4.3 Reduction代码(Heterogeneous Parallel Programming class lab)

// 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;
}