482 lines
19 KiB
C++
482 lines
19 KiB
C++
//@HEADER
|
|
// ************************************************************************
|
|
//
|
|
// Kokkos v. 2.0
|
|
// Copyright (2014) Sandia Corporation
|
|
//
|
|
// Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
|
|
// the U.S. Government retains certain rights in this software.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without
|
|
// modification, are permitted provided that the following conditions are
|
|
// met:
|
|
//
|
|
// 1. Redistributions of source code must retain the above copyright
|
|
// notice, this list of conditions and the following disclaimer.
|
|
//
|
|
// 2. 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.
|
|
//
|
|
// 3. Neither the name of the Corporation nor the names of the
|
|
// contributors may be used to endorse or promote products derived from
|
|
// this software without specific prior written permission.
|
|
//
|
|
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "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 SANDIA CORPORATION OR THE
|
|
// 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.
|
|
//
|
|
// Questions? Contact H. Carter Edwards (hcedwar@sandia.gov)
|
|
//
|
|
// ************************************************************************
|
|
//@HEADER
|
|
|
|
#ifndef KOKKOS_TEST_DUALVIEW_HPP
|
|
#define KOKKOS_TEST_DUALVIEW_HPP
|
|
|
|
#include <gtest/gtest.h>
|
|
#include <iostream>
|
|
#include <cstdlib>
|
|
#include <cstdio>
|
|
#include <impl/Kokkos_Timer.hpp>
|
|
#include <Kokkos_Core.hpp>
|
|
#include <Kokkos_Random.hpp>
|
|
#include <cmath>
|
|
#include <chrono>
|
|
|
|
namespace Test {
|
|
|
|
namespace Impl{
|
|
|
|
// This test runs the random number generators and uses some statistic tests to
|
|
// check the 'goodness' of the random numbers:
|
|
// (i) mean: the mean is expected to be 0.5*RAND_MAX
|
|
// (ii) variance: the variance is 1/3*mean*mean
|
|
// (iii) covariance: the covariance is 0
|
|
// (iv) 1-tupledistr: the mean, variance and covariance of a 1D Histrogram of random numbers
|
|
// (v) 3-tupledistr: the mean, variance and covariance of a 3D Histrogram of random numbers
|
|
|
|
#define HIST_DIM3D 24
|
|
#define HIST_DIM1D (HIST_DIM3D*HIST_DIM3D*HIST_DIM3D)
|
|
|
|
struct RandomProperties {
|
|
uint64_t count;
|
|
double mean;
|
|
double variance;
|
|
double covariance;
|
|
double min;
|
|
double max;
|
|
|
|
KOKKOS_INLINE_FUNCTION
|
|
RandomProperties() {
|
|
count = 0;
|
|
mean = 0.0;
|
|
variance = 0.0;
|
|
covariance = 0.0;
|
|
min = 1e64;
|
|
max = -1e64;
|
|
}
|
|
|
|
KOKKOS_INLINE_FUNCTION
|
|
RandomProperties& operator+=(const RandomProperties& add) {
|
|
count += add.count;
|
|
mean += add.mean;
|
|
variance += add.variance;
|
|
covariance += add.covariance;
|
|
min = add.min<min?add.min:min;
|
|
max = add.max>max?add.max:max;
|
|
return *this;
|
|
}
|
|
|
|
KOKKOS_INLINE_FUNCTION
|
|
void operator+=(const volatile RandomProperties& add) volatile {
|
|
count += add.count;
|
|
mean += add.mean;
|
|
variance += add.variance;
|
|
covariance += add.covariance;
|
|
min = add.min<min?add.min:min;
|
|
max = add.max>max?add.max:max;
|
|
}
|
|
};
|
|
|
|
template<class GeneratorPool, class Scalar>
|
|
struct test_random_functor {
|
|
typedef typename GeneratorPool::generator_type rnd_type;
|
|
|
|
typedef RandomProperties value_type;
|
|
typedef typename GeneratorPool::device_type device_type;
|
|
|
|
GeneratorPool rand_pool;
|
|
const double mean;
|
|
|
|
// NOTE (mfh 03 Nov 2014): Kokkos::rand::max() is supposed to define
|
|
// an exclusive upper bound on the range of random numbers that
|
|
// draw() can generate. However, for the float specialization, some
|
|
// implementations might violate this upper bound, due to rounding
|
|
// error. Just in case, we leave an extra space at the end of each
|
|
// dimension, in the View types below.
