6

我正在尝试编写一个函数，该函数采用一块未排序的键/值对，例如

<7, 4>
<2, 8>
<3, 1>
<2, 2>
<1, 5>
<7, 1>
<3, 8>
<7, 2>

并通过键对它们进行排序，同时减少具有相同键的对的值：

<1, 5>
<2, 10>
<3, 9>
<7, 7>

目前，我正在使用一个__device__类似下面的函数，它本质上是一种双调排序，它将组合相同键的值并将旧数据设置为无限大的值（仅99现在使用），以便后续的双调排序将筛选将它们移到底部并按int *删除的值将数组切割。

__device__ void interBitonicSortReduce(int2 *sdata, int tid, int recordNum, int *removed) {
  int n = MIN(DEFAULT_DIMBLOCK, recordNum);
  for (int k = 2; k <= n; k *= 2) {
    for (int j = k / 2; j > 0; j /= 2) {
      int ixj = tid ^ j;
      if (ixj > tid) {
        if (sdata[tid].x == sdata[ixj].x && sdata[tid].x < 99) {
          atomicAdd(&sdata[tid].y, sdata[ixj].y);
          sdata[ixj].x = 99; 
          sdata[ixj].y = 99; 
          atomicAdd(removed, 1); 
        }   
        if ((tid & k) == 0 && sdata[tid].x > sdata[ixj].x)
          swapData2(sdata[tid], sdata[ixj]);
        if ((tid & k) != 0 && sdata[tid].x < sdata[ixj].x)
          swapData2(sdata[tid], sdata[ixj]);
        __syncthreads();
      }   
    }   
  }
}

这适用于小数据集，但对于较大的数据集（尽管仍在单个块的大小内），单个调用将无法做到这一点。

尝试将排序和归约结合在同一个函数中是否明智？显然，该函数需要多次调用，但是否可以根据其大小准确确定需要调用多少次才能耗尽所有数据？

或者我应该用这样的东西单独进行减少：

__device__ int interReduce(int2 *sdata, int tid) {
  int index = tid;
  while (sdata[index].x == sdata[tid].x) {
    index--;
    if (index < 0)
      break;
  }
  if (index+1 != tid) {
    atomicAdd(&sdata[index+1].y, sdata[tid].y);
    sdata[tid].x = 99;
    sdata[tid].y = 99;
    return 1;
  }
  return 0;
}

我正在尝试提出最有效的解决方案，但我在 CUDA 和并行算法方面的经验有限。

4

4 回答 4

5

您可以使用推力来执行此操作。

使用推力::sort_by_key 后跟推力::reduce_by_key

这是一个例子：

#include <iostream>
#include <thrust/device_vector.h>
#include <thrust/copy.h>
#include <thrust/sort.h>
#include <thrust/reduce.h>
#include <thrust/sequence.h>

#define N 12
typedef thrust::device_vector<int>::iterator dintiter;
int main(){

  thrust::device_vector<int> keys(N);
  thrust::device_vector<int> values(N);
  thrust::device_vector<int> new_keys(N);
  thrust::device_vector<int> new_values(N);
  thrust::sequence(keys.begin(), keys.end());
  thrust::sequence(values.begin(), values.end());

  keys[3] = 1;
  keys[9] = 1;
  keys[8] = 2;
  keys[7] = 4;

  thrust::sort_by_key(keys.begin(), keys.end(), values.begin());
  thrust::pair<dintiter, dintiter> new_end;
  new_end = thrust::reduce_by_key(keys.begin(), keys.end(), values.begin(), new_keys.begin(), new_values.begin());

  std::cout << "results  values:" << std::endl;
  thrust::copy(new_values.begin(), new_end.second, std::ostream_iterator<int>( std::cout, " "));
  std::cout << std::endl << "results keys:" << std::endl;
  thrust::copy(new_keys.begin(), new_end.first, std::ostream_iterator<int>( std::cout, " "));
  std::cout << std::endl;

