[C++知识库]CUDA C编程 避免分支分化的九种并行规约方式

#include "../common/common.h"
#include <cuda_runtime.h>
#include <stdio.h>

/*
 * This code implements the interleaved and neighbor-paired approaches to
 * parallel reduction in CUDA. For this example, the sum operation is used. A
 * variety of optimizations on parallel reduction aimed at reducing divergence
 * are also demonstrated, such as unrolling.
 */

// Recursive Implementation of Interleaved Pair Approach
int recursiveReduce(int *data, int const size)
{
    // terminate check
    if (size == 1) return data[0];

    // renew the stride
    int const stride = size / 2;

    // in-place reduction
    for (int i = 0; i < stride; i++)
    {
        data[i] += data[i + stride];
    }

    // call recursively
    return recursiveReduce(data, stride);
}

// Neighbored Pair Implementation with divergence
__global__ void reduceNeighbored (int *g_idata, int *g_odata, unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x;

    // boundary check
    if (idx >= n) return;

    // in-place reduction in global memory
    for (int stride = 1; stride < blockDim.x; stride *= 2)
    {
        if ((tid % (2 * stride)) == 0)
        {
            idata[tid] += idata[tid + stride];
        }

        // synchronize within threadblock
        __syncthreads();
    }

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

// Neighbored Pair Implementation with less divergence
__global__ void reduceNeighboredLess (int *g_idata, int *g_odata,
                                      unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x;

    // boundary check
    if(idx >= n) return;

    // in-place reduction in global memory
    for (int stride = 1; stride < blockDim.x; stride *= 2)
    {
        // convert tid into local array index
        int index = 2 * stride * tid;

        if (index < blockDim.x)
        {
            idata[index] += idata[index + stride];
        }

        // synchronize within threadblock
        __syncthreads();
    }

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

// Interleaved Pair Implementation with less divergence
__global__ void reduceInterleaved (int *g_idata, int *g_odata, unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x;

    // boundary check
    if(idx >= n) return;

    // in-place reduction in global memory
    // 两个元素间的跨度被初始化为线程块的一半，然后在每次循环中减少一半
    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1)  
    {
        if (tid < stride)  //强制线程块中的前半部分线程执行求和操作
        {
            idata[tid] += idata[tid + stride];
        }

        __syncthreads();
    }

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

__global__ void reduceUnrolling2 (int *g_idata, int *g_odata, unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x * 2 + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x * 2;

    // unrolling 2
    // 每个线程都添加一个来自相邻数据块的元素
    if (idx + blockDim.x < n) g_idata[idx] += g_idata[idx + blockDim.x];

    __syncthreads();

    // in-place reduction in global memory
    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1)
    {
        if (tid < stride)
        {
            idata[tid] += idata[tid + stride];
        }

        // synchronize within threadblock
        __syncthreads();
    }

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

__global__ void reduceUnrolling4 (int *g_idata, int *g_odata, unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x * 4 + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x * 4;

    // unrolling 4
    if (idx + 3 * blockDim.x < n)
    {
        int a1 = g_idata[idx];
        int a2 = g_idata[idx + blockDim.x];
        int a3 = g_idata[idx + 2 * blockDim.x];
        int a4 = g_idata[idx + 3 * blockDim.x];
        g_idata[idx] = a1 + a2 + a3 + a4;
    }

    __syncthreads();

    // in-place reduction in global memory
    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1)
    {
        if (tid < stride)
        {
            idata[tid] += idata[tid + stride];
        }

        // synchronize within threadblock
        __syncthreads();
    }

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

__global__ void reduceUnrolling8 (int *g_idata, int *g_odata, unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x * 8 + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x * 8;

    // unrolling 8
    if (idx + 7 * blockDim.x < n)
    {
        int a1 = g_idata[idx];
        int a2 = g_idata[idx + blockDim.x];
        int a3 = g_idata[idx + 2 * blockDim.x];
        int a4 = g_idata[idx + 3 * blockDim.x];
        int b1 = g_idata[idx + 4 * blockDim.x];
        int b2 = g_idata[idx + 5 * blockDim.x];
        int b3 = g_idata[idx + 6 * blockDim.x];
        int b4 = g_idata[idx + 7 * blockDim.x];
        g_idata[idx] = a1 + a2 + a3 + a4 + b1 + b2 + b3 + b4;
    }

    __syncthreads();

    // in-place reduction in global memory
    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1)
    {
        if (tid < stride)
        {
            idata[tid] += idata[tid + stride];
        }

        // synchronize within threadblock
        __syncthreads();
    }

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

__global__ void reduceUnrollWarps8 (int *g_idata, int *g_odata, unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x * 8 + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x * 8;