|
|
typedef Kokkos::View<int[HIST_DIM1D+1],typename GeneratorPool::device_type> type_1d;
|
|
type_1d density_1d;
|
|
typedef Kokkos::View<int[HIST_DIM3D+1][HIST_DIM3D+1][HIST_DIM3D+1],typename GeneratorPool::device_type> type_3d;
|
|
type_3d density_3d;
|
|
|
|
test_random_functor (GeneratorPool rand_pool_, type_1d d1d, type_3d d3d) :
|
|
rand_pool (rand_pool_),
|
|
mean (0.5*Kokkos::rand<rnd_type,Scalar>::max ()),
|
|
density_1d (d1d),
|
|
density_3d (d3d)
|
|
{}
|
|
|
|
KOKKOS_INLINE_FUNCTION
|
|
void operator() (int i, RandomProperties& prop) const {
|
|
using Kokkos::atomic_fetch_add;
|
|
|
|
rnd_type rand_gen = rand_pool.get_state();
|
|
for (int k = 0; k < 1024; ++k) {
|
|
const Scalar tmp = Kokkos::rand<rnd_type,Scalar>::draw(rand_gen);
|
|
prop.count++;
|
|
prop.mean += tmp;
|
|
prop.variance += (tmp-mean)*(tmp-mean);
|
|
const Scalar tmp2 = Kokkos::rand<rnd_type,Scalar>::draw(rand_gen);
|
|
prop.count++;
|
|
prop.mean += tmp2;
|
|
prop.variance += (tmp2-mean)*(tmp2-mean);
|
|
prop.covariance += (tmp-mean)*(tmp2-mean);
|
|
const Scalar tmp3 = Kokkos::rand<rnd_type,Scalar>::draw(rand_gen);
|
|
prop.count++;
|
|
prop.mean += tmp3;
|
|
prop.variance += (tmp3-mean)*(tmp3-mean);
|
|
prop.covariance += (tmp2-mean)*(tmp3-mean);
|
|
|
|
// NOTE (mfh 03 Nov 2014): Kokkos::rand::max() is supposed to
|
|
// define an exclusive upper bound on the range of random
|
|
// numbers that draw() can generate. However, for the float
|
|
// specialization, some implementations might violate this upper
|
|
// bound, due to rounding error. Just in case, we have left an
|
|
// extra space at the end of each dimension of density_1d and
|
|
// density_3d.
|
|
//
|
|
// Please note that those extra entries might not get counted in
|
|
// the histograms. However, if Kokkos::rand is broken and only
|
|
// returns values of max(), the histograms will still catch this
|
|
// indirectly, since none of the other values will be filled in.
|
|
|
|
const Scalar theMax = Kokkos::rand<rnd_type, Scalar>::max ();
|
|
|
|
const uint64_t ind1_1d = static_cast<uint64_t> (1.0 * HIST_DIM1D * tmp / theMax);
|
|
const uint64_t ind2_1d = static_cast<uint64_t> (1.0 * HIST_DIM1D * tmp2 / theMax);
|
|
const uint64_t ind3_1d = static_cast<uint64_t> (1.0 * HIST_DIM1D * tmp3 / theMax);
|
|
|
|
const uint64_t ind1_3d = static_cast<uint64_t> (1.0 * HIST_DIM3D * tmp / theMax);
|
|
const uint64_t ind2_3d = static_cast<uint64_t> (1.0 * HIST_DIM3D * tmp2 / theMax);
|
|
const uint64_t ind3_3d = static_cast<uint64_t> (1.0 * HIST_DIM3D * tmp3 / theMax);
|
|
|
|
atomic_fetch_add (&density_1d(ind1_1d), 1);
|
|
atomic_fetch_add (&density_1d(ind2_1d), 1);
|
|
atomic_fetch_add (&density_1d(ind3_1d), 1);
|
|
atomic_fetch_add (&density_3d(ind1_3d, ind2_3d, ind3_3d), 1);
|
|
}
|
|
rand_pool.free_state(rand_gen);
|
|
}
|
|
};
|
|
|
|
template<class DeviceType>
|
|
struct test_histogram1d_functor {
|
|
typedef RandomProperties value_type;
|
|
typedef typename DeviceType::execution_space execution_space;
|
|
typedef typename DeviceType::memory_space memory_space;
|
|
|
|
// NOTE (mfh 03 Nov 2014): Kokkos::rand::max() is supposed to define
|
|
// an exclusive upper bound on the range of random numbers that
|
|
// draw() can generate. However, for the float specialization, some
|
|
// implementations might violate this upper bound, due to rounding
|
|
// error. Just in case, we leave an extra space at the end of each
|
|
// dimension, in the View type below.
