CUDA实例练习（十一）：零拷贝内存

      可以在CUDA C核函数中直接访问这种类型的主机内存。由于这种内存不需要复制到GPU，因此也称为零拷贝内存。

#include "book.h"
#include <stdio.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#define imin(a,b) (a<b?a:b)

const int N = 33 * 1024 * 1024;
const int threadsPerBlock = 256;
const int blocksPerGrid = imin(32, (N + threadsPerBlock - 1) / threadsPerBlock);

__global__ void dot(int size, float *a, float *b, float *c) {
    __shared__ float cache[threadsPerBlock];
    int tid = threadIdx.x + blockIdx.x * blockDim.x;
    int cacheIndex = threadIdx.x;

    float   temp = 0;
    while (tid < size) {
        temp += a[tid] * b[tid];
        tid += blockDim.x * gridDim.x;
    }

    // set the cache values
    cache[cacheIndex] = temp;

    // synchronize threads in this block
    __syncthreads();

    // for reductions, threadsPerBlock must be a power of 2
    // because of the following code
    int i = blockDim.x / 2;
    while (i != 0) {
        if (cacheIndex < i)
            cache[cacheIndex] += cache[cacheIndex + i];
        __syncthreads();
        i /= 2;
    }

    if (cacheIndex == 0)
        c[blockIdx.x] = cache[0];
}

//点积运算的主机内存版本
float malloc_test(int size) {
    cudaEvent_t start, stop;
    float *a, *b,c, *partial_c;
    float *dev_a, *dev_b, *dev_partial_c;
    float elapsedTime;

    HANDLE_ERROR(cudaEventCreate(&start));
    HANDLE_ERROR(cudaEventCreate(&stop));

    //在CPU上分配内存
    a = (float *)malloc(size * sizeof(float));
    b = (float *)malloc(size * sizeof(float));
    partial_c = (float *)malloc(blocksPerGrid * sizeof(float));

    //在GPU上分配内存
    HANDLE_ERROR(cudaMalloc((void **)&dev_a, size * sizeof(float)));
    HANDLE_ERROR(cudaMalloc((void **)&dev_b, size * sizeof(float)));
    HANDLE_ERROR(cudaMalloc((void **)&dev_partial_c, blocksPerGrid * sizeof(float)));
    
    //用数据填充主机内存
    for (int i = 0; i < size; i++){
        a[i] = i;
        b[i] = i * 2;
    }

    HANDLE_ERROR(cudaEventRecord(start, 0));
    //将数组'a'和'b'复制到GPU
    HANDLE_ERROR(cudaMemcpy(dev_a, a, size * sizeof(float),
        cudaMemcpyHostToDevice));
    HANDLE_ERROR(cudaMemcpy(dev_b, b, size * sizeof(float),
        cudaMemcpyHostToDevice));
    dot << <blocksPerGrid, threadsPerBlock >> >(size, dev_a, dev_b, dev_partial_c);

    //将数组'c'从GPU复制到CPU
    HANDLE_ERROR(cudaMemcpy(partial_c, dev_partial_c,
        blocksPerGrid * sizeof(float), cudaMemcpyDeviceToHost));
    HANDLE_ERROR(cudaEventRecord(stop, 0));
    HANDLE_ERROR(cudaEventSynchronize(stop));
    HANDLE_ERROR(cudaEventElapsedTime(&elapsedTime, start, stop));

    //结束CPU上的计算
    c = 0;
    for (int i = 0; i < blocksPerGrid; i++){
        c += partial_c[i];
    }
    HANDLE_ERROR(cudaFree(dev_a));
    HANDLE_ERROR(cudaFree(dev_b));
    HANDLE_ERROR(cudaFree(dev_partial_c));

    //释放CPU上的内存
    free(a);
    free(b);
    free(partial_c);

    //释放事件
    HANDLE_ERROR(cudaEventDestroy(start));
    HANDLE_ERROR(cudaEventDestroy(stop));

    printf("Value calculated: %f
", c);

    return elapsedTime;
}

//点积运算的零拷贝内存版本
float cuda_host_alloc_test(int size){
    cudaEvent_t start, stop;
    float *a, *b, c, *partial_c;
    float *dev_a, *dev_b, *dev_partial_c;
    float elapsedTime;

    HANDLE_ERROR(cudaEventCreate(&start));
    HANDLE_ERROR(cudaEventCreate(&stop));

    //在CPU上分配内存
    HANDLE_ERROR(cudaHostAlloc((void **)&a, size * sizeof(float),
        cudaHostAllocWriteCombined | cudaHostAllocMapped));
    HANDLE_ERROR(cudaHostAlloc((void **)&b, size * sizeof(float),
        cudaHostAllocWriteCombined | cudaHostAllocMapped));
    HANDLE_ERROR(cudaHostAlloc((void **)&partial_c,
        blocksPerGrid * sizeof(float), cudaHostAllocMapped));

    //用数据填充主机内存
    for (int i = 0; i < size; i++){
        a[i] = i;
        b[i] = i * 2;
    }
    // find out the GPU pointers
    HANDLE_ERROR(cudaHostGetDevicePointer(&dev_a, a, 0));
    HANDLE_ERROR(cudaHostGetDevicePointer(&dev_b, b, 0));
    HANDLE_ERROR(cudaHostGetDevicePointer(&dev_partial_c,
        partial_c, 0));

    // fill in the host memory with data
    for (int i = 0; i<size; i++) {
        a[i] = i;
        b[i] = i * 2;
    }

    HANDLE_ERROR(cudaEventRecord(start, 0));

    dot << <blocksPerGrid, threadsPerBlock >> >(size, dev_a, dev_b,
        dev_partial_c);

    HANDLE_ERROR(cudaThreadSynchronize());
    HANDLE_ERROR(cudaEventRecord(stop, 0));
    HANDLE_ERROR(cudaEventSynchronize(stop));
    HANDLE_ERROR(cudaEventElapsedTime(&elapsedTime,
        start, stop));

    // finish up on the CPU side
    c = 0;
    for (int i = 0; i<blocksPerGrid; i++) {
        c += partial_c[i];
    }

    HANDLE_ERROR(cudaFreeHost(a));
    HANDLE_ERROR(cudaFreeHost(b));
    HANDLE_ERROR(cudaFreeHost(partial_c));

    // free events
    HANDLE_ERROR(cudaEventDestroy(start));
    HANDLE_ERROR(cudaEventDestroy(stop));

    printf("Value calculated:  %f
", c);

    return elapsedTime;
}

int main(void) {
    cudaDeviceProp  prop;
    int whichDevice;
    HANDLE_ERROR(cudaGetDevice(&whichDevice));
    HANDLE_ERROR(cudaGetDeviceProperties(&prop, whichDevice));
    if (prop.canMapHostMemory != 1) {
        printf("Device can not map memory.
");
        return 0;
    }

    float           elapsedTime;

    HANDLE_ERROR(cudaSetDeviceFlags(cudaDeviceMapHost));

    // try it with malloc
    elapsedTime = malloc_test(N);
    printf("Time using cudaMalloc:  %3.1f ms
",
        elapsedTime);

    // now try it with cudaHostAlloc
    elapsedTime = cuda_host_alloc_test(N);
    printf("Time using cudaHostAlloc:  %3.1f ms
",
        elapsedTime);
}