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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/**
* This sample implements bitonic sort and odd-even merge sort, algorithms
* belonging to the class of sorting networks.
* While generally subefficient on large sequences
* compared to algorithms with better asymptotic algorithmic complexity
* (i.e. merge sort or radix sort), may be the algorithms of choice for sorting
* batches of short- or mid-sized arrays.
* Refer to the excellent tutorial by H. W. Lang:
* http://www.iti.fh-flensburg.de/lang/algorithmen/sortieren/networks/indexen.htm
*
* Victor Podlozhnyuk, 07/09/2009
*/
// CUDA Runtime
#include <cuda_runtime.h>
// Utilities and system includes
#include <helper_cuda.h>
#include <helper_timer.h>
#include "sortingNetworks_common.h"
////////////////////////////////////////////////////////////////////////////////
// Test driver
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
cudaError_t error;
printf("%s Starting...\n\n", argv[0]);
printf("Starting up CUDA context...\n");
int dev = findCudaDevice(argc, (const char **)argv);
uint *h_InputKey, *h_InputVal, *h_OutputKeyGPU, *h_OutputValGPU;
uint *d_InputKey, *d_InputVal, *d_OutputKey, *d_OutputVal;
StopWatchInterface *hTimer = NULL;
const uint N = 1048576;
const uint DIR = 0;
const uint numValues = 65536;
const uint numIterations = 1;
printf("Allocating and initializing host arrays...\n\n");
sdkCreateTimer(&hTimer);
h_InputKey = (uint *)malloc(N * sizeof(uint));
h_InputVal = (uint *)malloc(N * sizeof(uint));
h_OutputKeyGPU = (uint *)malloc(N * sizeof(uint));
h_OutputValGPU = (uint *)malloc(N * sizeof(uint));
srand(2001);
for (uint i = 0; i < N; i++) {
h_InputKey[i] = rand() % numValues;
h_InputVal[i] = i;
}
printf("Allocating and initializing CUDA arrays...\n\n");
error = cudaMalloc((void **)&d_InputKey, N * sizeof(uint));
checkCudaErrors(error);
error = cudaMalloc((void **)&d_InputVal, N * sizeof(uint));
checkCudaErrors(error);
error = cudaMalloc((void **)&d_OutputKey, N * sizeof(uint));
checkCudaErrors(error);
error = cudaMalloc((void **)&d_OutputVal, N * sizeof(uint));
checkCudaErrors(error);
error = cudaMemcpy(d_InputKey, h_InputKey, N * sizeof(uint),
cudaMemcpyHostToDevice);
checkCudaErrors(error);
error = cudaMemcpy(d_InputVal, h_InputVal, N * sizeof(uint),
cudaMemcpyHostToDevice);
checkCudaErrors(error);
int flag = 1;
printf("Running GPU bitonic sort (%u identical iterations)...\n\n",
numIterations);
for (uint arrayLength = 64; arrayLength <= N; arrayLength *= 2) {
printf("Testing array length %u (%u arrays per batch)...\n", arrayLength,
N / arrayLength);
error = cudaDeviceSynchronize();
checkCudaErrors(error);
sdkResetTimer(&hTimer);
sdkStartTimer(&hTimer);
uint threadCount = 0;
for (uint i = 0; i < numIterations; i++)
threadCount = bitonicSort(d_OutputKey, d_OutputVal, d_InputKey,
d_InputVal, N / arrayLength, arrayLength, DIR);
error = cudaDeviceSynchronize();
checkCudaErrors(error);
sdkStopTimer(&hTimer);
printf("Average time: %f ms\n\n",
sdkGetTimerValue(&hTimer) / numIterations);
if (arrayLength == N) {
double dTimeSecs = 1.0e-3 * sdkGetTimerValue(&hTimer) / numIterations;
printf(
"sortingNetworks-bitonic, Throughput = %.4f MElements/s, Time = %.5f "
"s, Size = %u elements, NumDevsUsed = %u, Workgroup = %u\n",
(1.0e-6 * (double)arrayLength / dTimeSecs), dTimeSecs, arrayLength, 1,
threadCount);
}
printf("\nValidating the results...\n");
printf("...reading back GPU results\n");
error = cudaMemcpy(h_OutputKeyGPU, d_OutputKey, N * sizeof(uint),
cudaMemcpyDeviceToHost);
checkCudaErrors(error);
error = cudaMemcpy(h_OutputValGPU, d_OutputVal, N * sizeof(uint),
cudaMemcpyDeviceToHost);
checkCudaErrors(error);
int keysFlag =
validateSortedKeys(h_OutputKeyGPU, h_InputKey, N / arrayLength,
arrayLength, numValues, DIR);
int valuesFlag = validateValues(h_OutputKeyGPU, h_OutputValGPU, h_InputKey,
N / arrayLength, arrayLength);
flag = flag && keysFlag && valuesFlag;
printf("\n");
}
printf("Shutting down...\n");
sdkDeleteTimer(&hTimer);
cudaFree(d_OutputVal);
cudaFree(d_OutputKey);
cudaFree(d_InputVal);
cudaFree(d_InputKey);
free(h_OutputValGPU);
free(h_OutputKeyGPU);
free(h_InputVal);
free(h_InputKey);
exit(flag ? EXIT_SUCCESS : EXIT_FAILURE);
}
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