  return 0;
}
于 2013-07-11T16:09:52.353 回答
1

从您的帖子来看，您似乎需要按键对许多小数组进行排序。引用自己：

这适用于小数据集，但对于较大的数据集（尽管仍在单个块的大小内），单个调用将无法做到这一点。

下面您将找到一个完整的示例，该示例围绕我对在 CUDA 中对许多小数组进行排序并使用cub::BlockRadixSort的答案构建。

#include <cub/cub.cuh>
#include <stdio.h>
#include <stdlib.h>

#include "Utilities.cuh"

using namespace cub;

/**********************************/
/* CUB BLOCKSORT KERNEL NO SHARED */
/**********************************/
template <int BLOCK_THREADS, int ITEMS_PER_THREAD>
__global__ void BlockSortKernel(float *d_values, int *d_keys, float *d_values_result, int *d_keys_result)
{
    // --- Specialize BlockLoad, BlockStore, and BlockRadixSort collective types
    typedef cub::BlockLoad      <int*,   BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_TRANSPOSE>  BlockLoadIntT;
    typedef cub::BlockLoad      <float*, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_TRANSPOSE>  BlockLoadFloatT;
    typedef cub::BlockStore     <int*,   BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_STORE_TRANSPOSE> BlockStoreIntT;
    typedef cub::BlockStore     <float*, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_STORE_TRANSPOSE> BlockStoreFloatT;
    typedef cub::BlockRadixSort <int ,   BLOCK_THREADS, ITEMS_PER_THREAD, float>                 BlockRadixSortT;

    // --- Allocate type-safe, repurposable shared memory for collectives
    __shared__ union {
        typename BlockLoadIntT      ::TempStorage loadInt;
        typename BlockLoadFloatT    ::TempStorage loadFloat;
        typename BlockStoreIntT     ::TempStorage storeInt;
        typename BlockStoreFloatT   ::TempStorage storeFloat;
        typename BlockRadixSortT    ::TempStorage sort;
    } temp_storage;

    // --- Obtain this block's segment of consecutive keys (blocked across threads)
    int   thread_keys[ITEMS_PER_THREAD];
    float thread_values[ITEMS_PER_THREAD];
    int block_offset = blockIdx.x * (BLOCK_THREADS * ITEMS_PER_THREAD);

    BlockLoadIntT(temp_storage.loadInt).Load(d_keys   + block_offset, thread_keys);
    BlockLoadFloatT(temp_storage.loadFloat).Load(d_values + block_offset, thread_values);
    __syncthreads(); 

    // --- Collectively sort the keys
    BlockRadixSortT(temp_storage.sort).SortBlockedToStriped(thread_keys, thread_values);
    __syncthreads(); 

    // --- Store the sorted segment
    BlockStoreIntT(temp_storage.storeInt).Store(d_keys_result   + block_offset, thread_keys);
    BlockStoreFloatT(temp_storage.storeFloat).Store(d_values_result + block_offset, thread_values);

}

/*******************************/
/* CUB BLOCKSORT KERNEL SHARED */
/*******************************/
template <int BLOCK_THREADS, int ITEMS_PER_THREAD>
__global__ void shared_BlockSortKernel(float *d_values, int *d_keys, float *d_values_result, int *d_keys_result)
{
    // --- Shared memory allocation
    __shared__ float sharedMemoryArrayValues[BLOCK_THREADS * ITEMS_PER_THREAD];
    __shared__ int   sharedMemoryArrayKeys[BLOCK_THREADS * ITEMS_PER_THREAD];

    // --- Specialize BlockStore and BlockRadixSort collective types
    typedef cub::BlockRadixSort <int , BLOCK_THREADS, ITEMS_PER_THREAD, float>  BlockRadixSortT;

