I am implementing a median filter in CUDA. For a particular pixel, I extract its neighbors corresponding to a window around the pixel, say a N x N (3 x 3) window, and now have an array of N x N elements. I do not envision using a window of more than 10 x 10 elements for my application.
This array is now locally present in the kernel and already loaded into device memory. From previous SO posts that I have read, the most common sorting algorithms are implemented by Thrust. But, Thrust can only be called from the host. Thread - Thrust inside user written kernels
Is there a quick and efficient way to sort a small array of N x N elements inside the kernel?
If the number of elements is fixed and small, you can use sorting networks (http://pages.ripco.net/~jgamble/nw.html). It provides a fixed number of compare/swap operations for a fixed number of elements (eg. 19 compare/swap iterations for 8 elements).
Your problem is sorting many small arrays in CUDA.
Following Robert's suggestion in his comment, CUB offers a possible solution to face this problem. Below I report an example that was constructed around Robert's code at cub BlockRadixSort: how to deal with large tile size or sort multiple tiles?.
The idea is assigning the small arrays to be sorted to different thread blocks and then using cub::BlockRadixSort to sort each array. Two versions are provided, one loading and one loading the small arrays into shared memory.
Let me finally note that your statement that CUDA Thrust is not callable from within kernels is not anymore true. The post Thrust inside user written kernels you linked to has been updated with other answers.
#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(int *d_in, int *d_out) { // --- Specialize BlockLoad, BlockStore, and BlockRadixSort collective types typedef cub::BlockLoad <int*, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_TRANSPOSE> BlockLoadT; typedef cub::BlockStore <int*, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_STORE_TRANSPOSE> BlockStoreT; typedef cub::BlockRadixSort <int , BLOCK_THREADS, ITEMS_PER_THREAD> BlockRadixSortT; // --- Allocate type-safe, repurposable shared memory for collectives __shared__ union { typename BlockLoadT ::TempStorage load; typename BlockStoreT ::TempStorage store; typename BlockRadixSortT::TempStorage sort; } temp_storage; // --- Obtain this block's segment of consecutive keys (blocked across threads) int thread_keys[ITEMS_PER_THREAD]; int block_offset = blockIdx.x * (BLOCK_THREADS * ITEMS_PER_THREAD); BlockLoadT(temp_storage.load).Load(d_in + block_offset, thread_keys); __syncthreads(); // --- Collectively sort the keys BlockRadixSortT(temp_storage.sort).Sort(thread_keys); __syncthreads(); // --- Store the sorted segment BlockStoreT(temp_storage.store).Store(d_out + block_offset, thread_keys); } /*******************************/ /* CUB BLOCKSORT KERNEL SHARED */ /*******************************/ template <int BLOCK_THREADS, int ITEMS_PER_THREAD> __global__ void shared_BlockSortKernel(int *d_in, int *d_out) { // --- Shared memory allocation __shared__ int sharedMemoryArray[BLOCK_THREADS * ITEMS_PER_THREAD]; // --- Specialize BlockStore and BlockRadixSort collective types typedef cub::BlockRadixSort <int , BLOCK_THREADS, ITEMS_PER_THREAD> 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++) sharedMemoryArray[threadIdx.x * ITEMS_PER_THREAD + k] = d_in[block_offset + threadIdx.x * ITEMS_PER_THREAD + k]; __syncthreads(); // --- Collectively sort the keys BlockRadixSortT(temp_storage).Sort(*static_cast<int(*)[ITEMS_PER_THREAD]>(static_cast<void*>(sharedMemoryArray + (threadIdx.x * ITEMS_PER_THREAD)))); __syncthreads(); // --- Write data to shared memory for (int k = 0; k < ITEMS_PER_THREAD; k++) d_out[block_offset + threadIdx.x * ITEMS_PER_THREAD + k] = sharedMemoryArray[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 int *h_data = (int *)malloc(N * sizeof(int)); for (int i = 0 ; i < N; i++) h_data[i] = rand() % RANGE; // --- Allocating the results on the host int *h_result1 = (int *)malloc(N * sizeof(int)); int *h_result2 = (int *)malloc(N * sizeof(int)); // --- Allocating space for data and results on device int *d_in; gpuErrchk(cudaMalloc((void **)&d_in, N * sizeof(int))); int *d_out1; gpuErrchk(cudaMalloc((void **)&d_out1, N * sizeof(int))); int *d_out2; gpuErrchk(cudaMalloc((void **)&d_out2, N * sizeof(int))); // --- BlockSortKernel no shared gpuErrchk(cudaMemcpy(d_in, h_data, N*sizeof(int), cudaMemcpyHostToDevice)); BlockSortKernel<N / numArrays / numElemsPerThread, numElemsPerThread><<<numArrays, numElemsPerArray / numElemsPerThread>>>(d_in, d_out1); gpuErrchk(cudaMemcpy(h_result1, d_out1, N*sizeof(int), cudaMemcpyDeviceToHost)); printf("BlockSortKernel 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; Value %i\n", k, i, h_result1[k * numElemsPerArray + i]); // --- BlockSortKernel with shared gpuErrchk(cudaMemcpy(d_in, h_data, N*sizeof(int), cudaMemcpyHostToDevice)); shared_BlockSortKernel<N / numArrays / numElemsPerThread, numElemsPerThread><<<numArrays, numElemsPerArray / numElemsPerThread>>>(d_in, d_out2); gpuErrchk(cudaMemcpy(h_result2, d_out2, N*sizeof(int), cudaMemcpyDeviceToHost)); printf("\n\nBlockSortKernel with shared\n\n"); for (int k = 0; k < numArrays; k++) for (int i = 0; i < numElemsPerArray; i++) printf("Array nr. %i; Element nr. %i; Value %i\n", k, i, h_result2[k * numElemsPerArray + i]); return 0; }
If you are using CUDA 5.X, you can use dynamic parallelism. You can make some child kernel in your filter kernel to finish the sort job. As how to sort by CUDA, you can use some induction skills.
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