|
|
typedef Kokkos::View<int[HIST_DIM1D+1], memory_space> type_1d;
|
|
type_1d density_1d;
|
|
double mean;
|
|
|
|
test_histogram1d_functor (type_1d d1d, int num_draws) :
|
|
density_1d (d1d),
|
|
mean (1.0*num_draws/HIST_DIM1D*3)
|
|
{
|
|
}
|
|
|
|
KOKKOS_INLINE_FUNCTION void
|
|
operator() (const typename memory_space::size_type i,
|
|
RandomProperties& prop) const
|
|
{
|
|
typedef typename memory_space::size_type size_type;
|
|
const double count = density_1d(i);
|
|
prop.mean += count;
|
|
prop.variance += 1.0 * (count - mean) * (count - mean);
|
|
//prop.covariance += 1.0*count*count;
|
|
prop.min = count < prop.min ? count : prop.min;
|
|
prop.max = count > prop.max ? count : prop.max;
|
|
if (i < static_cast<size_type> (HIST_DIM1D-1)) {
|
|
prop.covariance += (count - mean) * (density_1d(i+1) - mean);
|
|
}
|
|
}
|
|
};
|
|
|
|
template<class DeviceType>
|
|
struct test_histogram3d_functor {
|
|
typedef RandomProperties value_type;
|
|
typedef typename DeviceType::execution_space execution_space;
|
|
typedef typename DeviceType::memory_space memory_space;
|
|
|
|
// NOTE (mfh 03 Nov 2014): Kokkos::rand::max() is supposed to define
|
|
// an exclusive upper bound on the range of random numbers that
|
|
// draw() can generate. However, for the float specialization, some
|
|
// implementations might violate this upper bound, due to rounding
|
|
// error. Just in case, we leave an extra space at the end of each
|
|
// dimension, in the View type below.
|
|
typedef Kokkos::View<int[HIST_DIM3D+1][HIST_DIM3D+1][HIST_DIM3D+1], memory_space> type_3d;
|
|
type_3d density_3d;
|
|
double mean;
|
|
|
|
test_histogram3d_functor (type_3d d3d, int num_draws) :
|
|
density_3d (d3d),
|
|
mean (1.0*num_draws/HIST_DIM1D)
|
|
{}
|
|
|
|
KOKKOS_INLINE_FUNCTION void
|
|
operator() (const typename memory_space::size_type i,
|
|
RandomProperties& prop) const
|
|
{
|
|
typedef typename memory_space::size_type size_type;
|
|
const double count = density_3d(i/(HIST_DIM3D*HIST_DIM3D),
|
|
(i % (HIST_DIM3D*HIST_DIM3D))/HIST_DIM3D,
|
|
i % HIST_DIM3D);
|
|
prop.mean += count;
|
|
prop.variance += (count - mean) * (count - mean);
|
|
if (i < static_cast<size_type> (HIST_DIM1D-1)) {
|
|
const double count_next = density_3d((i+1)/(HIST_DIM3D*HIST_DIM3D),
|
|
((i+1)%(HIST_DIM3D*HIST_DIM3D))/HIST_DIM3D,
|
|
(i+1)%HIST_DIM3D);
|
|
prop.covariance += (count - mean) * (count_next - mean);
|
|
}
|
|
}
|
|
};
|
|
|
|
//
|
|
// Templated test that uses the above functors.
