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Squashed 'boost/' content from commit b4feb19f2
git-subtree-dir: boost git-subtree-split: b4feb19f287ee92d87a9624b5d36b7cf46aeadeb
This commit is contained in:
@@ -0,0 +1,34 @@
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# Copyright 2011-2014 Mario Mulansky
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# Copyright 2011-2012 Karsten Ahnert
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#
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# Distributed under the Boost Software License, Version 1.0.
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# (See accompanying file LICENSE_1_0.txt or
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# copy at http://www.boost.org/LICENSE_1_0.txt)
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# make sure BOOST_ROOT is pointing to your boost directory
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# otherwise, set it here:
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# BOOST_ROOT = /path/to/boost
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# path to the cuda installation
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CUDA_ROOT = /usr/local/cuda
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# target architecture
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ARCH = sm_13
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NVCC = $(CUDA_ROOT)/bin/nvcc
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INCLUDES += -I../../include/ -I$(BOOST_ROOT)
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NVCCFLAGS = -O3 $(INCLUDES) -arch $(ARCH)
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%.o : %.cu
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$(NVCC) $(NVCCFLAGS) -c $< -o $@
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% : %.o
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$(NVCC) $(NVCCFLAGS) -o $@ $<
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all : phase_oscillator_chain phase_oscillator_ensemble lorenz_parameters relaxation
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clean :
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-rm *~ *.o phase_oscillator_chain phase_oscillator_ensemble lorenz_parameters relaxation
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@@ -0,0 +1,296 @@
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/*
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Copyright 2011-2012 Karsten Ahnert
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Copyright 2011-2013 Mario Mulansky
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Distributed under the Boost Software License, Version 1.0.
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(See accompanying file LICENSE_1_0.txt or
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copy at http://www.boost.org/LICENSE_1_0.txt)
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*/
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#include <iostream>
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#include <cmath>
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#include <utility>
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#include <thrust/device_vector.h>
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#include <thrust/reduce.h>
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#include <thrust/functional.h>
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#include <boost/numeric/odeint.hpp>
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#include <boost/numeric/odeint/external/thrust/thrust.hpp>
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#include <boost/random/mersenne_twister.hpp>
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#include <boost/random/uniform_real.hpp>
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#include <boost/random/variate_generator.hpp>
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using namespace std;
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using namespace boost::numeric::odeint;
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//change this to float if your device does not support double computation
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typedef double value_type;
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//change this to host_vector< ... > of you want to run on CPU
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typedef thrust::device_vector< value_type > state_type;
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typedef thrust::device_vector< size_t > index_vector_type;
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// typedef thrust::host_vector< value_type > state_type;
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// typedef thrust::host_vector< size_t > index_vector_type;
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const value_type sigma = 10.0;
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const value_type b = 8.0 / 3.0;
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//[ thrust_lorenz_parameters_define_simple_system
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struct lorenz_system
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{
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struct lorenz_functor
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{
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template< class T >
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__host__ __device__
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void operator()( T t ) const
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{
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// unpack the parameter we want to vary and the Lorenz variables
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value_type R = thrust::get< 3 >( t );
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value_type x = thrust::get< 0 >( t );
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value_type y = thrust::get< 1 >( t );
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value_type z = thrust::get< 2 >( t );
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thrust::get< 4 >( t ) = sigma * ( y - x );
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thrust::get< 5 >( t ) = R * x - y - x * z;
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thrust::get< 6 >( t ) = -b * z + x * y ;
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}
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};
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lorenz_system( size_t N , const state_type &beta )
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: m_N( N ) , m_beta( beta ) { }
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template< class State , class Deriv >
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void operator()( const State &x , Deriv &dxdt , value_type t ) const
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{
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thrust::for_each(
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thrust::make_zip_iterator( thrust::make_tuple(
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boost::begin( x ) ,
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boost::begin( x ) + m_N ,
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boost::begin( x ) + 2 * m_N ,
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m_beta.begin() ,
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boost::begin( dxdt ) ,
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boost::begin( dxdt ) + m_N ,
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boost::begin( dxdt ) + 2 * m_N ) ) ,
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thrust::make_zip_iterator( thrust::make_tuple(
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boost::begin( x ) + m_N ,
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boost::begin( x ) + 2 * m_N ,
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boost::begin( x ) + 3 * m_N ,
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m_beta.begin() ,
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boost::begin( dxdt ) + m_N ,
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boost::begin( dxdt ) + 2 * m_N ,
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boost::begin( dxdt ) + 3 * m_N ) ) ,
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lorenz_functor() );
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}
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size_t m_N;
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const state_type &m_beta;
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};
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//]
