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328 lines
10 KiB
Plaintext
328 lines
10 KiB
Plaintext
/* Copyright (c) 2022, 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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#include <cooperative_groups/reduce.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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// leader does the update
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int res = 0;
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if (active.thread_rank() == 0) {
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res = atomicAdd(counter, active.size());
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}
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// broadcast result
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res = active.shfl(res, 0);
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// each thread computes its own value
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return res + active.thread_rank();
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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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// warp-aggregated atomic multi bucket increment
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#if __CUDA_ARCH__ >= 700
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__device__ int atomicAggIncMulti(const int bucket, int *counter)
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{
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cg::coalesced_group active = cg::coalesced_threads();
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// group all threads with same bucket value.
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auto labeledGroup = cg::labeled_partition(active, bucket);
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int res = 0;
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if (labeledGroup.thread_rank() == 0)
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{
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res = atomicAdd(&counter[bucket], labeledGroup.size());
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}
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// broadcast result
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res = labeledGroup.shfl(res, 0);
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// each thread computes its own value
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return res + labeledGroup.thread_rank();
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}
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#endif
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// Places individual value indices into its corresponding buckets.
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__global__ void mapToBuckets(const int *srcArr, int *indicesBuckets, int *bucketCounters, const int srcSize, const int numOfBuckets)
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{
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#if __CUDA_ARCH__ >= 700
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cg::grid_group grid = cg::this_grid();
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for (int i=grid.thread_rank(); i < srcSize; i += grid.size())
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{
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const int bucket = srcArr[i];
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if (bucket < numOfBuckets)
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{
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indicesBuckets[atomicAggIncMulti(bucket, bucketCounters)] = i;
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}
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}
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#endif
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}
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int mapIndicesToBuckets(int *h_srcArr, int *d_srcArr, int numOfBuckets)
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{
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int *d_indicesBuckets, *d_bucketCounters;
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int *cpuBucketCounters = new int[numOfBuckets];
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int *h_bucketCounters = new int[numOfBuckets];
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memset(cpuBucketCounters, 0, sizeof(int)*numOfBuckets);
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// Initialize each bucket counters.
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for (int i = 0; i < numOfBuckets; i++)
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{
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h_bucketCounters[i] = i*NUM_ELEMS;
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}
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checkCudaErrors(cudaMalloc(&d_indicesBuckets, sizeof(int) * NUM_ELEMS * numOfBuckets));
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checkCudaErrors(cudaMalloc(&d_bucketCounters, sizeof(int) * numOfBuckets));
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checkCudaErrors(cudaMemcpy(d_bucketCounters, h_bucketCounters, sizeof(int)*numOfBuckets, cudaMemcpyHostToDevice));
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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);
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mapToBuckets<<<dimGrid, dimBlock>>>(d_srcArr, d_indicesBuckets, d_bucketCounters, NUM_ELEMS, numOfBuckets);
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checkCudaErrors(cudaMemcpy(h_bucketCounters, d_bucketCounters, sizeof(int)*numOfBuckets, cudaMemcpyDeviceToHost));
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for (int i=0; i < NUM_ELEMS; i++)
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{
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cpuBucketCounters[h_srcArr[i]]++;
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}
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bool allMatch = true;
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int finalElems = 0;
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for (int i=0; i < numOfBuckets; i++)
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{
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finalElems += (h_bucketCounters[i] - i*NUM_ELEMS);
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if (cpuBucketCounters[i] != (h_bucketCounters[i] - i*NUM_ELEMS))
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{
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allMatch = false;
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break;
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}
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}
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if (!allMatch && finalElems != NUM_ELEMS)
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{
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return EXIT_FAILURE;
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}
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return EXIT_SUCCESS;
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}
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// Warp-aggregated atomic Max in multi bucket
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#if __CUDA_ARCH__ >= 700
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__device__ void atomicAggMaxMulti(const int bucket, int *counter, const int valueForMax)
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{
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cg::coalesced_group active = cg::coalesced_threads();
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// group all threads with same bucket value.
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auto labeledGroup = cg::labeled_partition(active, bucket);
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const int maxValueInGroup = cg::reduce(labeledGroup, valueForMax, cg::greater<int>());
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if (labeledGroup.thread_rank() == 0)
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{
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atomicMax(&counter[bucket], maxValueInGroup);
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}
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}
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#endif
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// Performs max calculation in each buckets.