    // --- Allocate type-safe, repurposable shared memory for collectives
    __shared__ typename BlockRadixSortT::TempStorage temp_storage;

    int block_offset = blockIdx.x * (BLOCK_THREADS * ITEMS_PER_THREAD);

    // --- Load data to shared memory
    for (int k = 0; k < ITEMS_PER_THREAD; k++) {
        sharedMemoryArrayValues[threadIdx.x * ITEMS_PER_THREAD + k] = d_values[block_offset + threadIdx.x * ITEMS_PER_THREAD + k];
        sharedMemoryArrayKeys[threadIdx.x * ITEMS_PER_THREAD + k]   = d_keys[block_offset + threadIdx.x * ITEMS_PER_THREAD + k];
    }
    __syncthreads();

    // --- Collectively sort the keys
    BlockRadixSortT(temp_storage).SortBlockedToStriped(*static_cast<int(*)  [ITEMS_PER_THREAD]>(static_cast<void*>(sharedMemoryArrayKeys   + (threadIdx.x * ITEMS_PER_THREAD))),
                                                       *static_cast<float(*)[ITEMS_PER_THREAD]>(static_cast<void*>(sharedMemoryArrayValues + (threadIdx.x * ITEMS_PER_THREAD))));
    __syncthreads();

    // --- Write data to shared memory
    for (int k = 0; k < ITEMS_PER_THREAD; k++) {
        d_values_result[block_offset + threadIdx.x * ITEMS_PER_THREAD + k] = sharedMemoryArrayValues[threadIdx.x * ITEMS_PER_THREAD + k];
        d_keys_result  [block_offset + threadIdx.x * ITEMS_PER_THREAD + k] = sharedMemoryArrayKeys  [threadIdx.x * ITEMS_PER_THREAD + k];
    }
}

/********/
/* MAIN */
/********/
int main() {

    const int numElemsPerArray  = 8;
    const int numArrays         = 4;
    const int N                 = numArrays * numElemsPerArray;
    const int numElemsPerThread = 4;

    const int RANGE             = N * numElemsPerThread;

    // --- Allocating and initializing the data on the host
    float *h_values = (float *)malloc(N * sizeof(float));
    int *h_keys     = (int *)  malloc(N * sizeof(int));
    for (int i = 0 ; i < N; i++) {
        h_values[i] = rand() % RANGE;
        h_keys[i]   = rand() % RANGE;
    }

    printf("Original\n\n");
    for (int k = 0; k < numArrays; k++) 
        for (int i = 0; i < numElemsPerArray; i++)
            printf("Array nr. %i; Element nr. %i; Key %i; Value %f\n", k, i, h_keys[k * numElemsPerArray + i], h_values[k * numElemsPerArray + i]);

    // --- Allocating the results on the host
    float *h_values_result1 = (float *)malloc(N * sizeof(float));
    float *h_values_result2 = (float *)malloc(N * sizeof(float));
    int   *h_keys_result1   = (int *)  malloc(N * sizeof(int));
    int   *h_keys_result2   = (int *)  malloc(N * sizeof(int));

    // --- Allocating space for data and results on device
    float *d_values;            gpuErrchk(cudaMalloc((void **)&d_values,         N * sizeof(float)));
    int   *d_keys;              gpuErrchk(cudaMalloc((void **)&d_keys,           N * sizeof(int)));
    float *d_values_result1;    gpuErrchk(cudaMalloc((void **)&d_values_result1, N * sizeof(float)));
    float *d_values_result2;    gpuErrchk(cudaMalloc((void **)&d_values_result2, N * sizeof(float)));
    int   *d_keys_result1;      gpuErrchk(cudaMalloc((void **)&d_keys_result1,   N * sizeof(int)));
    int   *d_keys_result2;      gpuErrchk(cudaMalloc((void **)&d_keys_result2,   N * sizeof(int)));

