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 { cl_int 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_mem d_A[BLOCKS];
  171. cl_mem d_C[BLOCKS];
  172. cl_mem d_B[BLOCKS];
  173. cl_event GPUDone[BLOCKS];
  174. cl_event GPUExecution[BLOCKS];
  175. struct timeval start, end;
  176. int workOffset[BLOCKS];
  177. int workSize[BLOCKS];
  178. unsigned int sizePerGPU = HC / BLOCKS;
  179. unsigned int sizeMod = HC % BLOCKS;
  180. size_t A_size = WA * HA;
  181. size_t A_mem_size = sizeof(TYPE) * A_size;
  182. TYPE* A_data;
  183. size_t B_size = WB * HB;
  184. size_t B_mem_size = sizeof(TYPE) * B_size;
  185. TYPE* B_data;
  186. size_t C_size = WC * HC;
  187. size_t C_mem_size = sizeof(TYPE) * C_size;
  188. TYPE* C_data;
  189. parse_args(argc, argv);
  190. check(clGetPlatformIDs(5, platforms, &platform_count));
  191. if (platform_count == 0) {
  192. printf("No platform found\n");
  193. exit(77);
  194. }
  195. cl_uint device_count;
  196. cl_uint devs[platform_count];
  197. cl_device_id * devices[platform_count];
  198. cl_context ctx[platform_count];
  199. cl_command_queue * commandQueue[platform_count];
  200. device_count = 0;
  201. for (p=0; p<platform_count; p++) {
  202. cl_platform_id platform = platforms[p];
  203. cl_int err = clGetDeviceIDs(platform, dev_type, 0, NULL, &devs[p]);
  204. if (err == CL_DEVICE_NOT_FOUND) {
  205. devs[p] = 0;
  206. continue;
  207. }
  208. if (devs[p] == 0) {
  209. printf("No OpenCL device found\n");
  210. exit(77);
  211. }
  212. if (err != CL_SUCCESS) {
  213. fprintf(stderr, "OpenCL Error (%d) in clGetDeviceIDs()\n", err);
  214. exit(EXIT_FAILURE);
  215. }
  216. if (devs[p] == 0)
  217. continue;
  218. devices[p] = (cl_device_id*)malloc(sizeof(cl_device_id) * devs[p]);
  219. commandQueue[p] = (cl_command_queue*)malloc(sizeof(cl_command_queue) * devs[p]);
  220. check(clGetDeviceIDs(platform, dev_type, devs[p], devices[p], NULL));
  221. cl_context_properties properties[] = {CL_CONTEXT_PLATFORM, (cl_context_properties)platform, 0};
  222. check2(ctx[p] = clCreateContext(properties, devs[p], devices[p], NULL, NULL, &err));
  223. for(i = 0; i < devs[p]; ++i)
  224. {
  225. cl_device_id device = devices[p][i];
  226. char name[2048];
  227. name[0] = '\0';
  228. clGetDeviceInfo(device, CL_DEVICE_NAME, 2048, name, NULL);
  229. printf("Device %d: %s\n", i, name);
  230. check2(commandQueue[p][i] = clCreateCommandQueue(ctx[p], device, CL_QUEUE_PROFILING_ENABLE | CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err));
  231. }
  232. device_count += devs[p];
  233. }
  234. if (device_count == 0)
  235. error("No device found\n");
  236. cl_kernel multiplicationKernel[platform_count];
  237. printf("\nUsing Matrix Sizes: A(%lu x %lu), B(%lu x %lu), C(%lu x %lu)\n",
  238. (unsigned long)WA, (unsigned long)HA, (unsigned long)WB, (unsigned long)HB, (unsigned long)WC, (unsigned long)HC);
  239. // allocate host memory for matrices A, B and C
  240. A_data = (TYPE*)malloc(A_mem_size);
  241. if (A_data == NULL) {
  242. perror("malloc");
  243. }
  244. B_data = (TYPE*)malloc(B_mem_size);
  245. if (B_data == NULL) {