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__global__ void calculateMaxInEachBuckets(const int *srcArr, const int *valueInBuckets, int *bucketsMax, const int srcSize, const int numOfBuckets)
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{
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#if __CUDA_ARCH__ >= 700
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cg::grid_group grid = cg::this_grid();
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for (int i=grid.thread_rank(); i < srcSize; i += grid.size())
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{
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const int bucket = srcArr[i];
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if (bucket < numOfBuckets)
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{
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atomicAggMaxMulti(bucket, bucketsMax, valueInBuckets[i]);
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}
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}
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#endif
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}
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int calculateMaxInBuckets(int *h_srcArr, int *d_srcArr, int numOfBuckets)
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{
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int *d_valueInBuckets, *d_bucketsMax;
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int *h_valueInBuckets = new int[NUM_ELEMS];
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int *cpuBucketsMax = new int[numOfBuckets];
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int *h_bucketsMax = new int[numOfBuckets];
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memset(cpuBucketsMax, 0, sizeof(int) * numOfBuckets);
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// Here we create values which is assumed to correspond to each
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// buckets of srcArr at same array index.
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for (int i=0; i < NUM_ELEMS; i++)
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{
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h_valueInBuckets[i] = rand();
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}
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checkCudaErrors(cudaMalloc(&d_valueInBuckets, sizeof(int) * NUM_ELEMS));
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checkCudaErrors(cudaMalloc(&d_bucketsMax, sizeof(int) * numOfBuckets));
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checkCudaErrors(cudaMemset(d_bucketsMax, 0, sizeof(int) * numOfBuckets));
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checkCudaErrors(cudaMemcpy(d_valueInBuckets, h_valueInBuckets, sizeof(int) * NUM_ELEMS, cudaMemcpyHostToDevice));
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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);
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calculateMaxInEachBuckets<<<dimGrid, dimBlock>>>(d_srcArr, d_valueInBuckets, d_bucketsMax, NUM_ELEMS, numOfBuckets);
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checkCudaErrors(cudaMemcpy(h_bucketsMax, d_bucketsMax, sizeof(int) * numOfBuckets, cudaMemcpyDeviceToHost));
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for (int i = 0; i < NUM_ELEMS; i++)
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{
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if (cpuBucketsMax[h_srcArr[i]] < h_valueInBuckets[i])
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{
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cpuBucketsMax[h_srcArr[i]] = h_valueInBuckets[i];
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}
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}
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bool allMatch = true;
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int finalElems = 0;
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for (int i=0; i < numOfBuckets; i++)
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{
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if (cpuBucketsMax[i] != h_bucketsMax[i])
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{
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allMatch = false;
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printf("CPU i=%d max = %d mismatches GPU max = %d\n", i, cpuBucketsMax[i], h_bucketsMax[i]);
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break;
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}
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}
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if (allMatch)
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{
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printf("CPU max matches GPU max\n");
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}
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delete[] h_valueInBuckets;
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delete[] cpuBucketsMax;
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delete[] h_bucketsMax;
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checkCudaErrors(cudaFree(d_valueInBuckets));
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checkCudaErrors(cudaFree(d_bucketsMax));
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if (!allMatch && finalElems != NUM_ELEMS)
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{
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return EXIT_FAILURE;
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}
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return EXIT_SUCCESS;
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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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int numOfBuckets = 5;
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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() % numOfBuckets;
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}
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int devId = 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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int major = 0;
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checkCudaErrors(cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devId));
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int mapIndicesToBucketsStatus = EXIT_SUCCESS;
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int calculateMaxInBucketsStatus = EXIT_SUCCESS;
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// atomicAggIncMulti & atomicAggMaxMulti require a GPU of Volta (SM7X) architecture or higher,
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// so that it can take advantage of the new MATCH capability of Volta hardware
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if (major >= 7) {
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mapIndicesToBucketsStatus = mapIndicesToBuckets(data_to_filter, d_data_to_filter, numOfBuckets);
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calculateMaxInBucketsStatus = calculateMaxInBuckets(data_to_filter, d_data_to_filter, numOfBuckets);
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}
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printf("\nWarp Aggregated Atomics %s \n",
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(host_flt_count == nres) && (mapIndicesToBucketsStatus == EXIT_SUCCESS) &&
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(calculateMaxInBucketsStatus == EXIT_SUCCESS) ? "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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