4.3 Reduction代码(Heterogeneous Parallel Programming class lab)

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;

}

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