    // --- BlockSortKernel no shared
    gpuErrchk(cudaMemcpy(d_values, h_values, N * sizeof(float), cudaMemcpyHostToDevice));
    gpuErrchk(cudaMemcpy(d_keys,   h_keys,   N * sizeof(int),   cudaMemcpyHostToDevice));
    BlockSortKernel<N / numArrays / numElemsPerThread, numElemsPerThread><<<numArrays, numElemsPerArray / numElemsPerThread>>>(d_values, d_keys, d_values_result1, d_keys_result1); 
    gpuErrchk(cudaPeekAtLastError());
    gpuErrchk(cudaDeviceSynchronize());    
    gpuErrchk(cudaMemcpy(h_values_result1, d_values_result1, N * sizeof(float), cudaMemcpyDeviceToHost));
    gpuErrchk(cudaMemcpy(h_keys_result1,   d_keys_result1,   N * sizeof(int),   cudaMemcpyDeviceToHost));

    printf("\n\nBlockSortKernel no shared\n\n");
    for (int k = 0; k < numArrays; k++) 
        for (int i = 0; i < numElemsPerArray; i++)
            printf("Array nr. %i; Element nr. %i; Key %i; Value %f\n", k, i, h_keys_result1[k * numElemsPerArray + i], h_values_result1[k * numElemsPerArray + i]);

    // --- BlockSortKernel with shared
    gpuErrchk(cudaMemcpy(d_values, h_values, N * sizeof(float), cudaMemcpyHostToDevice));
    gpuErrchk(cudaMemcpy(d_keys,   h_keys,   N * sizeof(int),   cudaMemcpyHostToDevice));
    shared_BlockSortKernel<N / numArrays / numElemsPerThread, numElemsPerThread><<<numArrays, numElemsPerArray / numElemsPerThread>>>(d_values, d_keys, d_values_result2, d_keys_result2); 
    gpuErrchk(cudaPeekAtLastError());
    gpuErrchk(cudaDeviceSynchronize());    
    gpuErrchk(cudaMemcpy(h_values_result2, d_values_result2, N * sizeof(float), cudaMemcpyDeviceToHost));
    gpuErrchk(cudaMemcpy(h_keys_result2,   d_keys_result2,   N * sizeof(int),   cudaMemcpyDeviceToHost));

    printf("\n\nBlockSortKernel shared\n\n");
    for (int k = 0; k < numArrays; k++) 
        for (int i = 0; i < numElemsPerArray; i++)
            printf("Array nr. %i; Element nr. %i; Key %i; Value %f\n", k, i, h_keys_result2[k * numElemsPerArray + i], h_values_result2[k * numElemsPerArray + i]);

    return 0;
}
于 2016-01-05T08:49:32.013 回答
0

我最近遇到了将上述方法扩展到必须根据相同键对多个数组进行排序的情况的问题。

看来，由于它的原型，不可能cub::BlockRadixSort通过使用 zip 迭代器和元组“打包”数组来使用，请参阅C++ 在“打包”数组上操作。因此，我利用了引用帖子中建议的辅助索引方法。

这是我制定的示例：

#include <cub/cub.cuh>
#include <stdio.h>
#include <stdlib.h>

#include "Utilities.cuh"

using namespace cub;

/*******************************/
/* CUB BLOCKSORT KERNEL SHARED */
/*******************************/
template <int BLOCK_THREADS, int ITEMS_PER_THREAD>
__global__ void shared_BlockSortKernel(float *d_valuesA, float *d_valuesB, int *d_keys, float *d_values_resultA, float *d_values_resultB, int *d_keys_result)
{
    // --- Shared memory allocation
    __shared__ float sharedMemoryArrayValuesA[BLOCK_THREADS * ITEMS_PER_THREAD];
    __shared__ float sharedMemoryArrayValuesB[BLOCK_THREADS * ITEMS_PER_THREAD];
    __shared__ int   sharedMemoryArrayKeys[BLOCK_THREADS * ITEMS_PER_THREAD];
    __shared__ int   sharedMemoryHelperIndices[BLOCK_THREADS * ITEMS_PER_THREAD];