  246. perror("malloc");
  247. }
  248. C_data = (TYPE*) malloc(C_mem_size);
  249. if (C_data == NULL) {
  250. perror("malloc");
  251. }
  252. cl_program program[platform_count];
  253. for (p=0; p<platform_count; p++) {
  254. if (devs[p] == 0)
  255. continue;
  256. check2(program[p] = clCreateProgramWithSource(ctx[p], 1, (const char **)&code, NULL, &err));
  257. check(clBuildProgram(program[p], 0, NULL, NULL, NULL, NULL));
  258. check2(multiplicationKernel[p] = clCreateKernel(program[p], "sgemmNN", &err));
  259. }
  260. printf("Initializing data...\n");
  261. srand(2008);
  262. fillArray(A_data, A_size);
  263. fillArray(B_data, B_size);
  264. memset(C_data, 0, C_size);
  265. printf("Computing...\n");
  266. workOffset[0] = 0;
  267. gettimeofday(&start, NULL);
  268. size_t localWorkSize[] = {BLOCK_SIZE, BLOCK_SIZE};
  269. int c = 0;
  270. for (p=0; p<platform_count;p++) {
  271. for (i=0; i<devs[p]; i++) {
  272. check2(d_B[c] = clCreateBuffer(ctx[p], CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, HB * WB * sizeof(TYPE), B_data, &err));
  273. c++;
  274. }
  275. }
  276. for(i=0; i < BLOCKS; ++i)
  277. {
  278. int d = i % device_count;
  279. cl_uint p = 0;
  280. // determine device platform
  281. int dev = d;
  282. for (p = 0; p < platform_count; p++) {
  283. if ((cl_int)(dev - devs[p]) < 0)
  284. break;
  285. dev -= devs[p];
  286. }
  287. workSize[i] = (i < sizeMod) ? sizePerGPU+1 : sizePerGPU;
  288. check2(d_A[i] = clCreateBuffer(ctx[p], CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, workSize[i] * WA * sizeof(TYPE), &A_data[workOffset[i] * WA], &err));
  289. check2(d_C[i] = clCreateBuffer(ctx[p], CL_MEM_WRITE_ONLY | CL_MEM_USE_HOST_PTR, workSize[i] * WC * sizeof(TYPE), &C_data[workOffset[i] * WC], &err));
  290. check(clSetKernelArg(multiplicationKernel[p], 0, sizeof(cl_int), &workSize[i]));
  291. check(clSetKernelArg(multiplicationKernel[p], 1, sizeof(cl_int), &workSize[i]));
  292. check(clSetKernelArg(multiplicationKernel[p], 2, sizeof(cl_int), &workSize[i]));
  293. check(clSetKernelArg(multiplicationKernel[p], 3, sizeof(cl_mem), (void *) &d_A[i]));
  294. check(clSetKernelArg(multiplicationKernel[p], 4, sizeof(cl_mem), (void *) &d_B[d]));
  295. check(clSetKernelArg(multiplicationKernel[p], 5, sizeof(cl_mem), (void *) &d_C[i]));
  296. size_t globalWorkSize[] = {roundUp(BLOCK_SIZE,WC), roundUp(BLOCK_SIZE,workSize[i])};
  297. check(clEnqueueNDRangeKernel(commandQueue[p][dev], multiplicationKernel[p], 2, NULL, globalWorkSize, localWorkSize, 0, NULL, &GPUExecution[i]));
  298. // Non-blocking copy of result from device to host
  299. check2(ptrs[i] = clEnqueueMapBuffer(commandQueue[p][dev], d_C[i], CL_FALSE, CL_MAP_READ, 0, WC * sizeof(TYPE) * workSize[i], 1, &GPUExecution[i], &GPUDone[i], &err));
  300. if(i+1 < BLOCKS)
  301. workOffset[i + 1] = workOffset[i] + workSize[i];
  302. }
  303. // CPU sync with GPU
  304. for (p=0; p<platform_count;p++) {
  305. cl_uint dev;
  306. for (dev=0; dev<devs[p]; dev++) {
  307. clFinish(commandQueue[p][dev]);
  308. }
  309. }
  310. gettimeofday(&end, NULL);
