; m < size_p; m++)
  277. {
  278. for (n = 0; n < size_p; n++)
  279. {
  280. if (n <= m)
  281. {
  282. FPRINTF(stdout, "%2.2f\t", mat[m +n*size_p]);
  283. }
  284. else
  285. {
  286. FPRINTF(stdout, ".\t");
  287. }
  288. }
  289. FPRINTF(stdout, "\n");
  290. }
  291. #endif
  292. #endif
  293. ret = cholesky(mat, size_p, size_p, nblocks_p);
  294. #ifndef STARPU_SIMGRID
  295. #ifdef PRINT_OUTPUT
  296. FPRINTF(stdout, "Results :\n");
  297. for (m = 0; m < size_p; m++)
  298. {
  299. for (n = 0; n < size_p; n++)
  300. {
  301. if (n <= m)
  302. {
  303. FPRINTF(stdout, "%2.2f\t", mat[m +n*size_p]);
  304. }
  305. else
  306. {
  307. FPRINTF(stdout, ".\t");
  308. }
  309. }
  310. FPRINTF(stdout, "\n");
  311. }
  312. #endif
  313. if (check_p)
  314. {
  315. FPRINTF(stderr, "compute explicit LLt ...\n");
  316. for (m = 0; m < size_p; m++)
  317. {
  318. for (n = 0; n < size_p; n++)
  319. {
  320. if (n > m)
  321. {
  322. mat[m+n*size_p] = 0.0f; /* debug */
  323. }
  324. }
  325. }
  326. float *test_mat = malloc(size_p*size_p*sizeof(float));
  327. STARPU_ASSERT(test_mat);
  328. STARPU_SSYRK("L", "N", size_p, size_p, 1.0f,
  329. mat, size_p, 0.0f, test_mat, size_p);
  330. FPRINTF(stderr, "comparing results ...\n");
  331. #ifdef PRINT_OUTPUT
  332. for (m = 0; m < size_p; m++)
  333. {
  334. for (n = 0; n < size_p; n++)
  335. {
  336. if (n <= m)
  337. {
  338. FPRINTF(stdout, "%2.2f\t", test_mat[m +n*size_p]);
  339. }
  340. else
  341. {
  342. FPRINTF(stdout, ".\t");
  343. }
  344. }
  345. FPRINTF(stdout, "\n");
  346. }
  347. #endif
  348. for (m = 0; m < size_p; m++)
  349. {
  350. for (n = 0; n < size_p; n++)
  351. {
  352. if (n <= m)
  353. {
  354. float orig = (1.0f/(1.0f+m+n)) + ((m == n)?1.0f*size_p:0.0f);
  355. float err = fabsf(test_mat[m +n*size_p] - orig) / orig;
  356. if (err > 0.0001)
  357. {
  358. FPRINTF(stderr, "Error[%u, %u] --> %2.6f != %2.6f (err %2.6f)\n", m, n, test_mat[m +n*size_p], orig, err);
  359. assert(0);
  360. }
  361. }
  362. }
  363. }
  364. free(test_mat);
  365. }
  366. #endif
  367. shutdown_system(&mat, size_p, pinned_p);
  368. return ret;
  369. }