matmul.c 14 KB

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  1. /* StarPU --- Runtime system for heterogeneous multicore architectures.
  2. *
  3. * Copyright (C) 2010,2011 University of Bordeaux
  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 <CL/cl.h>
  17. #include <stdio.h>
  18. #include <string.h>
  19. #include <stdlib.h>
  20. #include <stdint.h>
  21. #include <unistd.h>
  22. #include <assert.h>
  23. #include <math.h>
  24. #include <sys/time.h>
  25. #define error(...) do { fprintf(stderr, "Error: " __VA_ARGS__); exit(EXIT_FAILURE); } while(0)
  26. #define check(exp) do { err = exp; if(err != CL_SUCCESS) { fprintf(stderr, "OpenCL Error (%d): " #exp "\n", err); exit(EXIT_FAILURE); }} while(0)
  27. #define check2(exp) exp; if(err != CL_SUCCESS) { fprintf(stderr, "OpenCL Error (%d): " #exp "\n", err); exit(EXIT_FAILURE); }
  28. // Thread block size
  29. #define BLOCK_SIZE 16 // Kernel thread-block size
  30. #define WORK_SIZE 64 // Kernel global size in lines of A (or C)
  31. #define TYPE float
  32. // Basic Matrix dimensions
  33. #define WA (128L * BLOCK_SIZE) // Matrix A width
  34. #define HA (512L * BLOCK_SIZE) // Matrix A height
  35. #define WB (128L * BLOCK_SIZE) // Matrix B width
  36. #define HB WA // Matrix B height
  37. #define WC WB // Matrix C width
  38. #define HC HA // Matrix C height
  39. #define BLOCKS (HA / WORK_SIZE)
  40. ////////////////////////////////////////////////////////////////////////////////
  41. // declaration, forward
  42. void printDiff(TYPE*, TYPE*, int, int, int, TYPE);
  43. void computeReference(TYPE*, const TYPE*, const TYPE*, unsigned int, unsigned int, unsigned int);
  44. #define str(x) #x
  45. #define CODE "\
  46. #define TYPE float\n\
  47. __kernel void sgemmNN(int wa, int ha, int wb, __global TYPE* A, __global TYPE* B, __global TYPE* C) {\n\
  48. #define BS 16\n\
  49. #define BLOCK_SIZE 16\n\
  50. int bx = get_group_id(0);\n\
  51. int by = get_group_id(1);\n\
  52. \n\
  53. int tx = get_local_id(0);\n\
  54. int ty = get_local_id(1);\n\
  55. \n\
  56. int gx = get_global_id(0);\n\
  57. int gy = get_global_id(1);\n\
  58. __local float As[BS][BS+1];\
  59. __local float Bs[BS][BS+1];\
  60. \n\
  61. unsigned int block_w = min(wb - bx * BLOCK_SIZE, BLOCK_SIZE);\n\
  62. unsigned int block_h = min(ha - by * BLOCK_SIZE, BLOCK_SIZE);\n\
  63. \n\
  64. int valid = (gx < wb && gy < ha);\n\
  65. \n\
  66. TYPE Csub = (TYPE)0.0;\n\
  67. \n\
  68. int pos = 0;\n\
  69. while (pos < wa) {\n\
  70. unsigned int size = min(wa-pos, BLOCK_SIZE);\n\
  71. if (tx < size && gy < ha)\n\
  72. As[tx][ty] = A[pos + tx + wa * gy];\n\
  73. if (ty < size && gx < wb)\n\
  74. Bs[tx][ty] = B[gx + wb * (pos+ty)];\n\
  75. \n\
  76. barrier(CLK_LOCAL_MEM_FENCE);\n\
  77. \n\
  78. if (valid) {\n\
  79. for (int k = 0; k < size; ++k)\n\
  80. Csub += As[k][ty] * Bs[tx][k];\n\
  81. }\n\
  82. pos += size;\n\
  83. barrier(CLK_LOCAL_MEM_FENCE);\n\
  84. }\n\
  85. \n\
  86. if (valid)\n\
  87. C[wb * gy + gx] = Csub;\n\
  88. }"
  89. static char * code = CODE;
