Поиск минимального значения в массиве и его индекс с помощью функции CUDA __shfl_down

Question

Я пишу функцию, которая найдет минимальное значение и индекс, по которому было найдено 1D-массив с использованием CUDA.

Я начал с изменения кода восстановления для нахождения суммы значений в массиве 1d. Код работает нормально для функции sum, но я не могу заставить его работать для поиска минимума. Я прилагаю код в сообщении, если есть какой-либо гуру-гуда, пожалуйста, укажите ошибку, которую я делаю.

Фактическая функция приведена ниже, а в тестовом примере размер массива - 1024. Таким образом, я использую часть для перетасовки. Проблема заключается в значениях out put в g_oIdxs (дает индекс) для каждого блока, а g_odata (дает минимальное значение) на блок неверно по сравнению с простым последовательным кодом ЦП.

Также значения в g_odata равны нулю (0), когда я печатаю его на хосте.

Заранее благодарен!

#include <stdio.h> 
#include <stdlib.h> 
#include <cuda.h> 
#include <math.h> 
#include <unistd.h> 
#include <sys/time.h> 

#if __DEVICE_EMULATION__ 
#define DEBUG_SYNC __syncthreads(); 
#else 
#define DEBUG_SYNC 
#endif 

#ifndef MIN 
#define MIN(x,y) ((x < y) ? x : y) 
#endif 

#ifndef MIN_IDX 
#define MIN_IDX(x,y, idx_x, idx_y) ((x < y) ? idx_x : idx_y) 
#endif 

#if (__CUDA_ARCH__ < 200) 
#define int_mult(x,y) __mul24(x,y) 
#else 
#define int_mult(x,y) x*y 
#endif 

#define inf 0x7f800000 

bool isPow2(unsigned int x) 
{ 
    return ((x&(x-1))==0); 
} 

unsigned int nextPow2(unsigned int x) 
{ 
    --x; 
    x |= x >> 1; 
    x |= x >> 2; 
    x |= x >> 4; 
    x |= x >> 8; 
    x |= x >> 16; 
    return ++x; 
} 

// Utility class used to avoid linker errors with extern 
// unsized shared memory arrays with templated type 
template<class T> 
struct SharedMemory { 
    __device__ inline operator T *() { 
     extern __shared__ int __smem[]; 
     return (T *) __smem; 
    } 

    __device__ inline operator const T *() const { 
     extern __shared__ int __smem[]; 
     return (T *) __smem; 
    } 
}; 

// specialize for double to avoid unaligned memory 
// access compile errors 
template<> 
struct SharedMemory<double> { 
    __device__ inline operator double *() { 
     extern __shared__ double __smem_d[]; 
     return (double *) __smem_d; 
    } 

    __device__ inline operator const double *() const { 
     extern __shared__ double __smem_d[]; 
     return (double *) __smem_d; 
    } 
}; 

/* 
This version adds multiple elements per thread sequentially. This reduces the overall 
cost of the algorithm while keeping the work complexity O(n) and the step complexity O(log n). 
(Brent's Theorem optimization) 

Note, this kernel needs a minimum of 64*sizeof(T) bytes of shared memory. 
In other words if blockSize <= 32, allocate 64*sizeof(T) bytes. 
If blockSize > 32, allocate blockSize*sizeof(T) bytes. 
*/ 
template<class T, unsigned int blockSize, bool nIsPow2> 
__global__ void reduce6(T *g_idata, T *g_odata, unsigned int n) { 
    T *sdata = SharedMemory<T>(); 

    // perform first level of reduction, 
    // reading from global memory, writing to shared memory 
    unsigned int tid = threadIdx.x; 
    unsigned int i = blockIdx.x * blockSize * 2 + threadIdx.x; 
    unsigned int gridSize = blockSize * 2 * gridDim.x; 

    T mySum = 0; 

    // we reduce multiple elements per thread. The number is determined by the 
    // number of active thread blocks (via gridDim). More blocks will result 
    // in a larger gridSize and therefore fewer elements per thread 
    while (i < n) { 
     mySum += g_idata[i]; 

     // ensure we don't read out of bounds -- this is optimized away for powerOf2 sized arrays 
     if (nIsPow2 || i + blockSize < n) 
      mySum += g_idata[i + blockSize]; 

     i += gridSize; 
    } 

    // each thread puts its local sum into shared memory 
    sdata[tid] = mySum; 
    __syncthreads(); 

