parallel_redux_heterogeneous_tasks_data.c 6.3 KB

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  1. /* StarPU --- Runtime system for heterogeneous multicore architectures.
  2. *
  3. * Copyright (C) 2016 Bérangère Subervie
  4. *
  5. * StarPU is free software; you can redistribute it and/or modify
  6. * it under the terms of the GNU Lesser General Public License as published by
  7. * the Free Software Foundation; either version 2.1 of the License, or (at
  8. * your option) any later version.
  9. *
  10. * StarPU is distributed in the hope that it will be useful, but
  11. * WITHOUT ANY WARRANTY; without even the implied warranty of
  12. * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
  13. *
  14. * See the GNU Lesser General Public License in COPYING.LGPL for more details.
  15. */
  16. #include <stdbool.h>
  17. #include <starpu.h>
  18. #include "../helper.h"
  19. /* Run a series of tasks with heterogeneous execution time and redux data */
  20. #define TIME 0.010
  21. #ifdef STARPU_QUICK_CHECK
  22. #define TASK_COEFFICIENT 20
  23. #define MARGIN 0.20
  24. #else
  25. #define TASK_COEFFICIENT 100
  26. #define MARGIN 0.10
  27. #endif
  28. #define TIME_CUDA_COEFFICIENT 10
  29. #define TIME_OPENCL_COEFFICIENT 5
  30. #define SECONDS_SCALE_COEFFICIENT_TIMING_NOW 1000000
  31. #define NB_FLOAT 400000
  32. void wait_CPU(void *descr[] STARPU_ATTRIBUTE_UNUSED, void *_args)
  33. {
  34. starpu_sleep(TIME);
  35. }
  36. void wait_CUDA(void *descr[] STARPU_ATTRIBUTE_UNUSED, void *_args)
  37. {
  38. starpu_sleep(TIME/TIME_CUDA_COEFFICIENT);
  39. }
  40. void wait_OPENCL(void *descr[] STARPU_ATTRIBUTE_UNUSED, void *_args)
  41. {
  42. starpu_sleep(TIME/TIME_OPENCL_COEFFICIENT);
  43. }
  44. double cost_function(struct starpu_task *t, struct starpu_perfmodel_arch *a, unsigned i)
  45. {
  46. (void) t; (void) i;
  47. STARPU_ASSERT(a->ndevices == 1);
  48. if (a->devices[0].type == STARPU_CPU_WORKER)
  49. {
  50. STARPU_ASSERT(a->devices[0].ncores == 1);
  51. return TIME * 1000000;
  52. }
  53. else if (a->devices[0].type == STARPU_CUDA_WORKER)
  54. {
  55. return TIME/TIME_CUDA_COEFFICIENT * 1000000;
  56. }
  57. else if (a->devices[0].type == STARPU_OPENCL_WORKER)
  58. {
  59. return TIME/TIME_OPENCL_COEFFICIENT * 1000000;
  60. }
  61. STARPU_ASSERT(0);
  62. return 0.0;
  63. }
  64. static struct starpu_perfmodel perf_model =
  65. {
  66. .type = STARPU_PER_ARCH,
  67. .arch_cost_function = cost_function,
  68. };
  69. static struct starpu_codelet cl =
  70. {
  71. .cpu_funcs = { wait_CPU },
  72. .cuda_funcs = { wait_CUDA },
  73. .opencl_funcs = { wait_OPENCL },
  74. .cpu_funcs_name = { "wait_CPU" },
  75. .nbuffers = 1,
  76. .modes = {STARPU_REDUX},
  77. .flags = STARPU_CODELET_SIMGRID_EXECUTE,
  78. .model = &perf_model,
  79. .name = "cl",
  80. };
  81. static struct starpu_perfmodel perf_model_init =
  82. {
  83. .type = STARPU_PER_ARCH,
  84. .arch_cost_function = cost_function,
  85. };
  86. static struct starpu_codelet cl_init =
  87. {
  88. .cpu_funcs = { wait_CPU },
  89. .cuda_funcs = { wait_CUDA },
  90. .opencl_funcs = { wait_OPENCL },
  91. .cpu_funcs_name = { "wait_CPU" },
  92. .nbuffers = 1,
  93. .modes = {STARPU_RW},
  94. .flags = STARPU_CODELET_SIMGRID_EXECUTE,
  95. .model = &perf_model_init,
  96. .name = "init",
  97. };
  98. static struct starpu_perfmodel perf_model_redux =
  99. {
  100. .type = STARPU_PER_ARCH,
  101. .arch_cost_function = cost_function,
  102. };
  103. static struct starpu_codelet cl_redux =
  104. {
  105. .cpu_funcs = { wait_CPU },
  106. .cuda_funcs = { wait_CUDA },
  107. .opencl_funcs = { wait_OPENCL },
  108. .cpu_funcs_name = { "wait_CPU" },
  109. .nbuffers = 2,
  110. .modes = {STARPU_RW, STARPU_R},