|
|
//
|
|
template <class RandomGenerator,class Scalar>
|
|
struct test_random_scalar {
|
|
typedef typename RandomGenerator::generator_type rnd_type;
|
|
|
|
int pass_mean,pass_var,pass_covar;
|
|
int pass_hist1d_mean,pass_hist1d_var,pass_hist1d_covar;
|
|
int pass_hist3d_mean,pass_hist3d_var,pass_hist3d_covar;
|
|
|
|
test_random_scalar (typename test_random_functor<RandomGenerator,int>::type_1d& density_1d,
|
|
typename test_random_functor<RandomGenerator,int>::type_3d& density_3d,
|
|
RandomGenerator& pool,
|
|
unsigned int num_draws)
|
|
{
|
|
using std::cout;
|
|
using std::endl;
|
|
using Kokkos::parallel_reduce;
|
|
|
|
{
|
|
cout << " -- Testing randomness properties" << endl;
|
|
|
|
RandomProperties result;
|
|
typedef test_random_functor<RandomGenerator, Scalar> functor_type;
|
|
parallel_reduce (num_draws/1024, functor_type (pool, density_1d, density_3d), result);
|
|
|
|
//printf("Result: %lf %lf %lf\n",result.mean/num_draws/3,result.variance/num_draws/3,result.covariance/num_draws/2);
|
|
double tolerance = 1.6*std::sqrt(1.0/num_draws);
|
|
double mean_expect = 0.5*Kokkos::rand<rnd_type,Scalar>::max();
|
|
double variance_expect = 1.0/3.0*mean_expect*mean_expect;
|
|
double mean_eps = mean_expect/(result.mean/num_draws/3)-1.0;
|
|
double variance_eps = variance_expect/(result.variance/num_draws/3)-1.0;
|
|
double covariance_eps = result.covariance/num_draws/2/variance_expect;
|
|
pass_mean = ((-tolerance < mean_eps) &&
|
|
( tolerance > mean_eps)) ? 1:0;
|
|
pass_var = ((-1.5*tolerance < variance_eps) &&
|
|
( 1.5*tolerance > variance_eps)) ? 1:0;
|
|
pass_covar = ((-2.0*tolerance < covariance_eps) &&
|
|
( 2.0*tolerance > covariance_eps)) ? 1:0;
|
|
cout << "Pass: " << pass_mean
|
|
<< " " << pass_var
|
|
<< " " << mean_eps
|
|
<< " " << variance_eps
|
|
<< " " << covariance_eps
|
|
<< " || " << tolerance << endl;
|
|
}
|
|
{
|
|
cout << " -- Testing 1-D histogram" << endl;
|
|
|
|
RandomProperties result;
|
|
typedef test_histogram1d_functor<typename RandomGenerator::device_type> functor_type;
|
|
parallel_reduce (HIST_DIM1D, functor_type (density_1d, num_draws), result);
|
|
|
|
double tolerance = 6*std::sqrt(1.0/HIST_DIM1D);
|
|
double mean_expect = 1.0*num_draws*3/HIST_DIM1D;
|
|
double variance_expect = 1.0*num_draws*3/HIST_DIM1D*(1.0-1.0/HIST_DIM1D);
|
|
double covariance_expect = -1.0*num_draws*3/HIST_DIM1D/HIST_DIM1D;
|
|
double mean_eps = mean_expect/(result.mean/HIST_DIM1D)-1.0;
|
|
double variance_eps = variance_expect/(result.variance/HIST_DIM1D)-1.0;
|
|
double covariance_eps = (result.covariance/HIST_DIM1D - covariance_expect)/mean_expect;
|
|
pass_hist1d_mean = ((-0.0001 < mean_eps) &&
|
|
( 0.0001 > mean_eps)) ? 1:0;
|
|
pass_hist1d_var = ((-0.07 < variance_eps) &&
|
|
( 0.07 > variance_eps)) ? 1:0;
|
|
pass_hist1d_covar = ((-0.06 < covariance_eps) &&
|
|
( 0.06 > covariance_eps)) ? 1:0;
|
|
|
|
