CUDA中block和thread的合理划分配置

CUDA并行编程的基本思路是把一个很大的任务划分成N个简单重复的操作，创建N个线程分别执行执行，每个网格（Grid）可以最多创建65535个线程块，每个线程块（Block）一般最多可以创建512个并行线程，在第一个CUDA程序中对核函数的调用是：

addKernel<<<1, size>>>(dev_c, dev_a, dev_b);

这里的<<<>>>运算符内是核函数的执行参数，告诉编译器运行时如何启动核函数，用于说明内核函数中的线程数量，以及线程是如何组织的。

<<<>>>运算符完整的执行配置参数形式是<<<Dg, Db, Ns, S>>>

#include "cuda_runtime.h"#include "device_launch_parameters.h"#include <stdio.h>cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size);__global__ void addKernel(int *c, const int *a, const int *b){	int i = blockIdx.x;	c[i] = a[i] + b[i];}int main(){	const int arraySize = 5;	const int a[arraySize] = { 1, 2, 3, 4, 5 };	const int b[arraySize] = { 10, 20, 30, 40, 50 };	int c[arraySize] = { 0 };	// Add vectors in parallel.	cudaError_t cudaStatus = addWithCuda(c, a, b, arraySize);	if (cudaStatus != cudaSuccess) {		fPRintf(stderr, "addWithCuda failed!");		return 1;	}	printf("{1,2,3,4,5} + {10,20,30,40,50} = {%d,%d,%d,%d,%d}/n",		c[0], c[1], c[2], c[3], c[4]);	// cudaDeviceReset must be called before exiting in order for profiling and	// tracing tools such as Nsight and Visual Profiler to show complete traces.	cudaStatus = cudaDeviceReset();	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaDeviceReset failed!");		return 1;	}	getchar();	return 0;}// Helper function for using CUDA to add vectors in parallel.cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size){	int *dev_a = 0;	int *dev_b = 0;	int *dev_c = 0;	cudaError_t cudaStatus;	// Choose which GPU to run on, change this on a multi-GPU system.	cudaStatus = cudaSetDevice(0);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaSetDevice failed!  Do you have a CUDA-capable GPU installed?");		goto Error;	}	// Allocate GPU buffers for three vectors (two input, one output)    .	cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMalloc failed!");		goto Error;	}	cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMalloc failed!");		goto Error;	}	cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int));	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMalloc failed!");		goto Error;	}	// Copy input vectors from host memory to GPU buffers.	cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMemcpy failed!");		goto Error;	}	cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMemcpy failed!");		goto Error;	}	// Launch a kernel on the GPU with one thread for each element.	//addKernel<<<1, size>>>(dev_c, dev_a, dev_b);	addKernel << <size, 1 >> > (dev_c, dev_a, dev_b);	// Check for any errors launching the kernel	cudaStatus = cudaGetLastError();	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "addKernel launch failed: %s/n", cudaGetErrorString(cudaStatus));		goto Error;	}	// cudaDeviceSynchronize waits for the kernel to finish, and returns	// any errors encountered during the launch.	cudaStatus = cudaDeviceSynchronize();	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!/n", cudaStatus);		goto Error;	}	// Copy output vector from GPU buffer to host memory.	cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMemcpy failed!");		goto Error;	}Error:	cudaFree(dev_c);	cudaFree(dev_a);	cudaFree(dev_b);	return cudaStatus;}执行结果一致：更普遍的情况是需要创建多个线程块，每个线程块包含多个并行线程，这种情况下线程索引的计算为：int tid=threadIdx.x+blockIdx.x*blockDim.x;blockIdx代表线程块在网格中的索引值，blockDim代表线程块的尺寸大小，另外还有gridDim代表网格的尺寸大小。如果有N个并行的任务，我们希望每个线程块固定包含6个并行的线程，则可以使用以下的核函数调用：addKernel<<<(N+5)/6, 6>>>(dev_c, dev_a, dev_b);把第一个CUDA程序的向量个数增加到15个，修改成以上调用方式：