    // --- Specialize BlockStore and BlockRadixSort collective types
    typedef cub::BlockRadixSort <int , BLOCK_THREADS, ITEMS_PER_THREAD, int>    BlockRadixSortT;

    // --- Allocate type-safe, repurposable shared memory for collectives
    __shared__ typename BlockRadixSortT::TempStorage temp_storage;

    int block_offset = blockIdx.x * (BLOCK_THREADS * ITEMS_PER_THREAD);

    // --- Load data to shared memory
    for (int k = 0; k < ITEMS_PER_THREAD; k++) {
        sharedMemoryArrayValuesA [threadIdx.x * ITEMS_PER_THREAD + k] = d_valuesA[block_offset + threadIdx.x * ITEMS_PER_THREAD + k];
        sharedMemoryArrayValuesB [threadIdx.x * ITEMS_PER_THREAD + k] = d_valuesB[block_offset + threadIdx.x * ITEMS_PER_THREAD + k];
        sharedMemoryArrayKeys    [threadIdx.x * ITEMS_PER_THREAD + k] = d_keys   [block_offset + threadIdx.x * ITEMS_PER_THREAD + k];
        sharedMemoryHelperIndices[threadIdx.x * ITEMS_PER_THREAD + k] =                          threadIdx.x * ITEMS_PER_THREAD + k ;
    }
    __syncthreads();

    // --- Collectively sort the keys
    BlockRadixSortT(temp_storage).SortBlockedToStriped(*static_cast<int(*)[ITEMS_PER_THREAD]>(static_cast<void*>(sharedMemoryArrayKeys     + (threadIdx.x * ITEMS_PER_THREAD))),
                                                       *static_cast<int(*)[ITEMS_PER_THREAD]>(static_cast<void*>(sharedMemoryHelperIndices + (threadIdx.x * ITEMS_PER_THREAD))));
    __syncthreads();

    // --- Write data to shared memory
    for (int k = 0; k < ITEMS_PER_THREAD; k++) {
        d_values_resultA[block_offset + threadIdx.x * ITEMS_PER_THREAD + k] = sharedMemoryArrayValuesA[sharedMemoryHelperIndices[threadIdx.x * ITEMS_PER_THREAD + k]];
        d_values_resultB[block_offset + threadIdx.x * ITEMS_PER_THREAD + k] = sharedMemoryArrayValuesB[sharedMemoryHelperIndices[threadIdx.x * ITEMS_PER_THREAD + k]];
        d_keys_result   [block_offset + threadIdx.x * ITEMS_PER_THREAD + k] = sharedMemoryArrayKeys                             [threadIdx.x * ITEMS_PER_THREAD + k];
    }
}

/********/
/* MAIN */
/********/
int main() {

    const int numElemsPerArray  = 8;
    const int numArrays         = 4;
    const int N                 = numArrays * numElemsPerArray;
    const int numElemsPerThread = 4;

    const int RANGE             = N * numElemsPerThread;

    // --- Allocating and initializing the data on the host
    float *h_valuesA    = (float *)malloc(N * sizeof(float));
    float *h_valuesB    = (float *)malloc(N * sizeof(float));
    int *h_keys         = (int *)  malloc(N * sizeof(int));
    for (int i = 0 ; i < N; i++) {
        h_valuesA[i] = rand() % RANGE;
        h_valuesB[i] = rand() % RANGE;
        h_keys[i]    = rand() % RANGE;
    }

    printf("Original\n\n");
    for (int k = 0; k < numArrays; k++) 
        for (int i = 0; i < numElemsPerArray; i++)
            printf("Array nr. %i; Element nr. %i; Key %i; Value A %f; Value B %f\n", k, i, h_keys[k * numElemsPerArray + i], h_valuesA[k * numElemsPerArray + i], h_valuesB[k * numElemsPerArray + i]);