    // do reduction in shared mem 
    if ((blockSize >= 512) && (tid < 256)) { 
     sdata[tid] = mySum = mySum + sdata[tid + 256]; 
    } 

    __syncthreads(); 

    if ((blockSize >= 256) && (tid < 128)) { 
     sdata[tid] = mySum = mySum + sdata[tid + 128]; 
    } 

    __syncthreads(); 

    if ((blockSize >= 128) && (tid < 64)) { 
     sdata[tid] = mySum = mySum + sdata[tid + 64]; 
    } 

    __syncthreads(); 

#if (__CUDA_ARCH__ >= 300) 
    if (tid < 32) { 
     // Fetch final intermediate sum from 2nd warp 
     if (blockSize >= 64) 
      mySum += sdata[tid + 32]; 
     // Reduce final warp using shuffle 
     for (int offset = warpSize/2; offset > 0; offset /= 2) { 
      mySum += __shfl_down(mySum, offset); 
     } 
    } 
#else 
    // fully unroll reduction within a single warp 
    if ((blockSize >= 64) && (tid < 32)) 
    { 
     sdata[tid] = mySum = mySum + sdata[tid + 32]; 
    } 

    __syncthreads(); 

    if ((blockSize >= 32) && (tid < 16)) 
    { 
     sdata[tid] = mySum = mySum + sdata[tid + 16]; 
    } 

    __syncthreads(); 

    if ((blockSize >= 16) && (tid < 8)) 
    { 
     sdata[tid] = mySum = mySum + sdata[tid + 8]; 
    } 

    __syncthreads(); 

    if ((blockSize >= 8) && (tid < 4)) 
    { 
     sdata[tid] = mySum = mySum + sdata[tid + 4]; 
    } 

    __syncthreads(); 

    if ((blockSize >= 4) && (tid < 2)) 
    { 
     sdata[tid] = mySum = mySum + sdata[tid + 2]; 
    } 

    __syncthreads(); 

    if ((blockSize >= 2) && (tid < 1)) 
    { 
     sdata[tid] = mySum = mySum + sdata[tid + 1]; 
    } 

    __syncthreads(); 
#endif 

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


/* 
This version adds multiple elements per thread sequentially. This reduces the overall 
cost of the algorithm while keeping the work complexity O(n) and the step complexity O(log n). 
(Brent's Theorem optimization) 

Note, this kernel needs a minimum of 64*sizeof(T) bytes of shared memory. 
In other words if blockSize <= 32, allocate 64*sizeof(T) bytes. 
If blockSize > 32, allocate blockSize*sizeof(T) bytes. 
*/ 
template<class T, unsigned int blockSize, bool nIsPow2> 
__global__ void reduceMin6(T *g_idata, int *g_idxs, T *g_odata, int *g_oIdxs, unsigned int n) { 
    T *sdata = SharedMemory<T>(); 

    int *sdataIdx = SharedMemory<int>(); 

    // perform first level of reduction, 
    // reading from global memory, writing to shared memory 
    unsigned int tid = threadIdx.x; 
    unsigned int i = blockIdx.x * blockSize * 2 + threadIdx.x; 
    unsigned int gridSize = blockSize * 2 * gridDim.x; 


    T myMin = 99999; 
    int myMinIdx = -1; 
    // we reduce multiple elements per thread. The number is determined by the 
    // number of active thread blocks (via gridDim). More blocks will result 
    // in a larger gridSize and therefore fewer elements per thread 
    while (i < n) { 
     myMinIdx = MIN_IDX(g_idata[i], myMin, g_idxs[i], myMinIdx); 
     myMin = MIN(g_idata[i], myMin); 

     // ensure we don't read out of bounds -- this is optimized away for powerOf2 sized arrays 
     if (nIsPow2 || i + blockSize < n){ 
      //myMin += g_idata[i + blockSize]; 
      myMinIdx = MIN_IDX(g_idata[i + blockSize], myMin, g_idxs[i + blockSize], myMinIdx); 
      myMin = MIN(g_idata[i + blockSize], myMin); 
     } 

     i += gridSize; 
    } 

    // each thread puts its local sum into shared memory 
    sdata[tid] = myMin; 
    sdataIdx[tid] = myMinIdx; 
    __syncthreads(); 

    // do reduction in shared mem 
    if ((blockSize >= 512) && (tid < 256)) { 
     //sdata[tid] = mySum = mySum + sdata[tid + 256]; 

     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 256], myMin, sdataIdx[tid + 256], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 256], myMin); 
    } 

