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https://github.com/NVIDIA/cuda-samples.git
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166 lines
5.8 KiB
Plaintext
166 lines
5.8 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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/* Template project which demonstrates the basics on how to setup a project
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* example application.
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* Host code.
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*/
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// includes, system
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <math.h>
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// includes CUDA
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#include <cuda_runtime.h>
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// includes, project
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#include <helper_cuda.h>
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#include <helper_functions.h> // helper functions for SDK examples
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////////////////////////////////////////////////////////////////////////////////
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// declaration, forward
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void runTest(int argc, char **argv);
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extern "C" void computeGold(float *reference, float *idata,
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const unsigned int len);
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////////////////////////////////////////////////////////////////////////////////
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//! Simple test kernel for device functionality
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//! @param g_idata input data in global memory
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//! @param g_odata output data in global memory
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////////////////////////////////////////////////////////////////////////////////
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__global__ void testKernel(float *g_idata, float *g_odata) {
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// shared memory
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// the size is determined by the host application
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extern __shared__ float sdata[];
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// access thread id
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const unsigned int tid = threadIdx.x;
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// access number of threads in this block
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const unsigned int num_threads = blockDim.x;
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// read in input data from global memory
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sdata[tid] = g_idata[tid];
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__syncthreads();
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// perform some computations
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sdata[tid] = (float)num_threads * sdata[tid];
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__syncthreads();
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// write data to global memory
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g_odata[tid] = sdata[tid];
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}
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////////////////////////////////////////////////////////////////////////////////
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// Program main
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////////////////////////////////////////////////////////////////////////////////
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int main(int argc, char **argv) { runTest(argc, argv); }
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////////////////////////////////////////////////////////////////////////////////
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//! Run a simple test for CUDA
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////////////////////////////////////////////////////////////////////////////////
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void runTest(int argc, char **argv) {
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bool bTestResult = true;
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printf("%s Starting...\n\n", argv[0]);
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// use command-line specified CUDA device, otherwise use device with highest
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// Gflops/s
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int devID = findCudaDevice(argc, (const char **)argv);
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StopWatchInterface *timer = 0;
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sdkCreateTimer(&timer);
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sdkStartTimer(&timer);
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unsigned int num_threads = 32;
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unsigned int mem_size = sizeof(float) * num_threads;
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// allocate host memory
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float *h_idata = (float *)malloc(mem_size);
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// initalize the memory
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for (unsigned int i = 0; i < num_threads; ++i) {
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h_idata[i] = (float)i;
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}
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// allocate device memory
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float *d_idata;
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checkCudaErrors(cudaMalloc((void **)&d_idata, mem_size));
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// copy host memory to device
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checkCudaErrors(
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cudaMemcpy(d_idata, h_idata, mem_size, cudaMemcpyHostToDevice));
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// allocate device memory for result
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float *d_odata;
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checkCudaErrors(cudaMalloc((void **)&d_odata, mem_size));
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// setup execution parameters
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dim3 grid(1, 1, 1);
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dim3 threads(num_threads, 1, 1);
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// execute the kernel
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testKernel<<<grid, threads, mem_size>>>(d_idata, d_odata);
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// check if kernel execution generated and error
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getLastCudaError("Kernel execution failed");
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// allocate mem for the result on host side
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float *h_odata = (float *)malloc(mem_size);
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// copy result from device to host
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checkCudaErrors(cudaMemcpy(h_odata, d_odata, sizeof(float) * num_threads,
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cudaMemcpyDeviceToHost));
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sdkStopTimer(&timer);
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printf("Processing time: %f (ms)\n", sdkGetTimerValue(&timer));
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sdkDeleteTimer(&timer);
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// compute reference solution
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float *reference = (float *)malloc(mem_size);
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computeGold(reference, h_idata, num_threads);
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// check result
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if (checkCmdLineFlag(argc, (const char **)argv, "regression")) {
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// write file for regression test
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sdkWriteFile("./data/regression.dat", h_odata, num_threads, 0.0f, false);
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} else {
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// custom output handling when no regression test running
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// in this case check if the result is equivalent to the expected solution
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bTestResult = compareData(reference, h_odata, num_threads, 0.0f, 0.0f);
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}
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// cleanup memory
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free(h_idata);
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free(h_odata);
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free(reference);
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checkCudaErrors(cudaFree(d_idata));
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checkCudaErrors(cudaFree(d_odata));
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exit(bTestResult ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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