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306 lines
10 KiB
C++
306 lines
10 KiB
C++
/* 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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/*
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* Portions Copyright (c) 2009 Mike Giles, Oxford University. All rights
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* reserved.
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* Portions Copyright (c) 2008 Frances Y. Kuo and Stephen Joe. All rights
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* reserved.
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*
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* Sobol Quasi-random Number Generator example
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*
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* Based on CUDA code submitted by Mike Giles, Oxford University, United Kingdom
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* http://people.maths.ox.ac.uk/~gilesm/
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*
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* and C code developed by Stephen Joe, University of Waikato, New Zealand
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* and Frances Kuo, University of New South Wales, Australia
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* http://web.maths.unsw.edu.au/~fkuo/sobol/
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*
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* For theoretical background see:
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*
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* P. Bratley and B.L. Fox.
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* Implementing Sobol's quasirandom sequence generator
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* http://portal.acm.org/citation.cfm?id=42288
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* ACM Trans. on Math. Software, 14(1):88-100, 1988
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*
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* S. Joe and F. Kuo.
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* Remark on algorithm 659: implementing Sobol's quasirandom sequence generator.
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* http://portal.acm.org/citation.cfm?id=641879
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* ACM Trans. on Math. Software, 29(1):49-57, 2003
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*/
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#include <iostream>
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#include <cuda_runtime.h> // CUDA Runtime Functions
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#include <helper_cuda.h> // helper functions for CUDA error checking and initialization
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#include <helper_functions.h> // helper functions
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#include <stdexcept>
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#include <math.h>
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#include "sobol.h"
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#include "sobol_gold.h"
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#include "sobol_gpu.h"
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#define L1ERROR_TOLERANCE (1e-6)
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const char *sSDKsample = "Sobol Quasi-Random Number Generator";
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void printHelp(int argc, char *argv[]) {
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if (argc > 0) {
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std::cout << "\nUsage: " << argv[0] << " <options>\n\n";
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} else {
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std::cout << "\nUsage: <program name> <options>\n\n";
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}
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std::cout << "\t--vectors=M specify number of vectors (required)\n";
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std::cout << "\t The generator will output M vectors\n\n";
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std::cout << "\t--dimensions=N specify number of dimensions (required)\n";
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std::cout << "\t Each vector will consist of N components\n\n";
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std::cout << std::endl;
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}
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int main(int argc, char *argv[]) {
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bool ok = true;
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// We will generate n_vectors vectors of n_dimensions numbers
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int n_vectors = 100000;
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int n_dimensions = 100;
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printf("%s Starting...\n\n", sSDKsample);
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// Print help if requested
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if (checkCmdLineFlag(argc, (const char **)argv, "help")) {
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printHelp(argc, argv);
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return 0;
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "qatest")) {
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// For QA testing set a default number of vectors and dimensions
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n_vectors = 100000;
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n_dimensions = 100;
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} else {
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// Parse the command line to determine the required number of vectors
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if (checkCmdLineFlag(argc, (const char **)argv, "vectors")) {
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n_vectors = getCmdLineArgumentInt(argc, (const char **)argv, "vectors");
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if (n_vectors < 1) {
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std::cerr << "Illegal argument: number of vectors must be positive "
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"(--vectors=N)"
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<< std::endl;
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ok = false;
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}
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}
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std::cout << "> number of vectors = " << n_vectors << std::endl;
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// Parse the command line to determine the number of dimensions in each
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// vector
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if (checkCmdLineFlag(argc, (const char **)argv, "dimensions")) {
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n_dimensions =
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getCmdLineArgumentInt(argc, (const char **)argv, "dimensions");
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if (n_dimensions < 1) {
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std::cerr << "Illegal argument: number of dimensions must be positive "
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"(--dimensions=N)"
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<< std::endl;
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ok = false;
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}
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}
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std::cout << "> number of dimensions = " << n_dimensions << std::endl;
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}
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// If any of the command line checks failed, exit
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if (!ok) {
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return -1;
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}
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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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findCudaDevice(argc, (const char **)argv);
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// Create a timer to measure performance
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StopWatchInterface *hTimer = NULL;
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double time;
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sdkCreateTimer(&hTimer);
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// Allocate memory for the arrays
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std::cout << "Allocating CPU memory..." << std::endl;
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unsigned int *h_directions = 0;
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float *h_outputCPU = 0;
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float *h_outputGPU = 0;
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try {
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h_directions = new unsigned int[n_dimensions * n_directions];
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h_outputCPU = new float[n_vectors * n_dimensions];
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h_outputGPU = new float[n_vectors * n_dimensions];
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} catch (std::exception e) {
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std::cerr << "Caught exception: " << e.what() << std::endl;
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std::cerr << "Unable to allocate CPU memory (try running with fewer "
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"vectors/dimensions)"
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<< std::endl;
