mirror of
https://github.com/NVIDIA/cuda-samples.git
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164 lines
6.1 KiB
C++
164 lines
6.1 KiB
C++
/* Copyright (c) 2021, 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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/**
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* This sample implements bitonic sort and odd-even merge sort, algorithms
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* belonging to the class of sorting networks.
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* While generally subefficient on large sequences
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* compared to algorithms with better asymptotic algorithmic complexity
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* (i.e. merge sort or radix sort), may be the algorithms of choice for sorting
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* batches of short- or mid-sized arrays.
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* Refer to the excellent tutorial by H. W. Lang:
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* http://www.iti.fh-flensburg.de/lang/algorithmen/sortieren/networks/indexen.htm
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*
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* Victor Podlozhnyuk, 07/09/2009
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*/
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// CUDA Runtime
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#include <cuda_runtime.h>
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// Utilities and system includes
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#include <helper_cuda.h>
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#include <helper_timer.h>
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#include "sortingNetworks_common.h"
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////////////////////////////////////////////////////////////////////////////////
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// Test driver
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////////////////////////////////////////////////////////////////////////////////
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int main(int argc, char **argv) {
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cudaError_t error;
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printf("%s Starting...\n\n", argv[0]);
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printf("Starting up CUDA context...\n");
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int dev = findCudaDevice(argc, (const char **)argv);
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uint *h_InputKey, *h_InputVal, *h_OutputKeyGPU, *h_OutputValGPU;
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uint *d_InputKey, *d_InputVal, *d_OutputKey, *d_OutputVal;
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StopWatchInterface *hTimer = NULL;
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const uint N = 1048576;
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const uint DIR = 0;
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const uint numValues = 65536;
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const uint numIterations = 1;
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printf("Allocating and initializing host arrays...\n\n");
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sdkCreateTimer(&hTimer);
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h_InputKey = (uint *)malloc(N * sizeof(uint));
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h_InputVal = (uint *)malloc(N * sizeof(uint));
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h_OutputKeyGPU = (uint *)malloc(N * sizeof(uint));
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h_OutputValGPU = (uint *)malloc(N * sizeof(uint));
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srand(2001);
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for (uint i = 0; i < N; i++) {
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h_InputKey[i] = rand() % numValues;
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h_InputVal[i] = i;
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}
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printf("Allocating and initializing CUDA arrays...\n\n");
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error = cudaMalloc((void **)&d_InputKey, N * sizeof(uint));
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checkCudaErrors(error);
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error = cudaMalloc((void **)&d_InputVal, N * sizeof(uint));
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checkCudaErrors(error);
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error = cudaMalloc((void **)&d_OutputKey, N * sizeof(uint));
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checkCudaErrors(error);
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error = cudaMalloc((void **)&d_OutputVal, N * sizeof(uint));
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checkCudaErrors(error);
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error = cudaMemcpy(d_InputKey, h_InputKey, N * sizeof(uint),
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cudaMemcpyHostToDevice);
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checkCudaErrors(error);
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error = cudaMemcpy(d_InputVal, h_InputVal, N * sizeof(uint),
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cudaMemcpyHostToDevice);
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checkCudaErrors(error);
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int flag = 1;
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printf("Running GPU bitonic sort (%u identical iterations)...\n\n",
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numIterations);
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for (uint arrayLength = 64; arrayLength <= N; arrayLength *= 2) {
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printf("Testing array length %u (%u arrays per batch)...\n", arrayLength,
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N / arrayLength);
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error = cudaDeviceSynchronize();
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checkCudaErrors(error);
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sdkResetTimer(&hTimer);
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sdkStartTimer(&hTimer);
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uint threadCount = 0;
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for (uint i = 0; i < numIterations; i++)
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threadCount = bitonicSort(d_OutputKey, d_OutputVal, d_InputKey,
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d_InputVal, N / arrayLength, arrayLength, DIR);
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error = cudaDeviceSynchronize();
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checkCudaErrors(error);
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sdkStopTimer(&hTimer);
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printf("Average time: %f ms\n\n",
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sdkGetTimerValue(&hTimer) / numIterations);
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if (arrayLength == N) {
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double dTimeSecs = 1.0e-3 * sdkGetTimerValue(&hTimer) / numIterations;
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printf(
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"sortingNetworks-bitonic, Throughput = %.4f MElements/s, Time = %.5f "
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"s, Size = %u elements, NumDevsUsed = %u, Workgroup = %u\n",
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(1.0e-6 * (double)arrayLength / dTimeSecs), dTimeSecs, arrayLength, 1,
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threadCount);
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}
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printf("\nValidating the results...\n");
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printf("...reading back GPU results\n");
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error = cudaMemcpy(h_OutputKeyGPU, d_OutputKey, N * sizeof(uint),
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cudaMemcpyDeviceToHost);
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checkCudaErrors(error);
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error = cudaMemcpy(h_OutputValGPU, d_OutputVal, N * sizeof(uint),
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cudaMemcpyDeviceToHost);
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checkCudaErrors(error);
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int keysFlag =
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validateSortedKeys(h_OutputKeyGPU, h_InputKey, N / arrayLength,
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arrayLength, numValues, DIR);
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int valuesFlag = validateValues(h_OutputKeyGPU, h_OutputValGPU, h_InputKey,
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N / arrayLength, arrayLength);
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flag = flag && keysFlag && valuesFlag;
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printf("\n");
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}
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printf("Shutting down...\n");
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sdkDeleteTimer(&hTimer);
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cudaFree(d_OutputVal);
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cudaFree(d_OutputKey);
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cudaFree(d_InputVal);
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cudaFree(d_InputKey);
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free(h_OutputValGPU);
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free(h_OutputKeyGPU);
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free(h_InputVal);
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free(h_InputKey);
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exit(flag ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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