  90. int check = 0;
  91. static void __attribute__((unused)) parse_args(int argc, const char **argv)
  92. {
  93. int i;
  94. for (i = 1; i < argc; i++)
  95. {
  96. if (strcmp(argv[i], "-check") == 0)
  97. {
  98. check = 1;
  99. }
  100. if (strcmp(argv[i], "-h") == 0)
  101. {
  102. printf("usage : %s [-check]\n", argv[0]);
  103. }
  104. }
  105. }
  106. // Round Up Division function
  107. size_t roundUp(int group_size, int global_size) {
  108. int r = global_size % group_size;
  109. if(r == 0) {
  110. return global_size;
  111. } else {
  112. return global_size + group_size - r;
  113. }
  114. }
  115. void fillArray(TYPE* data, int size) {
  116. int i;
  117. const TYPE fScale = (TYPE)(1.0f / (float)RAND_MAX);
  118. for (i = 0; i < size; ++i) {
  119. data[i] = fScale * rand();
  120. }
  121. }
  122. void printArray(float* data, int size) {
  123. int i;
  124. for (i = 0; i < size; ++i) {
  125. printf("%d: %.3f\n", i, data[i]);
  126. }
  127. }
  128. /**
  129. * Compare two float arrays using L2-norm with an epsilon tolerance for equality
  130. * @return shrTRUE if \a reference and \a data are identical, otherwise shrFALSE
  131. * @param reference handle to the reference data / gold image
  132. * @param data handle to the computed data
  133. * @param len number of elements in reference and data
  134. * @param epsilon epsilon to use for the comparison
  135. */
  136. int shrCompareL2fe( const float* reference, const float* data, const unsigned int len, const float epsilon ) {
  137. assert(epsilon >= 0);
  138. float error = 0;
  139. float ref = 0;
  140. unsigned int i;
  141. for(i = 0; i < len; ++i) {
  142. float diff = reference[i] - data[i];
  143. error += diff * diff;
  144. ref += reference[i] * reference[i];
  145. }
  146. float normRef = sqrtf(ref);
  147. if (fabs(ref) < 1e-7) {
  148. #ifdef _DEBUG
  149. fprintf(stderr, "ERROR, reference l2-norm is 0\n");
  150. #endif
  151. return 0;
  152. }
  153. float normError = sqrtf(error);
  154. error = normError / normRef;
  155. int result = error < epsilon;
  156. #ifdef _DEBUG
  157. if( !result) {
  158. fprintf(stderr, "ERROR, l2-norm error %d is greater than epsilon %lf \n", error, epsilon);
  159. }
  160. #endif
  161. return result;
  162. }
  163. int main(int argc, const char** argv) {
  164. cl_uint platform_count;
  165. cl_platform_id platforms[5];
  166. cl_int err = CL_SUCCESS;
  167. unsigned int i, p;
  168. cl_device_type dev_type = CL_DEVICE_TYPE_ALL;
  169. void * ptrs[BLOCKS];
  170. cl_command_queue cqs[BLOCKS];
  171. cl_mem d_A[BLOCKS];
  172. cl_mem d_C[BLOCKS];
  173. cl_mem d_B[BLOCKS];
  174. cl_event GPUDone[BLOCKS];
  175. cl_event GPUExecution[BLOCKS];
  176. struct timeval start, end;
  177. int workOffset[BLOCKS];
  178. int workSize[BLOCKS];
  179. unsigned int sizePerGPU = HC / BLOCKS;
  180. unsigned int sizeMod = HC % BLOCKS;
  181. size_t A_size = WA * HA;
  182. size_t A_mem_size = sizeof(TYPE) * A_size;
  183. TYPE* A_data;
  184. size_t B_size = WB * HB;