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exit(EXIT_FAILURE);
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}
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std::cout << "Allocating GPU memory..." << std::endl;
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unsigned int *d_directions;
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float *d_output;
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try {
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cudaError_t cudaResult;
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cudaResult = cudaMalloc((void **)&d_directions,
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n_dimensions * n_directions * sizeof(unsigned int));
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if (cudaResult != cudaSuccess) {
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throw std::runtime_error(cudaGetErrorString(cudaResult));
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}
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cudaResult = cudaMalloc((void **)&d_output,
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n_vectors * n_dimensions * sizeof(float));
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if (cudaResult != cudaSuccess) {
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throw std::runtime_error(cudaGetErrorString(cudaResult));
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}
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} catch (std::runtime_error e) {
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std::cerr << "Caught exception: " << e.what() << std::endl;
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std::cerr << "Unable to allocate GPU memory (try running with fewer "
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"vectors/dimensions)"
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<< std::endl;
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exit(EXIT_FAILURE);
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}
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// Initialize the direction numbers (done on the host)
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std::cout << "Initializing direction numbers..." << std::endl;
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initSobolDirectionVectors(n_dimensions, h_directions);
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// Copy the direction numbers to the device
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std::cout << "Copying direction numbers to device..." << std::endl;
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checkCudaErrors(cudaMemcpy(d_directions, h_directions,
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n_dimensions * n_directions * sizeof(unsigned int),
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaDeviceSynchronize());
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// Execute the QRNG on the device
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std::cout << "Executing QRNG on GPU..." << std::endl;
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sdkResetTimer(&hTimer);
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sdkStartTimer(&hTimer);
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sobolGPU(n_vectors, n_dimensions, d_directions, d_output);
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checkCudaErrors(cudaDeviceSynchronize());
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sdkStopTimer(&hTimer);
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time = sdkGetTimerValue(&hTimer);
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if (time < 1e-6) {
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std::cout << "Gsamples/s: problem size too small to measure, try "
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"increasing number of vectors or dimensions"
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<< std::endl;
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} else {
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std::cout << "Gsamples/s: "
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<< (double)n_vectors * (double)n_dimensions * 1E-9 / (time * 1E-3)
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<< std::endl;
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}
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std::cout << "Reading results from GPU..." << std::endl;
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checkCudaErrors(cudaMemcpy(h_outputGPU, d_output,
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n_vectors * n_dimensions * sizeof(float),
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cudaMemcpyDeviceToHost));
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std::cout << std::endl;
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// Execute the QRNG on the host
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std::cout << "Executing QRNG on CPU..." << std::endl;
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sdkResetTimer(&hTimer);
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sdkStartTimer(&hTimer);
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sobolCPU(n_vectors, n_dimensions, h_directions, h_outputCPU);
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sdkStopTimer(&hTimer);
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time = sdkGetTimerValue(&hTimer);
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if (time < 1e-6) {
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std::cout << "Gsamples/s: problem size too small to measure, try "
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"increasing number of vectors or dimensions"
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<< std::endl;
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} else {
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std::cout << "Gsamples/s: "
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<< (double)n_vectors * (double)n_dimensions * 1E-9 / (time * 1E-3)
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<< std::endl;
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}
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// Check the results
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std::cout << "Checking results..." << std::endl;
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float l1norm_diff = 0.0F;
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float l1norm_ref = 0.0F;
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float l1error;
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// Special case if n_vectors is 1, when the vector should be exactly 0
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if (n_vectors == 1) {
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for (int d = 0, v = 0; d < n_dimensions; d++) {
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float ref = h_outputCPU[d * n_vectors + v];
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l1norm_diff += fabs(h_outputGPU[d * n_vectors + v] - ref);
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l1norm_ref += fabs(ref);
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}
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// Output the L1-Error
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l1error = l1norm_diff;
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if (l1norm_ref != 0) {
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std::cerr << "Error: L1-Norm of the reference is not zero (for single "
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"vector), golden generator appears broken\n";
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} else {
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std::cout << "L1-Error: " << l1error << std::endl;
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}
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} else {
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for (int d = 0; d < n_dimensions; d++) {
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for (int v = 0; v < n_vectors; v++) {
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float ref = h_outputCPU[d * n_vectors + v];
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l1norm_diff += fabs(h_outputGPU[d * n_vectors + v] - ref);
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l1norm_ref += fabs(ref);
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}
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}
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// Output the L1-Error
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l1error = l1norm_diff / l1norm_ref;
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if (l1norm_ref == 0) {
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std::cerr << "Error: L1-Norm of the reference is zero, golden generator "
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"appears broken\n";
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} else {
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std::cout << "L1-Error: " << l1error << std::endl;
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}
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}
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// Cleanup and terminate
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std::cout << "Shutting down..." << std::endl;
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sdkDeleteTimer(&hTimer);
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delete h_directions;
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delete h_outputCPU;
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delete h_outputGPU;
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checkCudaErrors(cudaFree(d_directions));
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checkCudaErrors(cudaFree(d_output));
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// Check pass/fail using L1 error
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exit(l1error < L1ERROR_TOLERANCE ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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