    // unrolling 8
    if (idx + 7 * blockDim.x < n)
    {
        int a1 = g_idata[idx];
        int a2 = g_idata[idx + blockDim.x];
        int a3 = g_idata[idx + 2 * blockDim.x];
        int a4 = g_idata[idx + 3 * blockDim.x];
        int b1 = g_idata[idx + 4 * blockDim.x];
        int b2 = g_idata[idx + 5 * blockDim.x];
        int b3 = g_idata[idx + 6 * blockDim.x];
        int b4 = g_idata[idx + 7 * blockDim.x];
        g_idata[idx] = a1 + a2 + a3 + a4 + b1 + b2 + b3 + b4;
    }

    __syncthreads();

    // in-place reduction in global memory
    for (int stride = blockDim.x / 2; stride > 32; stride >>= 1)
    {
        if (tid < stride)
        {
            idata[tid] += idata[tid + stride];
        }

        // synchronize within threadblock
        __syncthreads();
    }

    // unrolling warp
    if (tid < 32)
    {
        volatile int *vmem = idata;  // 由volatile修饰符修饰的变量会每次从内存中读数据，而不是从寄存器读数据
        vmem[tid] += vmem[tid + 32];
        vmem[tid] += vmem[tid + 16];
        vmem[tid] += vmem[tid +  8];
        vmem[tid] += vmem[tid +  4];
        vmem[tid] += vmem[tid +  2];
        vmem[tid] += vmem[tid +  1];
    }

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

__global__ void reduceCompleteUnrollWarps8 (int *g_idata, int *g_odata,
        unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x * 8 + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x * 8;

    // unrolling 8
    if (idx + 7 * blockDim.x < n)
    {
        int a1 = g_idata[idx];
        int a2 = g_idata[idx + blockDim.x];
        int a3 = g_idata[idx + 2 * blockDim.x];
        int a4 = g_idata[idx + 3 * blockDim.x];
        int b1 = g_idata[idx + 4 * blockDim.x];
        int b2 = g_idata[idx + 5 * blockDim.x];
        int b3 = g_idata[idx + 6 * blockDim.x];
        int b4 = g_idata[idx + 7 * blockDim.x];
        g_idata[idx] = a1 + a2 + a3 + a4 + b1 + b2 + b3 + b4;
    }

    __syncthreads();

    // in-place reduction and complete unroll
    if (blockDim.x >= 1024 && tid < 512) idata[tid] += idata[tid + 512];

    __syncthreads();

    if (blockDim.x >= 512 && tid < 256) idata[tid] += idata[tid + 256];

    __syncthreads();

    if (blockDim.x >= 256 && tid < 128) idata[tid] += idata[tid + 128];

    __syncthreads();

    if (blockDim.x >= 128 && tid < 64) idata[tid] += idata[tid + 64];

    __syncthreads();

    // unrolling warp
    if (tid < 32)
    {
        volatile int *vsmem = idata;
        vsmem[tid] += vsmem[tid + 32];
        vsmem[tid] += vsmem[tid + 16];
        vsmem[tid] += vsmem[tid +  8];
        vsmem[tid] += vsmem[tid +  4];
        vsmem[tid] += vsmem[tid +  2];
        vsmem[tid] += vsmem[tid +  1];
    }

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

template <unsigned int iBlockSize>
__global__ void reduceCompleteUnroll(int *g_idata, int *g_odata,
                                     unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x * 8 + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x * 8;

    // unrolling 8
    if (idx + 7 * blockDim.x < n)
    {
        int a1 = g_idata[idx];
        int a2 = g_idata[idx + blockDim.x];
        int a3 = g_idata[idx + 2 * blockDim.x];
        int a4 = g_idata[idx + 3 * blockDim.x];
        int b1 = g_idata[idx + 4 * blockDim.x];
        int b2 = g_idata[idx + 5 * blockDim.x];
        int b3 = g_idata[idx + 6 * blockDim.x];
        int b4 = g_idata[idx + 7 * blockDim.x];
        g_idata[idx] = a1 + a2 + a3 + a4 + b1 + b2 + b3 + b4;
    }

    __syncthreads();

    // in-place reduction and complete unroll
    if (iBlockSize >= 1024 && tid < 512) idata[tid] += idata[tid + 512];

    __syncthreads();

    if (iBlockSize >= 512 && tid < 256)  idata[tid] += idata[tid + 256];

    __syncthreads();

    if (iBlockSize >= 256 && tid < 128)  idata[tid] += idata[tid + 128];

    __syncthreads();

    if (iBlockSize >= 128 && tid < 64)   idata[tid] += idata[tid + 64];

