CUDA基础-实例演示

CUDA基础-实例演示

CUDA基础-实例演示

[TOC]

CUDA基础-实例演示

CUDA-SDK安装

CUDA10.1安装 +VS2015开发环境搭建

创建项目

  • 打开Visual Studio 2010 旗舰版

CUDA基础-实例演示-1

  • 创建测试项目(矢量相加)

CUDA基础-实例演示-2

  • 自动生成矢量相加代码
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#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); //封装函数,c结果,a第一个矢量,b第二个矢量,size矢量长度。

__global__ void addKernel(int *c, const int *a, const int *b) // Kernel声明
{
int i = threadIdx.x; //线程,一维情况下线程的编号,数据映射关系通过这种方式建立起来。
c[i] = a[i] + b[i];
}

int main()
{
//abc在host端口,所以要拷贝到device端
const int arraySize = 5;
const int a[arraySize] = { 1, 2, 3, 4, 5 }; //数组初始化
const int b[arraySize] = { 10, 20, 30, 40, 50 }; //数组初始化
int c[arraySize] = { 0 }; //结果数组

// Add vectors in parallel.
cudaError_t cudaStatus = addWithCuda(c, a, b, arraySize); //枚举类型
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;
}

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)
{
//abc在host端口,所以要拷贝到device端。有指针没有在GPU分配空间
int *dev_a = 0;
int *dev_b = 0;
int *dev_c = 0;
cudaError_t cudaStatus;

//设置哪一个GPU
// 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;
}

//GPU分配内存空间
// 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;
}

//CPU端口的数据传入
// Copy input vectors from host memory to GPU buffers.
cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);//(传到哪里,从哪传,传多少,枚举类型代表host-device)
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;
}

//c不需要传输

// Launch a kernel on the GPU with one thread for each element.
addKernel<<<1, size>>>(dev_c, dev_a, dev_b); //<<<1,size>>>1个block,size个线程

//错误控制
// 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(); //告诉它已经做完了
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus);
goto Error;
}

//返回数据
// Copy output vector from GPU buffer to host memory.
cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost); //D->H
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}

Error:
cudaFree(dev_c);
cudaFree(dev_a);
cudaFree(dev_b);

return cudaStatus; //返回状态是否成功
}

执行结果

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{1,2,3,4,5} + {10,20,30,40,50} = {11,22,33,44,55}

CUDA调试怎办

Nsight

CUDA基础-实例演示-3

addWithCuda 函数中添加

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int blockPerGrid = 2;
int threadsPerBlock = 8;

addKernel调用时候

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addKernel<<<blockPerGrid, threadsPerBlock>>>(dev c, dev a, dev b);

对于addKernel中线程编号问题,永远只对应一个block内的内容

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int i = threadIdx.x;
c[i] = a[i] + b[i];

如何联通两个block

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int i = blockIdx.x * blockDim.x + threadIdx.x;	//block编号*block内的线程个数 + 线程号

CUDA基础-实例演示-4

一万个向量需要一万个线程,但是硬件设置并没有一个block有一万个线程。

一个SM最多2048线程,最多8个block,所以线程最多不能超过2048,最好是2的次方倍数。(256)

线程规划

如果是不规则数字,比如arraySize=17,需要3个block,2*8+1,浪费了7个。

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__global__ void addKernel(int  *c, const int *a, const int *b)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if(i < 17)//让多余的线程不工作
c[i] = a[i] + b[i];
}

矩阵相加

1.host分配数据
2.初始化
3.GPU分配内存
4.配置线程

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/*
* Veotor addition : C = A + B
*/
#include <stdio.h>
#include <time.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#define HEIGHT 8
#define WIDTH 9

//更换行列不用改变算法结构

__global__ void vectorAdd(const float *A, const float *B, float *C, int numElements)
{
int Col = blockDim.x * blockIdx.x + threadIdx.x;
int Row = blockDim.y * blockIdx.y + threadIdx.y;
if (Col < WIDTH && Row < HEIGHT)
{
C[Row * WIDTH + Col] = A[Row * WIDTH + Col] + B[Row * WIDTH + Col];
}
}

int main(void)
{
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;

// Print the vector length to be used,and compute its size
int numElements = HEIGHT*WIDTH;
size_t size = numElements * sizeof(float);
printf("[Bector addition of %d elements]\n", numElements);

// Alocate the host input vector A
float *h_A = (float *)malloc(size);

// Alocate the host input vector B
float *h_B = (float *)malloc(size);

// Alocate the host input vector C
float *h_C = (float *)malloc(size);