  185. size_t B_mem_size = sizeof(TYPE) * B_size;
  186. TYPE* B_data;
  187. size_t C_size = WC * HC;
  188. size_t C_mem_size = sizeof(TYPE) * C_size;
  189. TYPE* C_data;
  190. parse_args(argc, argv);
  191. check(clGetPlatformIDs(5, platforms, &platform_count));
  192. if (platform_count == 0) {
  193. printf("No platform found\n");
  194. exit(77);
  195. }
  196. cl_uint device_count;
  197. cl_uint devs[platform_count];
  198. cl_device_id * devices[platform_count];
  199. cl_context ctx[platform_count];
  200. cl_command_queue * commandQueue[platform_count];
  201. device_count = 0;
  202. for (p=0; p<platform_count; p++) {
  203. cl_platform_id platform = platforms[p];
  204. err = clGetDeviceIDs(platform, dev_type, 0, NULL, &devs[p]);
  205. if (err == CL_DEVICE_NOT_FOUND) {
  206. devs[p] = 0;
  207. continue;
  208. }
  209. if (devs[p] == 0) {
  210. printf("No OpenCL device found\n");
  211. exit(77);
  212. }
  213. if (err != CL_SUCCESS) {
  214. fprintf(stderr, "OpenCL Error (%d) in clGetDeviceIDs()\n", err);
  215. exit(EXIT_FAILURE);
  216. }
  217. if (devs[p] == 0)
  218. continue;
  219. devices[p] = (cl_device_id*)malloc(sizeof(cl_device_id) * devs[p]);
  220. commandQueue[p] = (cl_command_queue*)malloc(sizeof(cl_command_queue) * devs[p]);
  221. check(clGetDeviceIDs(platform, dev_type, devs[p], devices[p], NULL));
  222. cl_context_properties properties[] = {CL_CONTEXT_PLATFORM, (cl_context_properties)platform, 0};
  223. check2(ctx[p] = clCreateContext(properties, devs[p], devices[p], NULL, NULL, &err));
  224. for(i = 0; i < devs[p]; ++i)
  225. {
  226. cl_device_id device = devices[p][i];
  227. char name[2048];
  228. name[0] = '\0';
  229. clGetDeviceInfo(device, CL_DEVICE_NAME, 2048, name, NULL);
  230. printf("Device %d: %s\n", i, name);
  231. check2(commandQueue[p][i] = clCreateCommandQueue(ctx[p], device, CL_QUEUE_PROFILING_ENABLE | CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err));
  232. }
  233. device_count += devs[p];
  234. }
  235. if (device_count == 0)
  236. error("No device found\n");
  237. cl_kernel multiplicationKernel[platform_count];
  238. printf("\nUsing Matrix Sizes: A(%lu x %lu), B(%lu x %lu), C(%lu x %lu)\n",
  239. (unsigned long)WA, (unsigned long)HA, (unsigned long)WB, (unsigned long)HB, (unsigned long)WC, (unsigned long)HC);
  240. // allocate host memory for matrices A, B and C
  241. A_data = (TYPE*)malloc(A_mem_size);
  242. if (A_data == NULL) {
  243. perror("malloc");
  244. }
  245. B_data = (TYPE*)malloc(B_mem_size);
  246. if (B_data == NULL) {
  247. perror("malloc");
  248. }
  249. C_data = (TYPE*) malloc(C_mem_size);
  250. if (C_data == NULL) {
  251. perror("malloc");
  252. }
  253. cl_program program[platform_count];
  254. for (p=0; p<platform_count; p++) {
  255. if (devs[p] == 0)
  256. continue;
  257. check2(program[p] = clCreateProgramWithSource(ctx[p], 1, (const char **)&code, NULL, &err));
  258. check(clBuildProgram(program[p], 0, NULL, NULL, NULL, NULL));
  259. check2(multiplicationKernel[p] = clCreateKernel(program[p], "sgemmNN", &err));