    __syncthreads(); 

    if ((blockSize >= 256) && (tid < 128)) { 
     //sdata[tid] = myMin = myMin + sdata[tid + 128]; 

     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 128], myMin, sdataIdx[tid + 128], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 128], myMin); 
    } 

    __syncthreads(); 

    if ((blockSize >= 128) && (tid < 64)) { 
     //sdata[tid] = myMin = myMin + sdata[tid + 64]; 

     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 64], myMin, sdataIdx[tid + 64], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 64], myMin); 
    } 

    __syncthreads(); 

#if (__CUDA_ARCH__ >= 300) 
    if (tid < 32) { 
     // Fetch final intermediate sum from 2nd warp 
     if (blockSize >= 64){ 
     //myMin += sdata[tid + 32]; 
      myMinIdx = MIN_IDX(sdata[tid + 32], myMin, sdataIdx[tid + 32], myMinIdx); 
      myMin = MIN(sdata[tid + 32], myMin); 
     } 
     // Reduce final warp using shuffle 
     for (int offset = warpSize/2; offset > 0; offset /= 2) { 
      //myMin += __shfl_down(myMin, offset); 
      int tempMyMinIdx = __shfl_down(myMinIdx, offset); 
      float tempMyMin = __shfl_down(myMin, offset); 

      myMinIdx = MIN_IDX(tempMyMin, myMin, tempMyMinIdx , myMinIdx); 
      myMin = MIN(tempMyMin, myMin); 
     } 

    } 
#else 
    // fully unroll reduction within a single warp 
    if ((blockSize >= 64) && (tid < 32)) 
    { 
     //sdata[tid] = myMin = myMin + sdata[tid + 32]; 
     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 32], myMin, sdataIdx[tid + 32], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 32], myMin); 
    } 

    __syncthreads(); 

    if ((blockSize >= 32) && (tid < 16)) 
    { 
     //sdata[tid] = myMin = myMin + sdata[tid + 16]; 

     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 16], myMin, sdataIdx[tid + 16], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 16], myMin); 
    } 

    __syncthreads(); 

    if ((blockSize >= 16) && (tid < 8)) 
    { 
     //sdata[tid] = myMin = myMin + sdata[tid + 8]; 

     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 8], myMin, sdataIdx[tid + 8], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 8], myMin); 
    } 

    __syncthreads(); 

    if ((blockSize >= 8) && (tid < 4)) 
    { 
     //sdata[tid] = myMin = myMin + sdata[tid + 4]; 

     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 4], myMin, sdataIdx[tid + 4], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 4], myMin); 
    } 

    __syncthreads(); 

    if ((blockSize >= 4) && (tid < 2)) 
    { 
     //sdata[tid] = myMin = myMin + sdata[tid + 2]; 
     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 2], myMin, sdataIdx[tid + 2], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 2], myMin); 
    } 

    __syncthreads(); 

    if ((blockSize >= 2) && (tid < 1)) 
    { 
     //sdata[tid] = myMin = myMin + sdata[tid + 1]; 
     sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 1], myMin, sdataIdx[tid + 1], myMinIdx); 
     sdata[tid] = myMin = MIN(sdata[tid + 1], myMin); 
    } 

    __syncthreads(); 
#endif 

    __syncthreads(); 
    // write result for this block to global mem 
    if (tid == 0){ 
     g_odata[blockIdx.x] = myMin; 
     g_oIdxs[blockIdx.x] = myMinIdx; 
    } 
} 

//////////////////////////////////////////////////////////////////////////////// 
// Compute the number of threads and blocks to use for the given reduction kernel 
// For the kernels >= 3, we set threads/block to the minimum of maxThreads and 
// n/2. For kernels < 3, we set to the minimum of maxThreads and n. For kernel 
// 6, we observe the maximum specified number of blocks, because each thread in 
// that kernel can process a variable number of elements. 
//////////////////////////////////////////////////////////////////////////////// 
void getNumBlocksAndThreads(int whichKernel, int n, int maxBlocks, 
     int maxThreads, int &blocks, int &threads) { 

