我正在尝试编写一个带有未分类的键/值对的函数，例如
<7, 4>
<2, 8>
<3, 1>
<2, 2>
<1, 5>
<7, 1>
<3, 8>
<7, 2>
并按键对它们进行排序，同时使用相同的键减少对的值：
<1, 5>
<2, 10>
<3, 9>
<7, 7>
目前，我正在使用类似下面的__device__函数，它本质上是一种bitonic排序，它将组合相同键的值并将旧数据设置为无限大的值（仅使用{{1现在），以便后续的bitonic排序将它们筛选到底部，并删除99值所删除的数组。
int *这适用于小型数据集，但设置较大（但仍然在单个块的大小范围内），单个调用就不会这样做。
尝试将排序和减少结合在同一个函数中是否明智？显然，该函数需要不止一次被调用，但有可能确切地确定需要根据其大小调用所有数据的次数吗？
或者我应该单独用这样的东西预先形成减少：
__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();
}
}
}
}
我正在尝试提供最有效的解决方案，但我对CUDA和并行算法的经验有限。
答案 0 :(得分：4)
您可以使用thrust执行此操作。
使用thrust::sort_by_key后跟thrust::reduce_by_key
以下是一个例子：
#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;
}
答案 1 :(得分：1)
从您的帖子中，您似乎需要按键排序许多小数组。引用自己：
这适用于小型数据集但是具有较大的数据集（尽管仍然在单个块的大小范围内），单个调用只是不会这样做。
下面您将找到一个完整的示例，围绕我对Sorting many small arrays in CUDA的回答并使用cub::BlockRadixSort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;
}
答案 2 :(得分：0)
我最近遇到了将上述方法扩展到必须根据相同密钥对多个数组进行排序的情况。
由于它的原型，似乎不可能通过使用zip迭代器和元组“打包”数组来使用cub::BlockRadixSort，请参阅C++ operating on “packed” arrays。因此，我已经利用了引用帖子中建议的辅助索引方法。
以下是我制定的例子：
#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;
}
答案 3 :(得分：0)
在我的第二个回答之后，当CUB用于对存储在由2D线程网格填充的线性共享内存数组中的元素进行排序时，我想进一步扩展该案例。因此，cub::BlockRadixSort与2D网格线程一起使用，而不是如前一个答案中的一维线程网格。这是一个完整的例子：
#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;
}