    __syncthreads();

    // unrolling warp
    if (tid < 32)
    {
        volatile int *vsmem = idata;
        vsmem[tid] += vsmem[tid + 32];
        vsmem[tid] += vsmem[tid + 16];
        vsmem[tid] += vsmem[tid +  8];
        vsmem[tid] += vsmem[tid +  4];
        vsmem[tid] += vsmem[tid +  2];
        vsmem[tid] += vsmem[tid +  1];
    }

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

__global__ void reduceUnrollWarps (int *g_idata, int *g_odata, unsigned int n)
{
    // set thread ID
    unsigned int tid = threadIdx.x;
    unsigned int idx = blockIdx.x * blockDim.x * 2 + threadIdx.x;

    // convert global data pointer to the local pointer of this block
    int *idata = g_idata + blockIdx.x * blockDim.x * 2;

    // unrolling 2
    if (idx + blockDim.x < n) g_idata[idx] += g_idata[idx + blockDim.x];

    __syncthreads();

    // in-place reduction in global memory
    for (int stride = blockDim.x / 2; stride > 32; stride >>= 1)
    {
        if (tid < stride)
        {
            idata[tid] += idata[tid + stride];
        }

        // synchronize within threadblock
        __syncthreads();
    }

    // unrolling last warp
    if (tid < 32)
    {
        volatile int *vsmem = idata;
        vsmem[tid] += vsmem[tid + 32];
        vsmem[tid] += vsmem[tid + 16];
        vsmem[tid] += vsmem[tid +  8];
        vsmem[tid] += vsmem[tid +  4];
        vsmem[tid] += vsmem[tid +  2];
        vsmem[tid] += vsmem[tid +  1];
    }

    if (tid == 0) g_odata[blockIdx.x] = idata[0];
}

int main(int argc, char **argv)
{
    // 设置设备
    int dev = 0;
    cudaDeviceProp deviceProp;
    CHECK(cudaGetDeviceProperties(&deviceProp, dev));
    printf("%s starting reduction at ", argv[0]);
    printf("device %d: %s ", dev, deviceProp.name);
    CHECK(cudaSetDevice(dev));

    bool bResult = false;

    // 初始化
    int size = 1 << 24; // 包含16M个元素
    printf("    with array size %d  ", size);

    // execution configuration
    int blocksize = 512;   // 初始化block的大小

    if(argc > 1)
    {
        blocksize = atoi(argv[1]);   // block size from command line argument
    }

    dim3 block (blocksize, 1);  //一维块
    dim3 grid  ((size + block.x - 1) / block.x, 1);  //一维网格
    printf("grid %d block %d\n", grid.x, block.x);

    // 分配主机内存
    size_t bytes = size * sizeof(int);
    int *h_idata = (int *) malloc(bytes);
    int *h_odata = (int *) malloc(grid.x * sizeof(int));
    int *tmp     = (int *) malloc(bytes);

    // 初始化数组
    for (int i = 0; i < size; i++)
    {
        // mask off high 2 bytes to force max number to 255
        h_idata[i] = (int)( rand() & 0xFF );
    }

    memcpy (tmp, h_idata, bytes); // 复制h_idata到tmp

    double iStart, iElaps;
    int gpu_sum = 0;

    // 分配设备内存
    int *d_idata = NULL;
    int *d_odata = NULL;
    CHECK(cudaMalloc((void **) &d_idata, bytes));
    CHECK(cudaMalloc((void **) &d_odata, grid.x * sizeof(int)));

    // 计算在cpu上的耗时
    iStart = seconds();
    int cpu_sum = recursiveReduce (tmp, size);
    iElaps = seconds() - iStart;
    printf("cpu reduce      elapsed %f sec cpu_sum: %d\n", iElaps, cpu_sum);

    // 核函数 1: reduceNeighbored
    // 相邻配对规约方式
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();
    reduceNeighbored<<<grid, block>>>(d_idata, d_odata, size);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x * sizeof(int),
                     cudaMemcpyDeviceToHost));  //将结果从device上拷贝回来
    gpu_sum = 0;

    for (int i = 0; i < grid.x; i++) gpu_sum += h_odata[i];  //在host上完成串行求和
    
    printf("gpu Neighbored  elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x, block.x);

    // 核函数 2: reduceNeighbored with less divergence
    // 改善的相邻配对规约方式
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();
    reduceNeighboredLess<<<grid, block>>>(d_idata, d_odata, size);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x * sizeof(int),
                     cudaMemcpyDeviceToHost));
    gpu_sum = 0;

    for (int i = 0; i < grid.x; i++) gpu_sum += h_odata[i];

    printf("gpu NeighboredL elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x, block.x);