    //get device capability, to avoid block/grid size exceed the upper bound 
    cudaDeviceProp prop; 
    int device; 
    cudaGetDevice(&device); 
    cudaGetDeviceProperties(&prop, device); 

    if (whichKernel < 3) { 
     threads = (n < maxThreads) ? nextPow2(n) : maxThreads; 
     blocks = (n + threads - 1)/threads; 
    } else { 
     threads = (n < maxThreads * 2) ? nextPow2((n + 1)/2) : maxThreads; 
     blocks = (n + (threads * 2 - 1))/(threads * 2); 
    } 

    if ((float) threads * blocks 
      > (float) prop.maxGridSize[0] * prop.maxThreadsPerBlock) { 
     printf("n is too large, please choose a smaller number!\n"); 
    } 

    if (blocks > prop.maxGridSize[0]) { 
     printf(
       "Grid size <%d> exceeds the device capability <%d>, set block size as %d (original %d)\n", 
       blocks, prop.maxGridSize[0], threads * 2, threads); 

     blocks /= 2; 
     threads *= 2; 
    } 

    if (whichKernel == 6) { 
     blocks = MIN(maxBlocks, blocks); 
    } 
} 

//////////////////////////////////////////////////////////////////////////////// 
// Wrapper function for kernel launch 
//////////////////////////////////////////////////////////////////////////////// 
template<class T> 
void reduce(int size, int threads, int blocks, int whichKernel, T *d_idata, 
     T *d_odata) { 
    dim3 dimBlock(threads, 1, 1); 
    dim3 dimGrid(blocks, 1, 1); 

    // when there is only one warp per block, we need to allocate two warps 
    // worth of shared memory so that we don't index shared memory out of bounds 
    int smemSize = 
      (threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T); 

    if (isPow2(size)) { 
     switch (threads) { 
     case 512: 
      reduce6<T, 512, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 256: 
      reduce6<T, 256, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 128: 
      reduce6<T, 128, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 64: 
      reduce6<T, 64, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 32: 
      reduce6<T, 32, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 16: 
      reduce6<T, 16, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 8: 
      reduce6<T, 8, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 4: 
      reduce6<T, 4, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 2: 
      reduce6<T, 2, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 1: 
      reduce6<T, 1, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 
     } 
    } else { 
     switch (threads) { 
     case 512: 
      reduce6<T, 512, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 256: 
      reduce6<T, 256, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 128: 
      reduce6<T, 128, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 64: 
      reduce6<T, 64, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 32: 
      reduce6<T, 32, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 16: 
      reduce6<T, 16, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 8: 
      reduce6<T, 8, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 4: 
      reduce6<T, 4, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 2: 
      reduce6<T, 2, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 

     case 1: 
      reduce6<T, 1, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, 
        d_odata, size); 
      break; 
     } 
    } 

} 


//////////////////////////////////////////////////////////////////////////////// 
// Wrapper function for kernel launch 
//////////////////////////////////////////////////////////////////////////////// 
template<class T> 
void reduceMin(int size, int threads, int blocks, int whichKernel, T *d_idata, 
     T *d_odata, int *idxs, int *oIdxs) { 
    dim3 dimBlock(threads, 1, 1); 
    dim3 dimGrid(blocks, 1, 1); 

    // when there is only one warp per block, we need to allocate two warps 
    // worth of shared memory so that we don't index shared memory out of bounds 
    int smemSize = 
      (threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T); 

    if (isPow2(size)) { 
     switch (threads) { 
     case 512: 
      reduceMin6<T, 512, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 256: 
      reduceMin6<T, 256, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 128: 
      reduceMin6<T, 128, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 64: 
      reduceMin6<T, 64, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 32: 
      reduceMin6<T, 32, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 16: 
      reduceMin6<T, 16, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 8: 
      reduceMin6<T, 8, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 4: 
      reduceMin6<T, 4, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 2: 
      reduceMin6<T, 2, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 1: 
      reduceMin6<T, 1, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 
     } 
    } else { 
     switch (threads) { 
     case 512: 
      reduceMin6<T, 512, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 256: 
      reduceMin6<T, 256, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 128: 
      reduceMin6<T, 128, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 64: 
      reduceMin6<T, 64, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 32: 
      reduceMin6<T, 32, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 16: 
      reduceMin6<T, 16, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 8: 
      reduceMin6<T, 8, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 4: 
      reduceMin6<T, 4, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 2: 
      reduceMin6<T, 2, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 

     case 1: 
      reduceMin6<T, 1, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs, 
        d_odata, oIdxs, size); 
      break; 
     } 
    } 