    // --- Allocating the results on the host
    float *h_values_resultA  = (float *)malloc(N * sizeof(float));
    float *h_values_resultB  = (float *)malloc(N * sizeof(float));
    float *h_values_result2  = (float *)malloc(N * sizeof(float));
    int   *h_keys_result1    = (int *)  malloc(N * sizeof(int));
    int   *h_keys_result2    = (int *)  malloc(N * sizeof(int));

    // --- Allocating space for data and results on device
    float *d_valuesA;           gpuErrchk(cudaMalloc((void **)&d_valuesA,        N * sizeof(float)));
    float *d_valuesB;           gpuErrchk(cudaMalloc((void **)&d_valuesB,        N * sizeof(float)));
    int   *d_keys;              gpuErrchk(cudaMalloc((void **)&d_keys,           N * sizeof(int)));
    float *d_values_resultA;    gpuErrchk(cudaMalloc((void **)&d_values_resultA, N * sizeof(float)));
    float *d_values_resultB;    gpuErrchk(cudaMalloc((void **)&d_values_resultB, N * sizeof(float)));
    float *d_values_result2;    gpuErrchk(cudaMalloc((void **)&d_values_result2, N * sizeof(float)));
    int   *d_keys_result1;      gpuErrchk(cudaMalloc((void **)&d_keys_result1,   N * sizeof(int)));
    int   *d_keys_result2;      gpuErrchk(cudaMalloc((void **)&d_keys_result2,   N * sizeof(int)));

    // --- BlockSortKernel with shared
    gpuErrchk(cudaMemcpy(d_valuesA, h_valuesA, N * sizeof(float), cudaMemcpyHostToDevice));
    gpuErrchk(cudaMemcpy(d_valuesB, h_valuesB, N * sizeof(float), cudaMemcpyHostToDevice));
    gpuErrchk(cudaMemcpy(d_keys,   h_keys,   N * sizeof(int),   cudaMemcpyHostToDevice));
    shared_BlockSortKernel<N / numArrays / numElemsPerThread, numElemsPerThread><<<numArrays, numElemsPerArray / numElemsPerThread>>>(d_valuesA, d_valuesB, d_keys, d_values_resultA, d_values_resultB, d_keys_result1); 
    gpuErrchk(cudaPeekAtLastError());
    gpuErrchk(cudaDeviceSynchronize());    
    gpuErrchk(cudaMemcpy(h_values_resultA, d_values_resultA, N * sizeof(float), cudaMemcpyDeviceToHost));
    gpuErrchk(cudaMemcpy(h_values_resultB, d_values_resultB, N * sizeof(float), cudaMemcpyDeviceToHost));
    gpuErrchk(cudaMemcpy(h_keys_result1,   d_keys_result1,   N * sizeof(int),   cudaMemcpyDeviceToHost));

    printf("\n\nBlockSortKernel using shared memory\n\n");
    for (int k = 0; k < numArrays; k++) 
        for (int i = 0; i < numElemsPerArray; i++)
            printf("Array nr. %i; Element nr. %i; Key %i; Value %f; Value %f\n", k, i, h_keys_result1[k * numElemsPerArray + i], h_values_resultA[k * numElemsPerArray + i], h_values_resultB[k * numElemsPerArray + i]);

    return 0;
}
于 2016-01-07T15:54:30.310 回答
0

在我的第二个回答之后，我想进一步扩展使用CUB对存储在由 2D 线程网格填充的线性共享内存数组中存储的元素进行排序的情况。因此，cub::BlockRadixSort与 2D 线程网格一起使用，而不是像前面的答案中那样使用 1D 线程网格。这是一个完整的示例：

#include <cub/cub.cuh>
#include <stdio.h>
#include <stdlib.h>

#include "Utilities.cuh"

using namespace cub;