  311. double timing = (double)((end.tv_sec - start.tv_sec)*1000000 + (end.tv_usec - start.tv_usec));
  312. double dSeconds = timing/1000/1000;
  313. double dNumOps = 2.0 * (double)WA * (double)HA * (double)WB;
  314. double gflops = 1.0e-9 * dNumOps/dSeconds;
  315. printf("Throughput = %.4f GFlops/s, Time = %.5f s, Size = %.0f, NumDevsUsed = %d, Blocks = %ld, Workgroup = %zu\n",
  316. gflops, dSeconds, dNumOps, device_count, BLOCKS, localWorkSize[0] * localWorkSize[1]);
  317. for (i=0; i<device_count; i++) {
  318. clReleaseMemObject(d_B[i]);
  319. }
  320. for(i = 0; i < BLOCKS; i++)
  321. {
  322. clReleaseMemObject(d_A[i]);
  323. clReleaseMemObject(d_C[i]);
  324. clReleaseEvent(GPUExecution[i]);
  325. clReleaseEvent(GPUDone[i]);
  326. }
  327. // compute reference solution
  328. if (check) {
  329. printf("Comparing results with CPU computation... ");
  330. TYPE* reference = (TYPE*)malloc(C_mem_size);
  331. computeReference(reference, A_data, B_data, HA, WA, WB);
  332. // check result
  333. int res = shrCompareL2fe(reference, C_data, C_size, 1.0e-6f);
  334. if (res == 0) {
  335. printf("\n\n");
  336. printDiff(reference, C_data, WC, HC, 100, 1.0e-5f);
  337. }
  338. else printf("PASSED\n\n");
  339. free(reference);
  340. }
  341. for (p=0; p<platform_count;p++) {
  342. if (devs[p] == 0)
  343. continue;
  344. check(clReleaseKernel(multiplicationKernel[p]));
  345. check(clReleaseProgram(program[p]));
  346. check(clReleaseContext(ctx[p]));
  347. cl_uint k;
  348. for(k = 0; k < devs[p]; ++k)
  349. {
  350. check(clReleaseCommandQueue(commandQueue[p][k]));
  351. }
  352. }
  353. free(A_data);
  354. free(B_data);
  355. free(C_data);
  356. return 0;
  357. }
  358. void printDiff(TYPE *data1, TYPE *data2, int width, int height, int listLength, TYPE listTol) {
  359. printf("Listing first %d Differences > %.6f...\n", listLength, listTol);
  360. int i,j,k;
  361. int error_count=0;
  362. for (j = 0; j < height; j++) {
  363. if (error_count < listLength) {
  364. printf("\n Row %d:\n", j);
  365. }
  366. for (i = 0; i < width; i++) {
  367. k = j * width + i;
  368. float diff = fabs(data1[k] - data2[k]);
  369. if (diff > listTol) {
  370. if (error_count < listLength) {
  371. printf(" Loc(%d,%d)\tCPU=%.5f\tGPU=%.5f\tDiff=%.6f\n", i, j, data1[k], data2[k], diff);
  372. }
  373. error_count++;
  374. }
  375. }
  376. }
  377. printf(" \n Total Errors = %d\n\n", error_count);
  378. }
  379. /**
  380. * Compute reference data set
  381. * C = A * B
  382. * @param C reference data, computed but preallocated
  383. * @param A matrix A as provided to device
  384. * @param B matrix B as provided to device
  385. * @param hA height of matrix A
  386. * @param wB width of matrix B
  387. */
  388. void computeReference(TYPE* C, const TYPE* A, const TYPE* B, unsigned int hA, unsigned int wA, unsigned int wB) {
  389. unsigned int i,j,k;
  390. for (i = 0; i < hA; ++i)
  391. for (j = 0; j < wB; ++j) {
  392. double sum = 0;
  393. for (k = 0; k < wA; ++k) {
  394. double a = A[i * wA + k];
  395. double b = B[k * wB + j];
  396. sum += a * b;
  397. }
  398. C[i * wB + j] = (TYPE)sum;
  399. }
  400. }