  260. }
  261. printf("Initializing data...\n");
  262. srand(2008);
  263. fillArray(A_data, A_size);
  264. fillArray(B_data, B_size);
  265. memset(C_data, 0, C_size);
  266. printf("Computing...\n");
  267. workOffset[0] = 0;
  268. gettimeofday(&start, NULL);
  269. size_t localWorkSize[] = {BLOCK_SIZE, BLOCK_SIZE};
  270. int c = 0;
  271. for (p=0; p<platform_count;p++) {
  272. for (i=0; i<devs[p]; i++) {
  273. check2(d_B[c] = clCreateBuffer(ctx[p], CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, HB * WB * sizeof(TYPE), B_data, &err));
  274. c++;
  275. }
  276. }
  277. for(i=0; i < BLOCKS; ++i)
  278. {
  279. int d = i % device_count;
  280. cl_uint platform = 0;
  281. // determine device platform
  282. int dev = d;
  283. for (platform = 0; platform < platform_count; platform++) {
  284. if ((cl_int)(dev - devs[platform]) < 0)
  285. break;
  286. dev -= devs[platform];
  287. }
  288. workSize[i] = (i < sizeMod) ? sizePerGPU+1 : sizePerGPU;
  289. check2(d_A[i] = clCreateBuffer(ctx[platform], CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, workSize[i] * WA * sizeof(TYPE), &A_data[workOffset[i] * WA], &err));
  290. check2(d_C[i] = clCreateBuffer(ctx[platform], CL_MEM_WRITE_ONLY | CL_MEM_USE_HOST_PTR, workSize[i] * WC * sizeof(TYPE), &C_data[workOffset[i] * WC], &err));
  291. check(clSetKernelArg(multiplicationKernel[platform], 0, sizeof(cl_int), &workSize[i]));
  292. check(clSetKernelArg(multiplicationKernel[platform], 1, sizeof(cl_int), &workSize[i]));
  293. check(clSetKernelArg(multiplicationKernel[platform], 2, sizeof(cl_int), &workSize[i]));
  294. check(clSetKernelArg(multiplicationKernel[platform], 3, sizeof(cl_mem), (void *) &d_A[i]));
  295. check(clSetKernelArg(multiplicationKernel[platform], 4, sizeof(cl_mem), (void *) &d_B[d]));
  296. check(clSetKernelArg(multiplicationKernel[platform], 5, sizeof(cl_mem), (void *) &d_C[i]));
  297. size_t globalWorkSize[] = {roundUp(BLOCK_SIZE,WC), roundUp(BLOCK_SIZE,workSize[i])};
  298. check(clEnqueueNDRangeKernel(commandQueue[platform][dev], multiplicationKernel[platform], 2, NULL, globalWorkSize, localWorkSize, 0, NULL, &GPUExecution[i]));
  299. // Non-blocking copy of result from device to host
  300. cqs[i] = commandQueue[platform][dev];
  301. check2(ptrs[i] = clEnqueueMapBuffer(cqs[i], d_C[i], CL_FALSE, CL_MAP_READ, 0, WC * sizeof(TYPE) * workSize[i], 1, &GPUExecution[i], &GPUDone[i], &err));
  302. if(i+1 < BLOCKS)
  303. workOffset[i + 1] = workOffset[i] + workSize[i];
  304. }
  305. // CPU sync with GPU
  306. for (p=0; p<platform_count;p++) {
  307. cl_uint dev;
  308. for (dev=0; dev<devs[p]; dev++) {
  309. clFinish(commandQueue[p][dev]);
  310. }
  311. }
  312. gettimeofday(&end, NULL);
  313. double timing = (double)((end.tv_sec - start.tv_sec)*1000000 + (end.tv_usec - start.tv_usec));
  314. double dSeconds = timing/1000/1000;
  315. double dNumOps = 2.0 * (double)WA * (double)HA * (double)WB;
  316. double gflops = 1.0e-9 * dNumOps/dSeconds;
