omp_set_num_threads(num_operator); #pragma omp parallel { int i = omp_get_thread_num(); int offset = i * size / num_operator; ls_operator[i].set_index(i); ls_operator[i].async_operation(&h_c[offset], &h_a[offset], &h_b[offset], &d_c[offset], &d_a[offset], &d_b...
unsigned int mask = __ballot_sync(0xffffffff, threadID < (NumThreads / 2)); if (threadId <= (NumThreads / 2)) { smem[threadId] = threadId; __syncwarp(mask); if (threadID == 0) { *sum_out = 0; for (int i = 0; i < (NumThreads / 2); ++i) *sum_out += smem[i...
(void *)d_a, 0, ARRAY_BYTES); gpu_increment_without_atomic << <NUM_THREADS / BLOCK_WIDTH, BLOCK_WIDTH >> >(d_a); // copy back the array to host memory cudaMemcpy(h_a, d_a, ARRAY_BYTES, cudaMemcpyDeviceToHost); printf("Number of times a particular Array index has been ...
printf("After populateMemory 1: bucket 0, 1 .. 62: %d %d .. %d\n", bucket[0], bucket[1], bucket[numThreads-1]); // Set some more values in bucket populateMemory<<<1, numThreads + 1>>>(bucket); cudaDeviceSynchronize(); printf("After populateMemory 2: bucket 0, 1 .. 63: ...
reduction1_kernel<<<1, numThreads, sharedSize>>>(answer, partial, numBlocks); } 共享内存的大小等于线程块的线程数量,在启动的时候指定。同时要注意,该内核块的线程数量必须是2的幂次,在下文,将介绍如何使用任意大小的数据。 CUDA会把线程组成线程束warp(目前是32个线程),warp的执行由SIMD硬件完成,每个线程...
pthread_t threads[num_threads]; for (int i = 0; i < num_threads; i++) { if (pthread_create(&threads[i], NULL, launch_kernel, 0)) { fprintf(stderr, "Error creating threadn"); return 1; } } for (int i = 0; i < num_threads; i++) { ...
for (int i = 0; i < num_threads; i++) { if (pthread_create(&threads[i], NULL, launch_kernel, 0)) { fprintf(stderr, "Error creating threadn"); return 1; } } for (int i = 0; i < num_threads; i++) { if(pthread_join(threads[i], NULL)) { ...
const int num_threads = 8; pthread_t threads[num_threads]; for (int i = 0; i < num_threads; i++) { if (pthread_create(&threads[i], NULL, launch_kernel, 0)) { fprintf(stderr, "Error creating threadn"); return 1; }
int repeat_ = cell_ * cell_ / num_threads; std::mutex mtx; void ExtractHighGradient(int flag, int cell_part, const cv::Mat &im){ //cell row and cell columns int rows = im.rows; int cols = im.cols; int c_row = im.rows/cell_; ...
张量核心操作发生在扭曲层。wmma中的w表示这一点。参考文件: 这需要一个扭曲中所有threads的co-operation。 每个tensorcore单元可以在每个时钟周期接受一个扭曲的矩阵乘法...