专业网站建设哪家效果好,wordpress head 优化,站酷海洛,货代一般都去哪个网站找客户为了确保能够真正地了解线程块的分配#xff0c;接下来我们写一个简短的内核程序来输出线程块、线程、线程束和线程全局标号到屏幕上。现在#xff0c;除非你使用的是 3.2 版本以上的 SDK否则内核中是不支持 printf的。因此#xff0c;我们可以将数据传送回 CPU 端然后输出到… 为了确保能够真正地了解线程块的分配接下来我们写一个简短的内核程序来输出线程块、线程、线程束和线程全局标号到屏幕上。现在除非你使用的是 3.2 版本以上的 SDK否则内核中是不支持 printf的。因此我们可以将数据传送回 CPU 端然后输出到控制台窗口内核的代码如下: #include cuda_runtime.h
#include device_launch_parameters.h#include stdio.h
#include stdlib.h
#include conio.h__global__ void what_is_my_id(unsigned int* const block,unsigned int* const thread,unsigned int* const warp,unsigned int* const calc_thread) {/* Thread id is block index * block size thread offset into the block */const unsigned int thread_idx (blockIdx.x * blockDim.x) threadIdx.x;block[thread_idx] blockIdx.x; thread[thread_idx] threadIdx.x;/* Calculate warp using buit in variable warpSize */warp[thread_idx] threadIdx.x / warpSize;calc_thread[thread_idx] thread_idx;
}#define ARRAY_SIZE 128
#define ARRAY_SIZE_IN_BYTES (sizeof(unsigned int)* (ARRAY_SIZE))/* Declare statically four arrays of ARRAY_SIZE each */
unsigned int cpu_block[ARRAY_SIZE];
unsigned int cpu_thread[ARRAY_SIZE];
unsigned int cpu_warp[ARRAY_SIZE];
unsigned int cpu_calc_thread[ARRAY_SIZE];int main(void) {/* Total thread count 2*64128 */const unsigned int num_blocks 2;const unsigned int num_threads 64;char ch;/* Declare pointers for GPU based params */unsigned int* gpu_block;unsigned int* gpu_thread;unsigned int* gpu_warp;unsigned int* gpu_calc_thread;/* Declare loop counter for use later */unsigned int i;/* Allocate four arrays on the GPU */cudaMalloc((void**)gpu_block, ARRAY_SIZE_IN_BYTES);cudaMalloc((void**)gpu_thread, ARRAY_SIZE_IN_BYTES);cudaMalloc((void**)gpu_warp, ARRAY_SIZE_IN_BYTES);cudaMalloc((void**)gpu_calc_thread, ARRAY_SIZE_IN_BYTES);/* Execute our kerne] */what_is_my_id num_blocks, num_threads(gpu_block, gpu_thread, gpu_warp, gpu_calc_thread);/* Copy back the gpu results to the CPU */cudaMemcpy(cpu_block, gpu_block, ARRAY_SIZE_IN_BYTES, cudaMemcpyDeviceToHost);cudaMemcpy(cpu_thread, gpu_thread, ARRAY_SIZE_IN_BYTES, cudaMemcpyDeviceToHost);cudaMemcpy(cpu_warp, gpu_warp, ARRAY_SIZE_IN_BYTES, cudaMemcpyDeviceToHost);cudaMemcpy(cpu_calc_thread, gpu_calc_thread, ARRAY_SIZE_IN_BYTES, cudaMemcpyDeviceToHost);/* Free the arrays on the GPU as now were done with them */cudaFree(gpu_block);cudaFree(gpu_thread);cudaFree(gpu_warp);cudaFree(gpu_calc_thread);/* Iterate through the arrays and print */for (i 0; i ARRAY_SIZE; i) {printf(Calculated Thread: %3u - Block:%2u - Warp %2u - Thread %3u\n, cpu_calc_thread[i], cpu_block[i], cpu_warp[i], cpu_thread[i]);}ch getch();
} 在这个例子中我们可以看到线程块按照线程块的编号紧密相连。由于处理的是一维数组所以我们对线程块采用相同的布局便可简单解决问题。以下是此程序的输出结果: 正如我们计算的那样线程索引是0~ 127。一共有两个线块每个线程块包含 64个线程每个线程块内部线程的索引为0~63。一个线程块包含两个线束。