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struct lorenz_perturbation_system
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{
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struct lorenz_perturbation_functor
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{
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template< class T >
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__host__ __device__
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void operator()( T t ) const
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{
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value_type R = thrust::get< 1 >( t );
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value_type x = thrust::get< 0 >( thrust::get< 0 >( t ) );
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value_type y = thrust::get< 1 >( thrust::get< 0 >( t ) );
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value_type z = thrust::get< 2 >( thrust::get< 0 >( t ) );
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value_type dx = thrust::get< 3 >( thrust::get< 0 >( t ) );
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value_type dy = thrust::get< 4 >( thrust::get< 0 >( t ) );
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value_type dz = thrust::get< 5 >( thrust::get< 0 >( t ) );
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thrust::get< 0 >( thrust::get< 2 >( t ) ) = sigma * ( y - x );
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thrust::get< 1 >( thrust::get< 2 >( t ) ) = R * x - y - x * z;
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thrust::get< 2 >( thrust::get< 2 >( t ) ) = -b * z + x * y ;
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thrust::get< 3 >( thrust::get< 2 >( t ) ) = sigma * ( dy - dx );
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thrust::get< 4 >( thrust::get< 2 >( t ) ) = ( R - z ) * dx - dy - x * dz;
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thrust::get< 5 >( thrust::get< 2 >( t ) ) = y * dx + x * dy - b * dz;
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}
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};
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lorenz_perturbation_system( size_t N , const state_type &beta )
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: m_N( N ) , m_beta( beta ) { }
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template< class State , class Deriv >
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void operator()( const State &x , Deriv &dxdt , value_type t ) const
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{
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thrust::for_each(
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thrust::make_zip_iterator( thrust::make_tuple(
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thrust::make_zip_iterator( thrust::make_tuple(
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boost::begin( x ) ,
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boost::begin( x ) + m_N ,
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boost::begin( x ) + 2 * m_N ,
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boost::begin( x ) + 3 * m_N ,
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boost::begin( x ) + 4 * m_N ,
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boost::begin( x ) + 5 * m_N ) ) ,
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m_beta.begin() ,
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thrust::make_zip_iterator( thrust::make_tuple(
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boost::begin( dxdt ) ,
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boost::begin( dxdt ) + m_N ,
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boost::begin( dxdt ) + 2 * m_N ,
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boost::begin( dxdt ) + 3 * m_N ,
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boost::begin( dxdt ) + 4 * m_N ,
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boost::begin( dxdt ) + 5 * m_N ) )
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) ) ,
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thrust::make_zip_iterator( thrust::make_tuple(
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thrust::make_zip_iterator( thrust::make_tuple(
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boost::begin( x ) + m_N ,
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boost::begin( x ) + 2 * m_N ,
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boost::begin( x ) + 3 * m_N ,
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boost::begin( x ) + 4 * m_N ,
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boost::begin( x ) + 5 * m_N ,
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boost::begin( x ) + 6 * m_N ) ) ,
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m_beta.begin() ,
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thrust::make_zip_iterator( thrust::make_tuple(
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boost::begin( dxdt ) + m_N ,
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boost::begin( dxdt ) + 2 * m_N ,
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boost::begin( dxdt ) + 3 * m_N ,
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boost::begin( dxdt ) + 4 * m_N ,
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boost::begin( dxdt ) + 5 * m_N ,
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boost::begin( dxdt ) + 6 * m_N ) )
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) ) ,
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lorenz_perturbation_functor() );
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}
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size_t m_N;
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const state_type &m_beta;
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};
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struct lyap_observer
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{
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//[thrust_lorenz_parameters_observer_functor
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struct lyap_functor
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{
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template< class T >
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__host__ __device__
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void operator()( T t ) const
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{
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value_type &dx = thrust::get< 0 >( t );
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value_type &dy = thrust::get< 1 >( t );
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value_type &dz = thrust::get< 2 >( t );
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value_type norm = sqrt( dx * dx + dy * dy + dz * dz );
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dx /= norm;
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dy /= norm;
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dz /= norm;
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thrust::get< 3 >( t ) += log( norm );
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}
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};
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//]
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lyap_observer( size_t N , size_t every = 100 )
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: m_N( N ) , m_lyap( N ) , m_every( every ) , m_count( 0 )
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{
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thrust::fill( m_lyap.begin() , m_lyap.end() , 0.0 );
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}
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template< class Lyap >
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void fill_lyap( Lyap &lyap )
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{
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thrust::copy( m_lyap.begin() , m_lyap.end() , lyap.begin() );
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for( size_t i=0 ; i<lyap.size() ; ++i )
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lyap[i] /= m_t_overall;
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}
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template< class State >
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void operator()( State &x , value_type t )
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{
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if( ( m_count != 0 ) && ( ( m_count % m_every ) == 0 ) )
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{
