mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-11-24 20:59:17 +08:00
164 lines
6.1 KiB
C++
164 lines
6.1 KiB
C++
/* Copyright (c) 2021, 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);
|
|
}
|