Suche nach Minimum-Wert in Array und seinen Index mit CUDA __shfl_down Funktion

Question

Ich schreibe eine Funktion, die den minimalen Wert und den Index finden, bei dem Wert ein 1D-Array mit CUDA gefunden wurde.

Ich begann mit der Änderung des Reduktionscodes zum Auffinden der Summe der Werte in 1d-Array. Der Code funktioniert gut für die Summenfunktion, aber ich bin nicht in der Lage, es zum Minimieren zu bringen. Ich füge den Code in der Nachricht an, wenn es einen Cuda Guru gibt, bitte zeige den Fehler, den ich mache.

Die tatsächliche Funktion ist unter und im Testbeispiel Array-Größe ist 1024. So verwende ich shuffle reduction Teil. Problem ist die Ausgabewerte in g_oIdxs (gibt den Index) pro Block und g_odata (gibt den Mindestwert) pro Block ist falsch im Vergleich zu einfachen sequentiellen CPU-Code.

Auch Werte in g_odata sind alle Null (0), wenn ich es in Host drucken.

Vielen Dank im Voraus! Diese

#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

ist keine gültige Art und Weise zu verwenden, dynamisch zugewiesen Shared Memory:

T *sdata = SharedMemory<T>(); 

int *sdataIdx = SharedMemory<int>(); 

diese beiden Zeiger (sdata und sdataIdx) wird am Ende zeigt auf die gleichen Lage. (Die documentation erläutert, wie Sie damit richtig umgehen.)

Und obwohl Sie jetzt doppelt so viel gemeinsam genutzten Speicher (für die Speicherung von min plus Index) verwenden möchten, haben Sie die dynamische Zuordnungsgröße im Vergleich zu den vorherige Summenreduzierung nur Code:

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

In Ihrem Fall threads 256 und Sie Zuweisung genug (threads*sizeof(T)) für die min Lagerung, aber nichts für den Index der Lagerung.

Wir können "reparieren" diese Probleme, indem sie die folgenden Änderungen vornehmen:

T *sdata = SharedMemory<T>(); 

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

und:

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

Mit diesen Änderungen, Ihr Code erzeugt erwartete Ausgabe:

$ 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 
$ 

(Ich habe die Schleife auskommentiert, die alle 1024 Anfangswerte ausgedruckt hat)