GPU-CUDA编程实践(一)
2017-03-23 14:21
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CUDA编程是有一定的流程和套路的
图1 CUDA程序流程
常用CUDA函数说明
1.__host__ cudaError_t cudaMalloc(void **devPtr, size_t size)
该函数主要用来分配设备上的内存(即显存中的内存)。该函数被声明为了__host__,即表示被host所调用,即在cpu中执行的代码所调用。
返回值:为cudaError_t类型,实质为cudaError的枚举类型,其中定义了一系列的错误代码。如果函数调用成功,则返回cudaSuccess。
第一个参数,void ** 类型,devPtr:用于接受该函数所分配的内存地址
第二个参数,size_t类型,size:指定分配内存的大小,单位为字
2.
__host__ cudaError_t cudaFree(void *devPtr)
该函数用来释放先前在设备上申请的内存空间(通过cudaMalloc、cudaMallocPitch等函数),注意,不能释放通过标准库函数malloc进行申请的内存。
返回值:为错误代码的类型值
第一个参数,void**类型,devPtr:指向需要释放的设备内存地址
3.
__host__ cudaError_t cudaMemcpy(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind)
该函数主要用于将不同内存段的数据进行拷贝,内存可用是设备内存,也可用是主机内存
第一个参数,void*类型,dst:为目的内存地址
第二个参数,const void *类型,src:源内存地址
第三个参数,size_t类型,count:将要进行拷贝的字节大小
第四个参数,enum cudaMemcpyKind类型,kind:拷贝的类型,决定拷贝的方向
cudaMemcpyKind类型如下:
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enum __device_builtin__ cudaMemcpyKind
{
cudaMemcpyHostToHost = 0, /**< Host -> Host */
cudaMemcpyHostToDevice = 1, /**< Host -> Device */
cudaMemcpyDeviceToHost = 2, /**< Device -> Host */
cudaMemcpyDeviceToDevice = 3, /**< Device -> Device */
cudaMemcpyDefault = 4 /**< Default based unified virtual address space */
};
cudaMemcpyKind决定了拷贝的方向,即是从主机的内存拷贝至设备内存,还是将设备内存拷贝值主机内存等。cudaMemcpy内部根据拷贝的类型(kind)来决定调用以下的某个函数:
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::cudaMemcpyHostToHost,
::cudaMemcpyHostToDevice,
::cudaMemcpyDeviceToHost,
::cudaMemcpyDeviceToDevice
4.
__host__ cudaError_t cudaDeviceReset(void)
该函数销毁当前进程中当前设备上所有的内存分配和重置所有状态,调用该函数达到重新初始该设备的作用。应该注意,在调用该函数时,应该确保该进程中其他host线程不能访问该设备!
下面是一个简单的向量相加的程序
/** * Copyright 1993-2015 NVIDIA Corporation. All rights reserved. * * Please refer to the NVIDIA end user license agreement (EULA) associated * with this source code for terms and conditions that govern your use of * this software. Any use, reproduction, disclosure, or distribution of * this software and related documentation outside the terms of the EULA * is strictly prohibited. * */ /** * Vector addition: C = A + B. * * This sample is a very basic sample that implements element by element * vector addition. It is the same as the sample illustrating Chapter 2 * of the programming guide with some additions like error checking. */ #include <stdio.h> // For the CUDA runtime routines (prefixed with "cuda_") #include <cuda_runtime.h> #include <helper_cuda.h> /** * CUDA Kernel Device code * * Computes the vector addition of A and B into C. The 3 vectors have the same * number of elements numElements. */ __global__ void vectorAdd(const float *A, const float *B, float *C, int numElements) { int i = blockDim.x * blockIdx.x + threadIdx.x; if (i < numElements) { C[i] = A[i] + B[i]; } } /** * Host main routine */ 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 = 50000; size_t size = numElements * sizeof(float); printf("[Vector addition of %d elements]\n", numElements); // Allocate the host input vector A float *h_A = (float *)malloc(size); // Allocate the host input vector B float *h_B = (float *)malloc(size); // Allocate the host output 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); } // Initialize the host input vectors for (int i = 0; i < numElements; ++i) { h_A[i] = rand()/(float)RAND_MAX; h_B[i] = rand()/(float)RAND_MAX; } // 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 output 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 = 256; int blocksPerGrid =(numElements + threadsPerBlock - 1) / threadsPerBlock; printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock); vectorAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, numElements); err = cudaGetLastError(); 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 to the host result vector // in host memory. printf("Copy output data from the CUDA device to 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); } // Verify that the result vector is correct for (int i = 0; i < numElements; ++i) { if (fabs(h_A[i] + h_B[i] - h_C[i]) > 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); printf("Done\n"); return 0; }
目前CUDA和OpenCL是最主流的两个GPU编程库,CUDA和OpenCL都是原生支持C/C++的,其它语言想要访问还有些麻烦,比如Java,需要通过JNI来访问CUDA或者OpenCL。基于JNI,现今有各种Java版本的GPU编程库,比如JCUDA等。另一种思路就是语言还是由java来编写,通过一种工具将java转换成C。
图2 GPU编程库
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