cout << "Density 1D: " << mean_eps
|
|
<< " " << variance_eps
|
|
<< " " << (result.covariance/HIST_DIM1D/HIST_DIM1D)
|
|
<< " || " << tolerance
|
|
<< " " << result.min
|
|
<< " " << result.max
|
|
<< " || " << result.variance/HIST_DIM1D
|
|
<< " " << 1.0*num_draws*3/HIST_DIM1D*(1.0-1.0/HIST_DIM1D)
|
|
<< " || " << result.covariance/HIST_DIM1D
|
|
<< " " << -1.0*num_draws*3/HIST_DIM1D/HIST_DIM1D
|
|
<< endl;
|
|
}
|
|
{
|
|
cout << " -- Testing 3-D histogram" << endl;
|
|
|
|
RandomProperties result;
|
|
typedef test_histogram3d_functor<typename RandomGenerator::device_type> functor_type;
|
|
parallel_reduce (HIST_DIM1D, functor_type (density_3d, num_draws), result);
|
|
|
|
double tolerance = 6*std::sqrt(1.0/HIST_DIM1D);
|
|
double mean_expect = 1.0*num_draws/HIST_DIM1D;
|
|
double variance_expect = 1.0*num_draws/HIST_DIM1D*(1.0-1.0/HIST_DIM1D);
|
|
double covariance_expect = -1.0*num_draws/HIST_DIM1D/HIST_DIM1D;
|
|
double mean_eps = mean_expect/(result.mean/HIST_DIM1D)-1.0;
|
|
double variance_eps = variance_expect/(result.variance/HIST_DIM1D)-1.0;
|
|
double covariance_eps = (result.covariance/HIST_DIM1D - covariance_expect)/mean_expect;
|
|
pass_hist3d_mean = ((-tolerance < mean_eps) &&
|
|
( tolerance > mean_eps)) ? 1:0;
|
|
pass_hist3d_var = ((-1.2*tolerance < variance_eps) &&
|
|
( 1.2*tolerance > variance_eps)) ? 1:0;
|
|
pass_hist3d_covar = ((-tolerance < covariance_eps) &&
|
|
( tolerance > covariance_eps)) ? 1:0;
|
|
|
|
cout << "Density 3D: " << mean_eps
|
|
<< " " << variance_eps
|
|
<< " " << result.covariance/HIST_DIM1D/HIST_DIM1D
|
|
<< " || " << tolerance
|
|
<< " " << result.min
|
|
<< " " << result.max << endl;
|
|
}
|
|
}
|
|
};
|
|
|
|
template <class RandomGenerator>
|
|
void test_random(unsigned int num_draws)
|
|
{
|
|
using std::cout;
|
|
using std::endl;
|
|
typename test_random_functor<RandomGenerator,int>::type_1d density_1d("D1d");
|
|
typename test_random_functor<RandomGenerator,int>::type_3d density_3d("D3d");
|
|
|
|
|
|
uint64_t ticks = std::chrono::high_resolution_clock::now().time_since_epoch().count();
|
|
cout << "Test Seed:" << ticks << endl;
|
|
|
|
RandomGenerator pool(ticks);
|
|
|
|
cout << "Test Scalar=int" << endl;
|
|
test_random_scalar<RandomGenerator,int> test_int(density_1d,density_3d,pool,num_draws);
|
|
ASSERT_EQ( test_int.pass_mean,1);
|
|
ASSERT_EQ( test_int.pass_var,1);
|
|
ASSERT_EQ( test_int.pass_covar,1);
|
|
ASSERT_EQ( test_int.pass_hist1d_mean,1);
|
|
ASSERT_EQ( test_int.pass_hist1d_var,1);
|
|
ASSERT_EQ( test_int.pass_hist1d_covar,1);
|
|
ASSERT_EQ( test_int.pass_hist3d_mean,1);
|
|
ASSERT_EQ( test_int.pass_hist3d_var,1);
|
|
ASSERT_EQ( test_int.pass_hist3d_covar,1);
|
|
deep_copy(density_1d,0);
|
|
deep_copy(density_3d,0);
|
|
|
|
cout << "Test Scalar=unsigned int" << endl;
|
|
test_random_scalar<RandomGenerator,unsigned int> test_uint(density_1d,density_3d,pool,num_draws);
|
|
ASSERT_EQ( test_uint.pass_mean,1);
