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
static constexpr int NumThreads = 32 ; __shared__ int smem[NumThreads]; __global__ void sumValues(int *sum_out) { int threadID = threadIdx.x; unsigned int mask = __ballot_sync(0xffffffff, threadID < (NumThreads / 2)); if (threadId <= (NumThreads / 2)) { smem[threadId] ...
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; } } for (int i = 0; i < num_threads; i++) { if(...
In that kernel, create a mask using__ballot_syncwiththreadID < NumThreads/2as the predicate, which evaluates to true for the first half of the warp wherethreadID<16(threads 0, 1, .. 15). For those 16 threads, assign a value (threadID) to shared memory, and...
(void *)d_a, 0, ARRAY_BYTES); gpu_increment_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 incremented ...
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; }
首先,按照 CUDA 编程模型对任务进行并行划分,grid 大小(num_heads, num_seqs),grid 中每个 CUDA thread block 大小(NUM_THREADS),NUM_THREADS 是常量默认为 128,也就说每个 thread block 包含 128 个线程,负责完成 output 矩阵一行(包含 head_size 个元素)结果的 attention 计算任务。thread block 中的线程进一...
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++) { ...
matMultCUDA<<<n, NUM_THREADS, sizeof(float) * n>>> (ac, pitch_a / sizeof(float), bc, pitch_b / sizeof(float), cc, pitch_c / sizeof(float), n); 同样的,把计算结果复制回到主内存时,也要使用传回的宽度值: cudaMemcpy2D(c, sizeof(float) * ldc, cc, pitch_c, ...
cuLaunchKernel( kernel, NUM_BLOCKS, # grid x dim 1, # grid y dim 1, # grid z dim NUM_THREADS, # block x dim 1, # block y dim 1, # block z dim 0, # dynamic shared memory stream, # stream args.ctypes.get_data(), # kernel arguments 0, # extra (ignore)) err, = cuda....