// Verify that allocations succeeded
if (h_A == NULL || h_B == NULL || h_C == NULL)
{
fprintf(stderr, "Failed to allocate host vectors:\n");
exit(EXIT_FAILURE);
}

// initalize the host input vectors
printf("Matrix A:\n");
for(int i = 0; i < HEIGHT; ++i)
{
for(int j = 0; j < WIDTH; ++j)
{
h_A[i * WIDTH +j] = 1 + i;
printf("%7.2f", h_A[i * WIDTH + j]);
}
printf("\n");
}

printf("Matrix B:\n");
for(int i = 0; i < HEIGHT; ++i)
{
for(int j = 0; j < WIDTH; ++j)
{
h_B[i * WIDTH +j] = 2 + i;
printf("%7.2f", h_A[i * WIDTH + j]);
}
printf("\n");
}

// Allocate the device input vector A
float *d_A = NULL;
err = cudaMalloc((void **)&d_A, size);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device vector A (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

// Allocate the device input vector B
float *d_B = NULL;
err = cudaMalloc((void **)&d_B, size);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device vector B (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

// Allocate the device input vector C
float *d_C = NULL;
err = cudaMalloc((void **)&d_C, size);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device vector C (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

// Copy the host input vectors A and B in host memory to the device input vectors in device memory
printf("Copy input data from the host memory to the CUDA device\n");

err = cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy vector A from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

err = cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy vector B from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}


// Launch the Vector Add CUDA Kernel
// int threadsPerBlock = 8
// int blocksperGrid = (numElements + threadsPerBlock -1) / threadsPerBlock

dim3 threadsPerblock(16, 16);
dim3 blocksPerGrid((WIDTH - 1) / 16 + 1,(HEIGHT - 1) / 16 + 1); //因为宽度对应grid中表示的是x坐标系,而高度对应表示的是y坐标系
//printf("CUDA kernel launch with %d blocks of %d threads\n", blockPerGrid, threadsPerBlock);

vectorAdd<<<blocksPerGrid, threadsPerblock>>>(d_A, d_B, d_C, numElements);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to launch vectorAdd kernel(error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

//Copy the device result vector in device memory in the host result vector in host memory
printf("Copy output data from the CUDA device in the host memory\n");
err = cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy vector C from device to host (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

printf("Matrix C:\n");
for(int i = 0; i < HEIGHT; ++i)
{
for(int j = 0; j < WIDTH; ++j)
{
printf("%7.2f", h_C[i * WIDTH + j]);
}
printf("\n");
}

// Verify that the result vector is correct
for(int i = 0; i < HEIGHT; ++i)
{ for(int j = 0; j < WIDTH; ++j)
{
if (fabs(h_A[i * WIDTH + j] + h_B[i * WIDTH +j] - h_C[i* WIDTH + j]) > 1e-5)
{
fprintf(stderr, "Result verification failed at element %d!\n", i);
exit(EXIT_FAILURE);
}
}
}
printf("Test PASSED\n");

// Free device global memory
err = cudaFree(d_A);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to free device vector A (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

err = cudaFree(d_B);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to free device vector B (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

err = cudaFree(d_C);

if (err != cudaSuccess)
{
fprintf(stderr, "Failed to free device vector C (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

// Free host memory
free(h_A);
free(h_B);
free(h_C);

// Reset the device and exit
err = cudaDeviceReset();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to deinitialize the device!(error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}

printf("Done\n");
system("pause");
return 0;
}

执行结果

CUDA基础-实例演示-5

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[Bector addition of 72 elements]
Matrix A:
1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00
3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00
4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00
5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00
6.00 6.00 6.00 6.00 6.00 6.00 6.00 6.00 6.00
7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00
8.00 8.00 8.00 8.00 8.00 8.00 8.00 8.00 8.00
Matrix B:
1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00
3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00
4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00
5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00
6.00 6.00 6.00 6.00 6.00 6.00 6.00 6.00 6.00
7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00
8.00 8.00 8.00 8.00 8.00 8.00 8.00 8.00 8.00
Copy input data from the host memory to the CUDA device
Copy output data from the CUDA device in the host memory
Matrix C:
3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00
5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00
7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00
9.00 9.00 9.00 9.00 9.00 9.00 9.00 9.00 9.00
11.00 11.00 11.00 11.00 11.00 11.00 11.00 11.00 11.00
13.00 13.00 13.00 13.00 13.00 13.00 13.00 13.00 13.00
15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00
17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00
Test PASSED
Done
文章作者: HibisciDai
文章链接: http://hibiscidai.com/2020/06/10/CUDA基础-实例演示/
版权声明: 本博客所有文章除特别声明外,均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 HibisciDai
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