|
|
ASSERT_EQ( test_uint.pass_var,1);
|
|
ASSERT_EQ( test_uint.pass_covar,1);
|
|
ASSERT_EQ( test_uint.pass_hist1d_mean,1);
|
|
ASSERT_EQ( test_uint.pass_hist1d_var,1);
|
|
ASSERT_EQ( test_uint.pass_hist1d_covar,1);
|
|
ASSERT_EQ( test_uint.pass_hist3d_mean,1);
|
|
ASSERT_EQ( test_uint.pass_hist3d_var,1);
|
|
ASSERT_EQ( test_uint.pass_hist3d_covar,1);
|
|
deep_copy(density_1d,0);
|
|
deep_copy(density_3d,0);
|
|
|
|
cout << "Test Scalar=int64_t" << endl;
|
|
test_random_scalar<RandomGenerator,int64_t> test_int64(density_1d,density_3d,pool,num_draws);
|
|
ASSERT_EQ( test_int64.pass_mean,1);
|
|
ASSERT_EQ( test_int64.pass_var,1);
|
|
ASSERT_EQ( test_int64.pass_covar,1);
|
|
ASSERT_EQ( test_int64.pass_hist1d_mean,1);
|
|
ASSERT_EQ( test_int64.pass_hist1d_var,1);
|
|
ASSERT_EQ( test_int64.pass_hist1d_covar,1);
|
|
ASSERT_EQ( test_int64.pass_hist3d_mean,1);
|
|
ASSERT_EQ( test_int64.pass_hist3d_var,1);
|
|
ASSERT_EQ( test_int64.pass_hist3d_covar,1);
|
|
deep_copy(density_1d,0);
|
|
deep_copy(density_3d,0);
|
|
|
|
cout << "Test Scalar=uint64_t" << endl;
|
|
test_random_scalar<RandomGenerator,uint64_t> test_uint64(density_1d,density_3d,pool,num_draws);
|
|
ASSERT_EQ( test_uint64.pass_mean,1);
|
|
ASSERT_EQ( test_uint64.pass_var,1);
|
|
ASSERT_EQ( test_uint64.pass_covar,1);
|
|
ASSERT_EQ( test_uint64.pass_hist1d_mean,1);
|
|
ASSERT_EQ( test_uint64.pass_hist1d_var,1);
|
|
ASSERT_EQ( test_uint64.pass_hist1d_covar,1);
|
|
ASSERT_EQ( test_uint64.pass_hist3d_mean,1);
|
|
ASSERT_EQ( test_uint64.pass_hist3d_var,1);
|
|
ASSERT_EQ( test_uint64.pass_hist3d_covar,1);
|
|
deep_copy(density_1d,0);
|
|
deep_copy(density_3d,0);
|
|
|
|
cout << "Test Scalar=float" << endl;
|
|
test_random_scalar<RandomGenerator,float> test_float(density_1d,density_3d,pool,num_draws);
|
|
ASSERT_EQ( test_float.pass_mean,1);
|
|
ASSERT_EQ( test_float.pass_var,1);
|
|
ASSERT_EQ( test_float.pass_covar,1);
|
|
ASSERT_EQ( test_float.pass_hist1d_mean,1);
|
|
ASSERT_EQ( test_float.pass_hist1d_var,1);
|
|
ASSERT_EQ( test_float.pass_hist1d_covar,1);
|
|
ASSERT_EQ( test_float.pass_hist3d_mean,1);
|
|
ASSERT_EQ( test_float.pass_hist3d_var,1);
|
|
ASSERT_EQ( test_float.pass_hist3d_covar,1);
|
|
deep_copy(density_1d,0);
|
|
deep_copy(density_3d,0);
|
|
|
|
cout << "Test Scalar=double" << endl;
|
|
test_random_scalar<RandomGenerator,double> test_double(density_1d,density_3d,pool,num_draws);
|
|
ASSERT_EQ( test_double.pass_mean,1);
|
|
ASSERT_EQ( test_double.pass_var,1);
|
|
ASSERT_EQ( test_double.pass_covar,1);
|
|
ASSERT_EQ( test_double.pass_hist1d_mean,1);
|
|
ASSERT_EQ( test_double.pass_hist1d_var,1);
|
|
ASSERT_EQ( test_double.pass_hist1d_covar,1);
|
|
ASSERT_EQ( test_double.pass_hist3d_mean,1);
|
|
ASSERT_EQ( test_double.pass_hist3d_var,1);
|
|
ASSERT_EQ( test_double.pass_hist3d_covar,1);
|
|
}
|
|
}
|
|
|
|
} // namespace Test
|
|
|
|
#endif //KOKKOS_TEST_UNORDERED_MAP_HPP
|