  317. printf("Throughput = %.4f GFlops/s, Time = %.5f s, Size = %.0f, NumDevsUsed = %d, Blocks = %ld, Workgroup = %zu\n",
  318. gflops, dSeconds, dNumOps, device_count, BLOCKS, localWorkSize[0] * localWorkSize[1]);
  319. // compute reference solution
  320. if (check) {
  321. printf("Comparing results with CPU computation... ");
  322. TYPE* reference = (TYPE*)malloc(C_mem_size);
  323. computeReference(reference, A_data, B_data, HA, WA, WB);
  324. // check result
  325. int res = shrCompareL2fe(reference, C_data, C_size, 1.0e-6f);
  326. if (res == 0) {
  327. printf("\n\n");
  328. printDiff(reference, C_data, WC, HC, 100, 1.0e-5f);
  329. }
  330. else printf("PASSED\n\n");
  331. free(reference);
  332. }
  333. for(i = 0; i < BLOCKS; i++)
  334. {
  335. clEnqueueUnmapMemObject(cqs[i], d_C[i], ptrs[i], 0, NULL, NULL);
  336. }
  337. for(i = 0; i < BLOCKS; i++)
  338. {
  339. clFinish(cqs[i]);
  340. }
  341. for (i=0; i<device_count; i++) {
  342. clReleaseMemObject(d_B[i]);
  343. }
  344. for(i = 0; i < BLOCKS; i++)
  345. {
  346. clReleaseMemObject(d_A[i]);
  347. clReleaseMemObject(d_C[i]);
  348. clReleaseEvent(GPUExecution[i]);
  349. clReleaseEvent(GPUDone[i]);
  350. }
  351. for (p=0; p<platform_count;p++) {
  352. if (devs[p] == 0)
  353. continue;
  354. check(clReleaseKernel(multiplicationKernel[p]));
  355. check(clReleaseProgram(program[p]));
  356. check(clReleaseContext(ctx[p]));
  357. cl_uint k;
  358. for(k = 0; k < devs[p]; ++k)
  359. {
  360. check(clReleaseCommandQueue(commandQueue[p][k]));
  361. }
  362. }
  363. free(A_data);
  364. free(B_data);
  365. free(C_data);
  366. return 0;
  367. }
  368. void printDiff(TYPE *data1, TYPE *data2, int width, int height, int listLength, TYPE listTol) {
  369. printf("Listing first %d Differences > %.6f...\n", listLength, listTol);
  370. int i,j,k;
  371. int error_count=0;
  372. for (j = 0; j < height; j++) {
  373. if (error_count < listLength) {
  374. printf("\n Row %d:\n", j);
  375. }
  376. for (i = 0; i < width; i++) {
  377. k = j * width + i;
  378. float diff = fabs(data1[k] - data2[k]);
  379. if (diff > listTol) {
  380. if (error_count < listLength) {
  381. printf(" Loc(%d,%d)\tCPU=%.5f\tGPU=%.5f\tDiff=%.6f\n", i, j, data1[k], data2[k], diff);
  382. }
  383. error_count++;
  384. }
  385. }
  386. }
  387. printf(" \n Total Errors = %d\n\n", error_count);
  388. }
  389. /**
  390. * Compute reference data set
  391. * C = A * B
  392. * @param C reference data, computed but preallocated
  393. * @param A matrix A as provided to device
  394. * @param B matrix B as provided to device
  395. * @param hA height of matrix A
  396. * @param wB width of matrix B
  397. */
  398. void computeReference(TYPE* C, const TYPE* A, const TYPE* B, unsigned int hA, unsigned int wA, unsigned int wB) {
  399. unsigned int i,j,k;
  400. for (i = 0; i < hA; ++i)
  401. for (j = 0; j < wB; ++j) {
  402. double sum = 0;
  403. for (k = 0; k < wA; ++k) {
  404. double a = A[i * wA + k];
  405. double b = B[k * wB + j];
  406. sum += a * b;
  407. }
  408. C[i * wB + j] = (TYPE)sum;
  409. }
  410. }