4.2 Reduction优化

首先添加上Heterogeneous Parallel Programming class 中 lab: Reduction的代码：

myReduction.c

// MP Reduction
// Given a list (lst) of length n
// Output its sum = lst[0] + lst[1] + ... + lst[n-1];

#include    <wb.h>

#define BLOCK_SIZE 512 //@@ You can change this

#define wbCheck(stmt) do {                                                    \
        cudaError_t err = stmt;                                                       if (err != cudaSuccess) {                                                         wbLog(ERROR, "Failed to run stmt ", #stmt);                                   wbLog(ERROR, "Got CUDA error ...  ", cudaGetErrorString(err));                return -1;                                                                }                                                                         } while(0)

__global__ void reduction(float *g_idata, float *g_odata, unsigned int n){

    __shared__ float sdata[BLOCK_SIZE];

    // load shared mem
    unsigned int tid = threadIdx.x;
    unsigned int i = blockIdx.x*blockDim.x + threadIdx.x;

    sdata[tid] = (i < n) ? g_idata[i] : 0;

    __syncthreads();

    // do reduction in shared mem, stride is divided by 2,
    for (unsigned int s=blockDim.x/2; s>0; s>>=1)
    {
        //__syncthreads();
        if (tid < s)
        {
            sdata[tid] += sdata[tid + s];
        }

        __syncthreads();
    }

    // write result for this block to global mem
    if (tid == 0) g_odata[blockIdx.x] = sdata[0];

}

__global__ void total(float * input, float * output, int len) {
    //@@ Load a segment of the input vector into shared memory
    __shared__ float partialSum[2 * BLOCK_SIZE];  //blockDim.x is not okay, compile fail
    unsigned int t = threadIdx.x;
    unsigned int start = 2 * blockIdx.x * blockDim.x;
    if (start + t < len)
       partialSum[t] = input[start + t];
    else
       partialSum[t] = 0;

    if (start + blockDim.x + t < len)
       partialSum[blockDim.x + t] = input[start + blockDim.x + t];
    else
       partialSum[blockDim.x + t] = 0;

    //@@ Traverse the reduction tree
    for (unsigned int stride = blockDim.x; stride >= 1; stride >>= 1) {
       __syncthreads();
       if (t < stride)
          partialSum[t] += partialSum[t+stride];
    }
    //@@ Write the computed sum of the block to the output vector at the
    //@@ correct index
    if (t == 0)
       output[blockIdx.x] = partialSum[0];
}

int main(int argc, char ** argv) {
    int ii;
    wbArg_t args;
    float * hostInput; // The input 1D list
    float * hostOutput; // The output list
    float * deviceInput;
    float * deviceOutput;
    int numInputElements; // number of elements in the input list
    int numOutputElements; // number of elements in the output list

    args = wbArg_read(argc, argv);

    wbTime_start(Generic, "Importing data and creating memory on host");
    hostInput = (float *) wbImport(wbArg_getInputFile(args, 0), &numInputElements);

    numOutputElements = numInputElements / (BLOCK_SIZE);
    if (numInputElements % (BLOCK_SIZE)) {
        numOutputElements++;
    }

    //This for kernel total
    /*numOutputElements = numInputElements / (BLOCK_SIZE <<1);
    if (numInputElements % (BLOCK_SIZE)<<1) {
        numOutputElements++;
    } */
    hostOutput = (float*) malloc(numOutputElements * sizeof(float));

    wbTime_stop(Generic, "Importing data and creating memory on host");

    wbLog(TRACE, "The number of input elements in the input is ", numInputElements);
    wbLog(TRACE, "The number of output elements in the input is ", numOutputElements);

    wbTime_start(GPU, "Allocating GPU memory.");
    //@@ Allocate GPU memory here
    cudaMalloc((void **) &deviceInput, numInputElements * sizeof(float));
    cudaMalloc((void **) &deviceOutput, numOutputElements * sizeof(float));

    wbTime_stop(GPU, "Allocating GPU memory.");

    wbTime_start(GPU, "Copying input memory to the GPU.");
    //@@ Copy memory to the GPU here
    cudaMemcpy(deviceInput,
               hostInput,
               numInputElements * sizeof(float),
               cudaMemcpyHostToDevice);

    wbTime_stop(GPU, "Copying input memory to the GPU.");
    //@@ Initialize the grid and block dimensions here
    dim3 dimGrid(numOutputElements, 1, 1);
    dim3 dimBlock(BLOCK_SIZE, 1, 1);

    wbTime_start(Compute, "Performing CUDA computation");
    //@@ Launch the GPU Kernel here
    reduction<<<dimGrid,dimBlock>>>(deviceInput, deviceOutput, numInputElements);
    //total<<<dimGrid, dimBlock>>>(deviceInput, deviceOutput, numInputElements);
    cudaDeviceSynchronize();
    wbTime_stop(Compute, "Performing CUDA computation");

    wbTime_start(Copy, "Copying output memory to the CPU");
    //@@ Copy the GPU memory back to the CPU here
    cudaMemcpy(hostOutput, deviceOutput, sizeof(float) * numOutputElements, cudaMemcpyDeviceToHost);
    wbTime_stop(Copy, "Copying output memory to the CPU");

    /********************************************************************
     * Reduce output vector on the host
     * NOTE: One could also perform the reduction of the output vector
     * recursively and support any size input. For simplicity, we do not
     * require that for this lab.
     ********************************************************************/
    for (ii = 1; ii < numOutputElements; ii++) {
        hostOutput[0] += hostOutput[ii];
    }

    wbTime_start(GPU, "Freeing GPU Memory");
    //@@ Free the GPU memory here
    cudaFree(deviceInput);
    cudaFree(deviceOutput);

    wbTime_stop(GPU, "Freeing GPU Memory");

    wbSolution(args, hostOutput, 1);

    free(hostInput);
    free(hostOutput);

    return 0;
}

Reduction优化：