#include "cuda_runtime.h"#include "device_launch_parameters.h"#include <stdio.h>cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size);__global__ void addKernel(int *c, const int *a, const int *b){	int i = threadIdx.x + blockIdx.x*blockDim.x;	if (i < 15)		c[i] = a[i] + b[i];}int main(){	const int arraySize = 15;	const int a[arraySize] = { 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15 };	const int b[arraySize] = { 10, 20, 30, 40, 50,60,70,80,90,100,110,120,130,140,150 };	int c[arraySize] = { 0 };	// Add vectors in parallel.	cudaError_t cudaStatus = addWithCuda(c, a, b, arraySize);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "addWithCuda failed!");		return 1;	}	printf("{ 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15}+/n{ 10, 20, 30, 40, 50,60,70,80,90,100,110,120,130,140,150}=/n{%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d}/n",		c[0], c[1], c[2], c[3], c[4], c[5], c[6], c[7], c[8], c[9], c[10], c[11], c[12], c[13], c[14]);	// cudaDeviceReset must be called before exiting in order for profiling and	// tracing tools such as Nsight and Visual Profiler to show complete traces.	cudaStatus = cudaDeviceReset();	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaDeviceReset failed!");		return 1;	}	getchar();	return 0;}// Helper function for using CUDA to add vectors in parallel.cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size){	int *dev_a = 0;	int *dev_b = 0;	int *dev_c = 0;	cudaError_t cudaStatus;	// Choose which GPU to run on, change this on a multi-GPU system.	cudaStatus = cudaSetDevice(0);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaSetDevice failed!  Do you have a CUDA-capable GPU installed?");		goto Error;	}	// Allocate GPU buffers for three vectors (two input, one output)    .	cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMalloc failed!");		goto Error;	}	cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMalloc failed!");		goto Error;	}	cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int));	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMalloc failed!");		goto Error;	}	// Copy input vectors from host memory to GPU buffers.	cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMemcpy failed!");		goto Error;	}	cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMemcpy failed!");		goto Error;	}	// Launch a kernel on the GPU with one thread for each element.	//addKernel<<<1, size>>>(dev_c, dev_a, dev_b);	addKernel << <(size + 5) / 6, 6 >> > (dev_c, dev_a, dev_b);	// Check for any errors launching the kernel	cudaStatus = cudaGetLastError();	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "addKernel launch failed: %s/n", cudaGetErrorString(cudaStatus));		goto Error;	}	// cudaDeviceSynchronize waits for the kernel to finish, and returns	// any errors encountered during the launch.	cudaStatus = cudaDeviceSynchronize();	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!/n", cudaStatus);		goto Error;	}	// Copy output vector from GPU buffer to host memory.	cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);	if (cudaStatus != cudaSuccess) {		fprintf(stderr, "cudaMemcpy failed!");		goto Error;	}Error:	cudaFree(dev_c);	cudaFree(dev_a);	cudaFree(dev_b);	return cudaStatus;}执行结果：以下CUDA和OpenCV混合编程，对一幅图像上每个像素点的颜色执行一次运算，生成一幅规则的图形。新建了一个 dim3类型的变量grid(DIM, DIM)，代表一个二维的网格，尺寸大小是DIM*DIM个线程块:
#include "cuda_runtime.h"  #include <highgui.hpp>  using namespace cv;#define DIM 600   //图像长宽  __global__ void kernel(unsigned char *ptr){	// map from blockIdx to pixel position  	int x = blockIdx.x;	int y = blockIdx.y;	int offset = x + y * gridDim.x;	//BGR设置	ptr[offset * 3 + 0] = 999 * x*y % 255;	ptr[offset * 3 + 1] = 99 * x*x*y*y % 255;	ptr[offset * 3 + 2] = 9 * offset*offset % 255;}// globals needed by the update routine  struct DataBlock{	unsigned char   *dev_bitmap;};int main(void){	DataBlock   data;	cudaError_t error;	Mat image = Mat(DIM, DIM, CV_8UC3, Scalar::all(0));	data.dev_bitmap = image.data;	unsigned char    *dev_bitmap;	error = cudaMalloc((void**)&dev_bitmap, 3 * image.cols*image.rows);	data.dev_bitmap = dev_bitmap;	dim3    grid(DIM, DIM);	//DIM*DIM个线程块	kernel <<<grid, 1 >>> (dev_bitmap);	error = cudaMemcpy(image.data, dev_bitmap,		3 * image.cols*image.rows,		cudaMemcpyDeviceToHost);	error = cudaFree(dev_bitmap);	imshow("CUDA Grid/Block/Thread)", image);	waitKey();}执行效果：