} 

//////////////////////////////////////////////////////////////////////////////// 
//! Compute sum reduction on CPU 
//! We use Kahan summation for an accurate sum of large arrays. 
//! http://en.wikipedia.org/wiki/Kahan_summation_algorithm 
//! 
//! @param data  pointer to input data 
//! @param size  number of input data elements 
//////////////////////////////////////////////////////////////////////////////// 
template<class T> 
void reduceMINCPU(T *data, int size, T *min, int *idx) 
{ 
    *min = data[0]; 
    int min_idx = 0; 
    T c = (T)0.0; 

    for (int i = 1; i < size; i++) 
    { 
     T y = data[i]; 
     T t = MIN(*min, y); 
     min_idx = MIN_IDX(*min, y, min_idx, i); 
     (*min) = t; 
    } 

    *idx = min_idx; 

    return; 
} 

//////////////////////////////////////////////////////////////////////////////// 
//! Compute sum reduction on CPU 
//! We use Kahan summation for an accurate sum of large arrays. 
//! http://en.wikipedia.org/wiki/Kahan_summation_algorithm 
//! 
//! @param data  pointer to input data 
//! @param size  number of input data elements 
//////////////////////////////////////////////////////////////////////////////// 
template<class T> 
T reduceCPU(T *data, int size) 
{ 
    T sum = data[0]; 
    T c = (T)0.0; 

    for (int i = 1; i < size; i++) 
    { 
     T y = data[i] - c; 
     T t = sum + y; 
     c = (t - sum) - y; 
     sum = t; 
    } 

    return sum; 
} 

// Instantiate the reduction function for 3 types 
template void 
reduce<int>(int size, int threads, int blocks, int whichKernel, int *d_idata, 
     int *d_odata); 

template void 
reduce<float>(int size, int threads, int blocks, int whichKernel, 
     float *d_idata, float *d_odata); 

template void 
reduce<double>(int size, int threads, int blocks, int whichKernel, 
     double *d_idata, double *d_odata); 

// Instantiate the reduction function for 3 types 
template void 
reduceMin<int>(int size, int threads, int blocks, int whichKernel, int *d_idata, 
     int *d_odata, int *idxs, int *oIdxs); 

template void 
reduceMin<float>(int size, int threads, int blocks, int whichKernel, float *d_idata, 
     float *d_odata, int *idxs, int *oIdxs); 

template void 
reduceMin<double>(int size, int threads, int blocks, int whichKernel, double *d_idata, 
     double *d_odata, int *idxs, int *oIdxs); 

unsigned long long int my_min_max_test(int num_els) { 

    // timers 

    unsigned long long int start; 
    unsigned long long int delta; 

    int maxThreads = 256; // number of threads per block 
    int whichKernel = 6; 
    int maxBlocks = 64; 

    int testIterations = 100; 



    float* d_in = NULL; 
    float* d_out = NULL; 
    int *d_idxs = NULL; 
    int *d_oIdxs = NULL; 

    printf("%d elements\n", num_els); 
    printf("%d threads (max)\n", maxThreads); 

    int numBlocks = 0; 
    int numThreads = 0; 
    getNumBlocksAndThreads(whichKernel, num_els, maxBlocks, maxThreads, numBlocks, 
      numThreads); 



    // in[1024] = 34.0f; 
    // in[333] = 55.0f; 
    // in[23523] = -42.0f; 

// cudaMalloc((void**) &d_in, size); 
// cudaMalloc((void**) &d_out, size); 
// cudaMalloc((void**) &d_idxs, num_els * sizeof(int)); 

    cudaMalloc((void **) &d_in, num_els * sizeof(float)); 
    cudaMalloc((void **) &d_idxs, num_els * sizeof(int)); 
    cudaMalloc((void **) &d_out, numBlocks * sizeof(float)); 
    cudaMalloc((void **) &d_oIdxs, numBlocks * sizeof(int)); 

    float* in = (float*) malloc(num_els * sizeof(float)); 
    int *idxs = (int*) malloc(num_els * sizeof(int)); 
    float* out = (float*) malloc(numBlocks * sizeof(float)); 
    int* oIdxs = (int*) malloc(numBlocks * sizeof(int)); 

    for (int i = 0; i < num_els; i++) { 
     in[i] = (double) rand()/(double) RAND_MAX; 
     idxs[i] = i; 
    } 

    for (int i = 0; i < num_els; i++) { 

     printf("\n [%d] = %.4f", idxs[i], in[i]); 
    } 

    // copy data directly to device memory 
    cudaMemcpy(d_in, in, num_els * sizeof(float), cudaMemcpyHostToDevice); 
    cudaMemcpy(d_idxs, idxs, num_els * sizeof(int),cudaMemcpyHostToDevice); 
    cudaMemcpy(d_out, out, numBlocks * sizeof(float),cudaMemcpyHostToDevice); 
    cudaMemcpy(d_oIdxs, oIdxs, numBlocks * sizeof(int),cudaMemcpyHostToDevice); 