    // 核函数 3: reduceInterleaved
    // 交错配对规约内核
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();

    reduceInterleaved<<<grid, block>>>(d_idata, d_odata, size);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x * sizeof(int),
                     cudaMemcpyDeviceToHost));
    gpu_sum = 0;

    for (int i = 0; i < grid.x; i++) gpu_sum += h_odata[i];

    printf("gpu Interleaved elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x, block.x);

    // kernel 4: reduceUnrolling2
    // 展开的规约
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();
    reduceUnrolling2<<<grid.x / 2, block>>>(d_idata, d_odata, size);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x / 2 * sizeof(int),
                     cudaMemcpyDeviceToHost));
    gpu_sum = 0;

    for (int i = 0; i < grid.x / 2; i++) gpu_sum += h_odata[i];

    printf("gpu Unrolling2  elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x / 2, block.x);

    // kernel 5: reduceUnrolling4
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();
    reduceUnrolling4<<<grid.x / 4, block>>>(d_idata, d_odata, size);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x / 4 * sizeof(int),
                     cudaMemcpyDeviceToHost));
    gpu_sum = 0;

    for (int i = 0; i < grid.x / 4; i++) gpu_sum += h_odata[i];

    printf("gpu Unrolling4  elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x / 4, block.x);

    // kernel 6: reduceUnrolling8
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();
    reduceUnrolling8<<<grid.x / 8, block>>>(d_idata, d_odata, size);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x / 8 * sizeof(int),
                     cudaMemcpyDeviceToHost));
    gpu_sum = 0;

    for (int i = 0; i < grid.x / 8; i++) gpu_sum += h_odata[i];

    printf("gpu Unrolling8  elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x / 8, block.x);

    for (int i = 0; i < grid.x / 16; i++) gpu_sum += h_odata[i];

    // kernel 8: reduceUnrollWarps8
    // 展开线程的规约
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();
    reduceUnrollWarps8<<<grid.x / 8, block>>>(d_idata, d_odata, size);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x / 8 * sizeof(int),
                     cudaMemcpyDeviceToHost));
    gpu_sum = 0;

    for (int i = 0; i < grid.x / 8; i++) gpu_sum += h_odata[i];

    printf("gpu UnrollWarp8 elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x / 8, block.x);


    // kernel 9: reduceCompleteUnrollWarsp8
    // 完全展开的规约
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();
    reduceCompleteUnrollWarps8<<<grid.x / 8, block>>>(d_idata, d_odata, size);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x / 8 * sizeof(int),
                     cudaMemcpyDeviceToHost));
    gpu_sum = 0;

    for (int i = 0; i < grid.x / 8; i++) gpu_sum += h_odata[i];

    printf("gpu Cmptnroll8  elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x / 8, block.x);

    // kernel 9: reduceCompleteUnroll
    // 模板函数的规约
    CHECK(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
    CHECK(cudaDeviceSynchronize());
    iStart = seconds();

    switch (blocksize)
    {
    case 1024:
        reduceCompleteUnroll<1024><<<grid.x / 8, block>>>(d_idata, d_odata,
                size);
        break;

    case 512:
        reduceCompleteUnroll<512><<<grid.x / 8, block>>>(d_idata, d_odata,
                size);
        break;

    case 256:
        reduceCompleteUnroll<256><<<grid.x / 8, block>>>(d_idata, d_odata,
                size);
        break;

    case 128:
        reduceCompleteUnroll<128><<<grid.x / 8, block>>>(d_idata, d_odata,
                size);
        break;

    case 64:
        reduceCompleteUnroll<64><<<grid.x / 8, block>>>(d_idata, d_odata, size);
        break;
    }

    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    CHECK(cudaMemcpy(h_odata, d_odata, grid.x / 8 * sizeof(int),
                     cudaMemcpyDeviceToHost));

    gpu_sum = 0;

    for (int i = 0; i < grid.x / 8; i++) gpu_sum += h_odata[i];

    printf("gpu Cmptnroll   elapsed %f sec gpu_sum: %d <<<grid %d block "
           "%d>>>\n", iElaps, gpu_sum, grid.x / 8, block.x);

    // 释放host内存
    free(h_idata);
    free(h_odata);

    // 释放device内存
    CHECK(cudaFree(d_idata));
    CHECK(cudaFree(d_odata));

    // reset device
    CHECK(cudaDeviceReset());

    // check the results
    bResult = (gpu_sum == cpu_sum);

    if(!bResult) printf("Test failed!\n");

    return EXIT_SUCCESS;
}

编译：

nvcc reduceInteger.cu -o reduceInteger

执行：./reduceIntege

输出：

?

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