/*******************************/
/* CUB BLOCKSORT KERNEL SHARED */
/*******************************/
template <int BLOCKSIZE_X, int BLOCKSIZE_Y, int ITEMS_PER_THREAD>
__global__ void shared_BlockSortKernel(float *d_valuesA, float *d_valuesB, int *d_keys, float *d_values_resultA, float *d_values_resultB, int *d_keys_result)
{
    // --- Shared memory allocation
    __shared__ float sharedMemoryArrayValuesA [BLOCKSIZE_X * BLOCKSIZE_Y * ITEMS_PER_THREAD];
    __shared__ float sharedMemoryArrayValuesB [BLOCKSIZE_X * BLOCKSIZE_Y * ITEMS_PER_THREAD];
    __shared__ int   sharedMemoryArrayKeys    [BLOCKSIZE_X * BLOCKSIZE_Y * ITEMS_PER_THREAD];
    __shared__ int   sharedMemoryHelperIndices[BLOCKSIZE_X * BLOCKSIZE_Y * ITEMS_PER_THREAD];

    // --- Specialize BlockStore and BlockRadixSort collective types
    typedef cub::BlockRadixSort <int , BLOCKSIZE_X, ITEMS_PER_THREAD, int, 4, false, BLOCK_SCAN_WARP_SCANS, cudaSharedMemBankSizeFourByte, BLOCKSIZE_Y> BlockRadixSortT;

    // --- Allocate type-safe, repurposable shared memory for collectives
    __shared__ typename BlockRadixSortT::TempStorage temp_storage;

    int block_offset = blockIdx.x * (BLOCKSIZE_X * BLOCKSIZE_Y * ITEMS_PER_THREAD);

    // --- Load data to shared memory
    for (int k = 0; k < ITEMS_PER_THREAD; k++) {
        sharedMemoryArrayValuesA [(threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k] = d_valuesA[block_offset + (threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k];
        sharedMemoryArrayValuesB [(threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k] = d_valuesB[block_offset + (threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k];
        sharedMemoryArrayKeys    [(threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k] = d_keys   [block_offset + (threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k];
        sharedMemoryHelperIndices[(threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k] =                          (threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k ;
    }
    __syncthreads();

    // --- Collectively sort the keys
    BlockRadixSortT(temp_storage).SortBlockedToStriped(*static_cast<int(*)[ITEMS_PER_THREAD]>(static_cast<void*>(sharedMemoryArrayKeys     + ((threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD))),
                                                       *static_cast<int(*)[ITEMS_PER_THREAD]>(static_cast<void*>(sharedMemoryHelperIndices + ((threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD))));
    __syncthreads();

    // --- Write data to shared memory
    for (int k = 0; k < ITEMS_PER_THREAD; k++) {
        d_values_resultA[block_offset + (threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k] = sharedMemoryArrayValuesA[sharedMemoryHelperIndices[(threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k]];
        d_values_resultB[block_offset + (threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k] = sharedMemoryArrayValuesB[sharedMemoryHelperIndices[(threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k]];
        d_keys_result   [block_offset + (threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k] = sharedMemoryArrayKeys                             [(threadIdx.y * BLOCKSIZE_X + threadIdx.x) * ITEMS_PER_THREAD + k];
    }
}

/********/
/* MAIN */
/********/
int main() {

    const int blockSize_x       = 2;
    const int blockSize_y       = 4;
    const int numElemsPerArray  = blockSize_x * blockSize_y;
    const int numArrays         = 4;
    const int N                 = numArrays * numElemsPerArray;
    const int numElemsPerThread = numElemsPerArray / (blockSize_x * blockSize_y);

    const int RANGE             = N * numElemsPerThread;

