mirror of
https://github.com/NVIDIA/cuda-samples.git
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265 lines
8.2 KiB
Plaintext
265 lines
8.2 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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/* This sample is a templatized version of the template project.
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* It also shows how to correctly templatize dynamically allocated shared
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* memory arrays.
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* Host code.
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*/
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// System includes
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#include <stdio.h>
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#include <assert.h>
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#include <string.h>
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#include <math.h>
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// CUDA runtime
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#include <cuda_runtime.h>
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// helper functions and utilities to work with CUDA
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#include <helper_functions.h>
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#include <helper_cuda.h>
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#ifndef MAX
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#define MAX(a, b) (a > b ? a : b)
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#endif
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// includes, kernels
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#include "sharedmem.cuh"
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int g_TotalFailures = 0;
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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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template <class T>
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__global__ void testKernel(T *g_idata, T *g_odata) {
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// Shared mem size is determined by the host app at run time
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SharedMemory<T> smem;
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T *sdata = smem.getPointer();
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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] = (T)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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// declaration, forward
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template <class T>
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void runTest(int argc, char **argv, int len);
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template <class T>
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void computeGold(T *reference, T *idata, const unsigned int len) {
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const T T_len = static_cast<T>(len);
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for (unsigned int i = 0; i < len; ++i) {
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reference[i] = idata[i] * T_len;
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}
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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) {
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printf("> runTest<float,32>\n");
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runTest<float>(argc, argv, 32);
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printf("> runTest<int,64>\n");
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runTest<int>(argc, argv, 64);
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printf("\n[simpleTemplates] -> Test Results: %d Failures\n", g_TotalFailures);
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exit(g_TotalFailures == 0 ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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// To completely templatize runTest (below) with cutil, we need to use
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// template specialization to wrap up CUTIL's array comparison and file writing
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// functions for different types.
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// Here's the generic wrapper for cutCompare*
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template <class T>
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class ArrayComparator {
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public:
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bool compare(const T *reference, T *data, unsigned int len) {
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fprintf(stderr,
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"Error: no comparison function implemented for this type\n");
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return false;
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}
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};
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// Here's the specialization for ints:
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template <>
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class ArrayComparator<int> {
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public:
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bool compare(const int *reference, int *data, unsigned int len) {
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return compareData(reference, data, len, 0.15f, 0.0f);
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}
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};
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// Here's the specialization for floats:
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template <>
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class ArrayComparator<float> {
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public:
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bool compare(const float *reference, float *data, unsigned int len) {
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return compareData(reference, data, len, 0.15f, 0.15f);
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}
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};
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// Here's the generic wrapper for cutWriteFile*
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template <class T>
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class ArrayFileWriter {
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public:
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bool write(const char *filename, T *data, unsigned int len, float epsilon) {
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fprintf(stderr,
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"Error: no file write function implemented for this type\n");
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return false;
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}
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};
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// Here's the specialization for ints:
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template <>
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class ArrayFileWriter<int> {
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public:
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bool write(const char *filename, int *data, unsigned int len, float epsilon) {
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return sdkWriteFile(filename, data, len, epsilon, false);
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}
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};
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// Here's the specialization for floats:
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template <>
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class ArrayFileWriter<float> {
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public:
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bool write(const char *filename, float *data, unsigned int len,
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float epsilon) {
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return sdkWriteFile(filename, data, len, epsilon, false);
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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//! Run a simple test for CUDA
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////////////////////////////////////////////////////////////////////////////////
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template <class T>
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void runTest(int argc, char **argv, int len) {
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int devID;
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cudaDeviceProp deviceProps;
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devID = findCudaDevice(argc, (const char **)argv);
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// get number of SMs on this GPU
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checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));
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printf("CUDA device [%s] has %d Multi-Processors\n", deviceProps.name,
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deviceProps.multiProcessorCount);
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// create and start timer
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StopWatchInterface *timer = NULL;
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sdkCreateTimer(&timer);
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// start the timer
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sdkStartTimer(&timer);
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unsigned int num_threads = len;
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unsigned int mem_size = sizeof(float) * num_threads;
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// allocate host memory
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T *h_idata = (T *)malloc(mem_size);
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// initialize the memory
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for (unsigned int i = 0; i < num_threads; ++i) {
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h_idata[i] = (T)i;
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}
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// allocate device memory
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T *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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T *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<T><<<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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T *h_odata = (T *)malloc(mem_size);
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// copy result from device to host
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checkCudaErrors(cudaMemcpy(h_odata, d_odata, sizeof(T) * 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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T *reference = (T *)malloc(mem_size);
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computeGold<T>(reference, h_idata, num_threads);
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ArrayComparator<T> comparator;
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ArrayFileWriter<T> writer;
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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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writer.write("./data/regression.dat", h_odata, num_threads, 0.0f);
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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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bool res = comparator.compare(reference, h_odata, num_threads);
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printf("Compare %s\n\n", (1 == res) ? "OK" : "MISMATCH");
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g_TotalFailures += (1 != res);
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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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}
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