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Update Kokkos library in LAMMPS to v3.0

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This commit is contained in:
Stan Moore
2020-03-25 14:08:39 -06:00
parent 0252d8c210
commit 60864e38d1
2169 changed files with 121406 additions and 126492 deletions
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495
lib/kokkos/algorithms/unit_tests/TestRandom.hpp
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@ -1,10 +1,11 @@
//@HEADER
// ************************************************************************
//
// Kokkos v. 2.0
// Copyright (2014) Sandia Corporation
// Kokkos v. 3.0
// Copyright (2020) National Technology & Engineering
// Solutions of Sandia, LLC (NTESS).
//
// Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
// Under the terms of Contract DE-NA0003525 with NTESS,
// the U.S. Government retains certain rights in this software.
//
// Redistribution and use in source and binary forms, with or without
@ -22,10 +23,10 @@
// 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
// THIS SOFTWARE IS PROVIDED BY NTESS "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
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NTESS 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
@ -54,18 +55,19 @@
namespace Test {
namespace Impl{
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
// (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)
#define HIST_DIM1D (HIST_DIM3D * HIST_DIM3D * HIST_DIM3D)
struct RandomProperties {
uint64_t count;
@ -77,37 +79,37 @@ struct RandomProperties {
KOKKOS_INLINE_FUNCTION
RandomProperties() {
count = 0;
mean = 0.0;
variance = 0.0;
count = 0;
mean = 0.0;
variance = 0.0;
covariance = 0.0;
min = 1e64;
max = -1e64;
min = 1e64;
max = -1e64;
}
KOKKOS_INLINE_FUNCTION
RandomProperties& operator+=(const RandomProperties& add) {
count += add.count;
mean += add.mean;
variance += add.variance;
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;
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;
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;
min = add.min < min ? add.min : min;
max = add.max > max ? add.max : max;
}
};
template<class GeneratorPool, class Scalar>
template <class GeneratorPool, class Scalar>
struct test_random_functor {
typedef typename GeneratorPool::generator_type rnd_type;
@ -123,38 +125,40 @@ struct test_random_functor {
// 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;
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;
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)
{}
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 {
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);
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.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.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);
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
@ -169,26 +173,32 @@ struct test_random_functor {
// 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 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_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);
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);
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>
template <class DeviceType>
struct test_histogram1d_functor {
typedef RandomProperties value_type;
typedef typename DeviceType::execution_space execution_space;
@ -200,34 +210,29 @@ struct test_histogram1d_functor {
// 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;
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)
{
}
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
{
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.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);
if (i < static_cast<size_type>(HIST_DIM1D - 1)) {
prop.covariance += (count - mean) * (density_1d(i + 1) - mean);
}
}
};
template<class DeviceType>
template <class DeviceType>
struct test_histogram3d_functor {
typedef RandomProperties value_type;
typedef typename DeviceType::execution_space execution_space;
@ -239,29 +244,28 @@ struct test_histogram3d_functor {
// 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;
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)
{}
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
{
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);
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);
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);
}
}
@ -270,212 +274,223 @@ struct test_histogram3d_functor {
//
// Templated test that uses the above functors.
//
template <class RandomGenerator,class Scalar>
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;
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)
{
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 Kokkos::parallel_reduce;
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);
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;
// 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);
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;
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 << "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);
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;
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;
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)
{
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");
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();
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);
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);
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);
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);
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);
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);
}
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 Impl
} // namespace Test
} // namespace Test
#endif //KOKKOS_TEST_UNORDERED_MAP_HPP
#endif // KOKKOS_TEST_UNORDERED_MAP_HPP