    // --- Allocating and initializing the data on the host
    float *h_valuesA    = (float *)malloc(N * sizeof(float));
    float *h_valuesB    = (float *)malloc(N * sizeof(float));
    int *h_keys         = (int *)  malloc(N * sizeof(int));
    for (int i = 0 ; i < N; i++) {
        h_valuesA[i] = rand() % RANGE;
        h_valuesB[i] = rand() % RANGE;
        h_keys[i]    = rand() % RANGE;
    }

    printf("Original\n\n");
    for (int k = 0; k < numArrays; k++) 
        for (int i = 0; i < numElemsPerArray; i++)
            printf("Array nr. %i; Element nr. %i; Key %i; Value A %f; Value B %f\n", k, i, h_keys[k * numElemsPerArray + i], h_valuesA[k * numElemsPerArray + i], h_valuesB[k * numElemsPerArray + i]);

    // --- Allocating the results on the host
    float *h_values_resultA  = (float *)malloc(N * sizeof(float));
    float *h_values_resultB  = (float *)malloc(N * sizeof(float));
    float *h_values_result2  = (float *)malloc(N * sizeof(float));
    int   *h_keys_result1    = (int *)  malloc(N * sizeof(int));
    int   *h_keys_result2    = (int *)  malloc(N * sizeof(int));

    // --- Allocating space for data and results on device
    float *d_valuesA;           gpuErrchk(cudaMalloc((void **)&d_valuesA,        N * sizeof(float)));
    float *d_valuesB;           gpuErrchk(cudaMalloc((void **)&d_valuesB,        N * sizeof(float)));
    int   *d_keys;              gpuErrchk(cudaMalloc((void **)&d_keys,           N * sizeof(int)));
    float *d_values_resultA;    gpuErrchk(cudaMalloc((void **)&d_values_resultA, N * sizeof(float)));
    float *d_values_resultB;    gpuErrchk(cudaMalloc((void **)&d_values_resultB, N * sizeof(float)));
    float *d_values_result2;    gpuErrchk(cudaMalloc((void **)&d_values_result2, N * sizeof(float)));
    int   *d_keys_result1;      gpuErrchk(cudaMalloc((void **)&d_keys_result1,   N * sizeof(int)));
    int   *d_keys_result2;      gpuErrchk(cudaMalloc((void **)&d_keys_result2,   N * sizeof(int)));

    // --- BlockSortKernel with shared
    gpuErrchk(cudaMemcpy(d_valuesA, h_valuesA, N * sizeof(float), cudaMemcpyHostToDevice));
    gpuErrchk(cudaMemcpy(d_valuesB, h_valuesB, N * sizeof(float), cudaMemcpyHostToDevice));
    gpuErrchk(cudaMemcpy(d_keys,   h_keys,   N * sizeof(int),   cudaMemcpyHostToDevice));
    shared_BlockSortKernel<blockSize_x, blockSize_y, numElemsPerThread><<<numArrays, numElemsPerArray / numElemsPerThread>>>(d_valuesA, d_valuesB, d_keys, d_values_resultA, d_values_resultB, d_keys_result1); 
    gpuErrchk(cudaPeekAtLastError());
    gpuErrchk(cudaDeviceSynchronize());    
    gpuErrchk(cudaMemcpy(h_values_resultA, d_values_resultA, N * sizeof(float), cudaMemcpyDeviceToHost));
    gpuErrchk(cudaMemcpy(h_values_resultB, d_values_resultB, N * sizeof(float), cudaMemcpyDeviceToHost));
    gpuErrchk(cudaMemcpy(h_keys_result1,   d_keys_result1,   N * sizeof(int),   cudaMemcpyDeviceToHost));

    printf("\n\nBlockSortKernel using shared memory\n\n");
    for (int k = 0; k < numArrays; k++) 
        for (int i = 0; i < numElemsPerArray; i++)
            printf("Array nr. %i; Element nr. %i; Key %i; Value %f; Value %f\n", k, i, h_keys_result1[k * numElemsPerArray + i], h_values_resultA[k * numElemsPerArray + i], h_values_resultB[k * numElemsPerArray + i]);

    return 0;
}
于 2016-01-19T08:40:42.740 回答