TestOpenCLRandom.cpp

/* -------------------------------------------------------------------------- *
 *                                   OpenMM                                   *
 * -------------------------------------------------------------------------- *
 * This is part of the OpenMM molecular simulation toolkit originating from   *
 * Simbios, the NIH National Center for Physics-Based Simulation of           *
 * Biological Structures at Stanford, funded under the NIH Roadmap for        *
 * Medical Research, grant U54 GM072970. See https://simtk.org.               *
 *                                                                            *
 * Portions copyright (c) 2008-2009 Stanford University and the Authors.      *
 * Authors: Peter Eastman                                                     *
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/**
 * This tests the OpenCL implementation of random number generation.
 */

#include "../../../tests/AssertionUtilities.h"
#include "../src/OpenCLArray.h"
#include "../src/OpenCLContext.h"
#include "../src/OpenCLIntegrationUtilities.h"
#include "openmm/System.h"
#include <iostream>

using namespace OpenMM;
using namespace std;

void testGaussian() {
    int numAtoms = 5000;
    System system;
    for (int i = 0; i < numAtoms; i++)
        system.addParticle(1.0);
    OpenCLPlatform::PlatformData platformData(numAtoms, "", "");
    OpenCLContext& context = *platformData.contexts[0];
    context.initialize(system);
    context.getIntegrationUtilities().initRandomNumberGenerator(0);
    OpenCLArray<mm_float4>& random = context.getIntegrationUtilities().getRandom();
    context.getIntegrationUtilities().prepareRandomNumbers(random.getSize());
    const int numValues = random.getSize()*4;
    vector<mm_float4> values(numValues);
    random.download(values);
    float* data = reinterpret_cast<float*>(&values[0]);
    double mean = 0.0;
    double var = 0.0;
    double skew = 0.0;
    double kurtosis = 0.0;
    for (int i = 0; i < numValues; i++) {
        double value = data[i];
        mean += value;
        var += value*value;
        skew += value*value*value;
        kurtosis += value*value*value*value;
    }
    mean /= numValues;
    var /= numValues;
    skew /= numValues;
    kurtosis /= numValues;
    double c2 = var-mean*mean;
    double c3 = skew-3*var*mean+2*mean*mean*mean;
    double c4 = kurtosis-4*skew*mean-3*var*var+12*var*mean*mean-6*mean*mean*mean*mean;
    ASSERT_EQUAL_TOL(0.0, mean, 3.0/sqrt((double)numValues));
    ASSERT_EQUAL_TOL(1.0, c2, 3.0/pow(numValues, 1.0/3.0));
    ASSERT_EQUAL_TOL(0.0, c3, 3.0/pow(numValues, 1.0/4.0));
    ASSERT_EQUAL_TOL(0.0, c4, 3.0/pow(numValues, 1.0/4.0));
}

int main() {
    try {
        testGaussian();
    }
    catch(const exception& e) {
        cout << "exception: " << e.what() << endl;
        return 1;
    }
    cout << "Done" << endl;
    return 0;
}