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127 lines
4.4 KiB
Plaintext
127 lines
4.4 KiB
Plaintext
/* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#include <stdio.h>
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// includes, project
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#include <helper_cuda.h>
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#include <helper_functions.h>
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#include <cuda_runtime.h>
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#include <cooperative_groups.h>
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namespace cg = cooperative_groups;
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#define NUM_ELEMS 10000000
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#define NUM_THREADS_PER_BLOCK 512
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// warp-aggregated atomic increment
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__device__ int atomicAggInc(int *counter) {
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cg::coalesced_group active = cg::coalesced_threads();
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int mask = active.ballot(1);
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// select the leader
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int leader = __ffs(mask) - 1;
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// leader does the update
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int res = 0;
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if (active.thread_rank() == leader) {
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res = atomicAdd(counter, __popc(mask));
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}
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// broadcast result
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res = active.shfl(res, leader);
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// each thread computes its own value
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return res + __popc(mask & ((1 << active.thread_rank()) - 1));
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}
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__global__ void filter_arr(int *dst, int *nres, const int *src, int n) {
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int id = threadIdx.x + blockIdx.x * blockDim.x;
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for (int i = id; i < n; i += gridDim.x * blockDim.x) {
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if (src[i] > 0) dst[atomicAggInc(nres)] = src[i];
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}
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}
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int main(int argc, char **argv) {
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int *data_to_filter, *filtered_data, nres = 0;
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int *d_data_to_filter, *d_filtered_data, *d_nres;
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data_to_filter = reinterpret_cast<int *>(malloc(sizeof(int) * NUM_ELEMS));
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// Generate input data.
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for (int i = 0; i < NUM_ELEMS; i++) {
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data_to_filter[i] = rand() % 20;
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}
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findCudaDevice(argc, (const char **)argv);
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checkCudaErrors(cudaMalloc(&d_data_to_filter, sizeof(int) * NUM_ELEMS));
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checkCudaErrors(cudaMalloc(&d_filtered_data, sizeof(int) * NUM_ELEMS));
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checkCudaErrors(cudaMalloc(&d_nres, sizeof(int)));
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checkCudaErrors(cudaMemcpy(d_data_to_filter, data_to_filter,
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sizeof(int) * NUM_ELEMS, cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemset(d_nres, 0, sizeof(int)));
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dim3 dimBlock(NUM_THREADS_PER_BLOCK, 1, 1);
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dim3 dimGrid((NUM_ELEMS / NUM_THREADS_PER_BLOCK) + 1, 1, 1);
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filter_arr<<<dimGrid, dimBlock>>>(d_filtered_data, d_nres, d_data_to_filter,
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NUM_ELEMS);
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checkCudaErrors(
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cudaMemcpy(&nres, d_nres, sizeof(int), cudaMemcpyDeviceToHost));
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filtered_data = reinterpret_cast<int *>(malloc(sizeof(int) * nres));
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checkCudaErrors(cudaMemcpy(filtered_data, d_filtered_data, sizeof(int) * nres,
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cudaMemcpyDeviceToHost));
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int *host_filtered_data =
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reinterpret_cast<int *>(malloc(sizeof(int) * NUM_ELEMS));
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// Generate host output with host filtering code.
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int host_flt_count = 0;
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for (int i = 0; i < NUM_ELEMS; i++) {
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if (data_to_filter[i] > 0) {
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host_filtered_data[host_flt_count++] = data_to_filter[i];
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}
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}
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printf("\nWarp Aggregated Atomics %s \n",
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host_flt_count == nres ? "PASSED" : "FAILED");
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checkCudaErrors(cudaFree(d_data_to_filter));
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checkCudaErrors(cudaFree(d_filtered_data));
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checkCudaErrors(cudaFree(d_nres));
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free(data_to_filter);
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free(filtered_data);
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free(host_filtered_data);
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}
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