    // warm-up 
// reduce<float>(num_els, numThreads, numBlocks, whichKernel, d_in, d_out); 
// 
// cudaMemcpy(out, d_out, numBlocks * sizeof(float), cudaMemcpyDeviceToHost); 
// 
// for(int i=0; i< numBlocks; i++) 
// printf("\nFinal Result[BLK:%d]: %f", i, out[i]); 

// printf("\n Reduce CPU : %f", reduceCPU<float>(in, num_els)); 

    reduceMin<float>(num_els, numThreads, numBlocks, whichKernel, d_in, d_out, d_idxs, d_oIdxs); 

    cudaMemcpy(out, d_out, numBlocks * sizeof(float), cudaMemcpyDeviceToHost); 
    cudaMemcpy(oIdxs, d_oIdxs, numBlocks * sizeof(int), cudaMemcpyDeviceToHost); 

    for(int i=0; i< numBlocks; i++) 
     printf("\n Reduce MIN GPU idx: %d value: %f", oIdxs[i], out[i]); 


    int min_idx; 
    float min; 
    reduceMINCPU<float>(in, num_els, &min, &min_idx); 

    printf("\n\n Reduce MIN CPU idx: %d value: %f", min_idx, min); 

    cudaFree(d_in); 
    cudaFree(d_out); 
    cudaFree(d_idxs); 

    free(in); 
    free(out); 

    //system("pause"); 

    return delta; 

} 

int main(int argc, char* argv[]) { 

    printf(" GTS250 @ 70.6 GB/s - Finding min and max"); 
    printf("\n N \t\t [GB/s] \t [perc] \t [usec] \t test \n"); 

//#pragma unroll 
//for(int i = 1024*1024; i <= 32*1024*1024; i=i*2) 
//{ 
// my_min_max_test(i); 
//} 

    printf("\n Non-base 2 tests! \n"); 
    printf("\n N \t\t [GB/s] \t [perc] \t [usec] \t test \n"); 

    my_min_max_test(1024); 



// just some large numbers.... 
//my_min_max_test(14*1024*1024+38); 
//my_min_max_test(14*1024*1024+55); 
//my_min_max_test(18*1024*1024+1232); 
//my_min_max_test(7*1024*1024+94854); 

//for(int i = 0; i < 4; i++) 
//{ 
// 
// float ratio = float(rand())/float(RAND_MAX); 
// ratio = ratio >= 0 ? ratio : -ratio; 
// int big_num = ratio*18*1e6; 
// 
// my_min_max_test(big_num); 
//} 

    return 0; 
} 

Answer 1

Это не является корректным способом использования динамически выделяемой разделяемой памяти:

T *sdata = SharedMemory<T>(); 

int *sdataIdx = SharedMemory<int>(); 

оба этих указателей (sdata и sdataIdx) будет указывать на же места. (documentation обсуждает, как правильно это сделать.)

И хотя вы теперь хотите использовать вдвое большую общую память (для хранения индекса min plus), вы не увеличили размер динамического размещения по сравнению с предыдущая сумма-восстановительный только код:

int smemSize = 
     (threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T); 

В вашем случае, threads 256, и вы выделение достаточно (threads*sizeof(T)) для хранения мин, но ничего для хранения индекса.

Мы можем "исправить" эти проблемы путем внесения следующих изменений:

T *sdata = SharedMemory<T>(); 

int *sdataIdx = ((int *)sdata) + blockSize; 

и:

int smemSize = 
     (threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T); 
smemSize += threads*sizeof(int); 

С учетом этих изменений, ваш код производит ожидаемый результат:

$ cuda-memcheck ./t1182 
========= CUDA-MEMCHECK 
GTS250 @ 70.6 GB/s - Finding min and max 
N [GB/s] [perc] [usec] test 

Non-base 2 tests! 

N [GB/s] [perc] [usec] test 
1024 elements 
256 threads (max) 

Reduce MIN GPU idx: 484 value: 0.001125 
Reduce MIN GPU idx: 972 value: 0.002828 

Reduce MIN CPU idx: 484 value: 0.001125 
========= ERROR SUMMARY: 0 errors 
$ 

(Я прокомментировал цикл, который печатал все 1024 начальных значения)