montecarlo_reduction.cuh

采用GPU通用计算API（CUDA）实现蒙特卡罗方程。

/*
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 * documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
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 */



#ifndef MONTECARLO_REDUCTION_CUH
#define MONTECARLO_REDUCTION_CUH



// Needed by the optimized sum reduction for correct execution in device emulation
#ifdef __DEVICE_EMULATION__
    #define EMUSYNC __syncthreads()
#else
    #define EMUSYNC
#endif

template<class T, unsigned int blockSize>
__device__ void sumReduceSharedMem(T *sum, T *sum2, int tid)
{
    // do reduction in shared mem
    if (blockSize >= 512) { if (tid < 256) { sum[tid] += sum[tid + 256]; sum2[tid] += sum2[tid + 256]; } __syncthreads(); }
    if (blockSize >= 256) { if (tid < 128) { sum[tid] += sum[tid + 128]; sum2[tid] += sum2[tid + 128]; } __syncthreads(); }
    if (blockSize >= 128) { if (tid <  64) { sum[tid] += sum[tid +  64]; sum2[tid] += sum2[tid +  64]; } __syncthreads(); }
    
#ifndef __DEVICE_EMULATION__
    if (tid < 32)
#endif
    {
        if (blockSize >=  64) { sum[tid] += sum[tid + 32]; sum2[tid] += sum2[tid + 32]; EMUSYNC; }
        if (blockSize >=  32) { sum[tid] += sum[tid + 16]; sum2[tid] += sum2[tid + 16]; EMUSYNC; }
        if (blockSize >=  16) { sum[tid] += sum[tid +  8]; sum2[tid] += sum2[tid +  8]; EMUSYNC; }
        if (blockSize >=   8) { sum[tid] += sum[tid +  4]; sum2[tid] += sum2[tid +  4]; EMUSYNC; }
        if (blockSize >=   4) { sum[tid] += sum[tid +  2]; sum2[tid] += sum2[tid +  2]; EMUSYNC; }
        if (blockSize >=   2) { sum[tid] += sum[tid +  1]; sum2[tid] += sum2[tid +  1]; EMUSYNC; }
    }
}

////////////////////////////////////////////////////////////////////////////////
// This function calculates total sum for each of the two input arrays.
// SUM_N must be power of two
// Unrolling provides a bit of a performance improvement for small
// to medium path counts.
////////////////////////////////////////////////////////////////////////////////
#define UNROLL_REDUCTION

template<class T, int SUM_N, int blockSize> 
__device__ void sumReduce(T *sum, T *sum2)
{
#ifdef UNROLL_REDUCTION
    for(int pos = threadIdx.x; pos < SUM_N; pos += blockSize){
        __syncthreads();
        sumReduceSharedMem<T, blockSize>(sum, sum2, pos);
    }
#else
    for(int stride = SUM_N / 2; stride > 0; stride >>= 1){
        __syncthreads();
        for(int pos = threadIdx.x; pos < stride; pos += blockSize){
            sum[pos] += sum[pos + stride];
            sum2[pos] += sum2[pos + stride];
        }
    }
#endif
}



#endif