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//@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.
//
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// 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
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#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::cerr;
using std::endl;
using Kokkos::parallel_reduce;
{
cerr << " -- 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*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;
cerr << "Pass: " << pass_mean
<< " " << pass_var
<< " " << mean_eps
<< " " << variance_eps
<< " " << covariance_eps
<< " || " << tolerance << endl;
}
{
cerr << " -- 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*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;
cerr << "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;
}
{
cerr << " -- 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*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;
cerr << "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::cerr;
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();
cerr << "Test Seed:" << ticks << endl;
RandomGenerator pool(ticks);
cerr << "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);
cerr << "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);
cerr << "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);
cerr << "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);
cerr << "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);
cerr << "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
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