Coursera MP4

以后再改吧= =

// MP 4 Reduction
// Given a list (lst) of length n
// Output its sum = lst[0] + lst[1] + ... + lst[n-1];
// Due Tuesday, January 15, 2013 at 11:59 p.m. PST

#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);    \
            return -1;                                     \
        }                                                  \
    } while(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];
    unsigned int t=threadIdx.x;
    unsigned int start=2*blockIdx.x*BLOCK_SIZE;
    if((start+t)<len){
        partialSum[t]=input[start+t];
    }else partialSum[t]=0.0;
    if((start+BLOCK_SIZE+t)<len){partialSum[BLOCK_SIZE+t]=input[start+BLOCK_SIZE+t];}
    else partialSum[BLOCK_SIZE+t]=0.0;
    
    
    //@@ Traverse the reduction tree
    for(unsigned int stride=BLOCK_SIZE;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
    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<<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
    wbCheck(cudaMalloc((void**)&deviceInput,numInputElements*sizeof(float)));
    wbCheck(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 dimBlock(numOutputElements,1,1);
    dim3 dimGrid(BLOCK_SIZE,1,1);
    wbTime_start(Compute, "Performing CUDA computation");
    //@@ Launch the GPU Kernel here
    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,numOutputElements*sizeof(float),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;
}