  111. .flags = STARPU_CODELET_SIMGRID_EXECUTE,
  112. .model = &perf_model_redux,
  113. .name = "redux",
  114. };
  115. int main(int argc, char *argv[])
  116. {
  117. int ret;
  118. ret = starpu_initialize(NULL, &argc, &argv);
  119. if (ret == -ENODEV) return STARPU_TEST_SKIPPED;
  120. STARPU_CHECK_RETURN_VALUE(ret, "starpu_init");
  121. unsigned nb_tasks, nb_workers_CPU, nb_workers_CUDA, nb_workers_OPENCL;
  122. double begin_time, end_time, time_m, time_s, speed_up, expected_speed_up, percentage_expected_speed_up;
  123. bool check, check_sup;
  124. nb_workers_CPU = starpu_worker_get_count_by_type(STARPU_CPU_WORKER);
  125. nb_workers_CUDA = starpu_worker_get_count_by_type(STARPU_CUDA_WORKER);
  126. nb_workers_OPENCL = starpu_worker_get_count_by_type(STARPU_OPENCL_WORKER);
  127. nb_tasks = (nb_workers_CPU + nb_workers_CUDA + nb_workers_OPENCL)*TASK_COEFFICIENT;
  128. /* We consider a vector of float that is initialized just as any of C
  129. * data */
  130. float *vector;
  131. starpu_data_handle_t vector_handle;
  132. unsigned i;
  133. vector = calloc(NB_FLOAT, sizeof(float));
  134. #ifndef STARPU_SIMGRID
  135. for (i = 0; i < NB_FLOAT; i++)
  136. vector[i] = (i+1.0f);
  137. #endif
  138. /* Tell StaPU to associate the "vector" vector with the "vector_handle"
  139. * identifier. When a task needs to access a piece of data, it should
  140. * refer to the handle that is associated to it.
  141. * In the case of the "vector" data interface:
  142. * - the first argument of the registration method is a pointer to the
  143. * handle that should describe the data
  144. * - the second argument is the memory node where the data (ie. "vector")
  145. * resides initially: STARPU_MAIN_RAM stands for an address in main memory, as
  146. * opposed to an adress on a GPU for instance.
  147. * - the third argument is the adress of the vector in RAM
  148. * - the fourth argument is the number of elements in the vector
  149. * - the fifth argument is the size of each element.
  150. */
  151. starpu_vector_data_register(&vector_handle, STARPU_MAIN_RAM, (uintptr_t)vector, NB_FLOAT, sizeof(vector[0]));
  152. starpu_data_set_reduction_methods(vector_handle, &cl_redux, &cl_init);
  153. begin_time = starpu_timing_now();
  154. /*execution des tasks*/
  155. for (i=0; i<nb_tasks; i++)
  156. starpu_task_insert(&cl, STARPU_REDUX, vector_handle, 0);
  157. starpu_data_wont_use(vector_handle);
  158. starpu_task_wait_for_all();
  159. end_time = starpu_timing_now();
  160. starpu_data_unregister(vector_handle);
  161. /*on determine si le temps mesure est satisfaisant ou pas*/
  162. time_m = (end_time - begin_time)/SECONDS_SCALE_COEFFICIENT_TIMING_NOW; //pour ramener en secondes
  163. time_s = nb_tasks * TIME;
  164. speed_up = time_s/time_m;
  165. expected_speed_up = nb_workers_CPU + TIME_CUDA_COEFFICIENT*nb_workers_CUDA + TIME_OPENCL_COEFFICIENT*nb_workers_OPENCL;
  166. percentage_expected_speed_up = 100 * (speed_up/expected_speed_up);
  167. check = speed_up >= ((1 - MARGIN) * expected_speed_up);
  168. check_sup = speed_up <= ((1 + MARGIN) * expected_speed_up);
  169. printf("measured time = %f seconds\nsequential time = %f seconds\nspeed up = %f\nnumber of workers CPU = %u\nnumber of workers CUDA = %u\nnumber of workers OPENCL = %u\nnumber of tasks = %u\nexpected speed up = %f\npercentage of expected speed up %.2f%%\n", time_m, time_s, speed_up, nb_workers_CPU, nb_workers_CUDA, nb_workers_OPENCL, nb_tasks, expected_speed_up, percentage_expected_speed_up);
  170. starpu_shutdown();
  171. free(vector);
  172. //test reussi ou test echoue
  173. if (check && check_sup)
  174. {
  175. return EXIT_SUCCESS;
  176. }
  177. else
  178. {
  179. return EXIT_FAILURE;
  180. }
  181. }