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thrust::for_each(
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thrust::make_zip_iterator( thrust::make_tuple(
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boost::begin( x ) + 3 * m_N ,
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boost::begin( x ) + 4 * m_N ,
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boost::begin( x ) + 5 * m_N ,
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m_lyap.begin() ) ) ,
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thrust::make_zip_iterator( thrust::make_tuple(
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boost::begin( x ) + 4 * m_N ,
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boost::begin( x ) + 5 * m_N ,
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boost::begin( x ) + 6 * m_N ,
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m_lyap.end() ) ) ,
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lyap_functor() );
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clog << t << "\n";
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}
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++m_count;
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m_t_overall = t;
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}
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size_t m_N;
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state_type m_lyap;
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size_t m_every;
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size_t m_count;
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value_type m_t_overall;
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};
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const size_t N = 1024*2;
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const value_type dt = 0.01;
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int main( int arc , char* argv[] )
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{
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int driver_version , runtime_version;
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cudaDriverGetVersion( &driver_version );
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cudaRuntimeGetVersion ( &runtime_version );
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cout << driver_version << "\t" << runtime_version << endl;
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//[ thrust_lorenz_parameters_define_beta
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vector< value_type > beta_host( N );
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const value_type beta_min = 0.0 , beta_max = 56.0;
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for( size_t i=0 ; i<N ; ++i )
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beta_host[i] = beta_min + value_type( i ) * ( beta_max - beta_min ) / value_type( N - 1 );
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state_type beta = beta_host;
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//]
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//[ thrust_lorenz_parameters_integration
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state_type x( 6 * N );
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// initialize x,y,z
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thrust::fill( x.begin() , x.begin() + 3 * N , 10.0 );
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// initial dx
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thrust::fill( x.begin() + 3 * N , x.begin() + 4 * N , 1.0 );
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// initialize dy,dz
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thrust::fill( x.begin() + 4 * N , x.end() , 0.0 );
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// create error stepper, can be used with make_controlled or make_dense_output
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typedef runge_kutta_dopri5< state_type , value_type , state_type , value_type > stepper_type;
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lorenz_system lorenz( N , beta );
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lorenz_perturbation_system lorenz_perturbation( N , beta );
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lyap_observer obs( N , 1 );
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// calculate transients
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integrate_adaptive( make_controlled( 1.0e-6 , 1.0e-6 , stepper_type() ) , lorenz , std::make_pair( x.begin() , x.begin() + 3 * N ) , 0.0 , 10.0 , dt );
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// calculate the Lyapunov exponents -- the main loop
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double t = 0.0;
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while( t < 10000.0 )
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{
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integrate_adaptive( make_controlled( 1.0e-6 , 1.0e-6 , stepper_type() ) , lorenz_perturbation , x , t , t + 1.0 , 0.1 );
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t += 1.0;
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obs( x , t );
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}
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vector< value_type > lyap( N );
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obs.fill_lyap( lyap );
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for( size_t i=0 ; i<N ; ++i )
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cout << beta_host[i] << "\t" << lyap[i] << "\n";
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//]
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return 0;
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}
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@@ -0,0 +1,156 @@
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/*
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Copyright 2011-2013 Mario Mulansky
|
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Copyright 2011 Karsten Ahnert
|
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|
||||
Distributed under the Boost Software License, Version 1.0.
|
||||
(See accompanying file LICENSE_1_0.txt or
|
||||
copy at http://www.boost.org/LICENSE_1_0.txt)
|
||||
*/
|
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|
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/*
|
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* This example shows how to use odeint on CUDA devices with thrust.
|
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* Note that we require at least Version 3.2 of the nVidia CUDA SDK
|
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* and the thrust library should be installed in the CUDA include
|
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* folder.
|
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*
|
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* As example we use a chain of phase oscillators with nearest neighbour
|
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* coupling, as described in:
|
||||
*
|
||||
* Avis H. Cohen, Philip J. Holmes and Richard H. Rand:
|
||||
* JOURNAL OF MATHEMATICAL BIOLOGY Volume 13, Number 3, 345-369,
|
||||
*
|
||||
*/
|
||||
|
||||
#include <iostream>
|
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#include <cmath>
|
||||
|
||||
#include <thrust/device_vector.h>
|
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#include <thrust/iterator/permutation_iterator.h>
|
||||
#include <thrust/iterator/counting_iterator.h>
|
||||
|
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#include <boost/numeric/odeint/stepper/runge_kutta4.hpp>
|
||||
#include <boost/numeric/odeint/integrate/integrate_const.hpp>
|
||||
#include <boost/numeric/odeint/external/thrust/thrust.hpp>
|
||||
|
||||
using namespace std;
|
||||
|
||||
using namespace boost::numeric::odeint;
|
||||
|
||||
|
||||
//change this to float if your device does not support double computation
|
||||
typedef double value_type;
|
||||
|
||||
|
||||
//[ thrust_phase_chain_system
|
||||
//change this to host_vector< ... > if you want to run on CPU
|
||||
typedef thrust::device_vector< value_type > state_type;
|
||||
typedef thrust::device_vector< size_t > index_vector_type;
|
||||
//typedef thrust::host_vector< value_type > state_type;
|
||||
//typedef thrust::host_vector< size_t > index_vector_type;
|
||||
|
||||
//<-
|
||||
/*
|
||||
* This implements the rhs of the dynamical equation:
|
||||
* \phi'_0 = \omega_0 + sin( \phi_1 - \phi_0 )
|
||||
* \phi'_i = \omega_i + sin( \phi_i+1 - \phi_i ) + sin( \phi_i - \phi_i-1 )
|
||||
* \phi'_N-1 = \omega_N-1 + sin( \phi_N-1 - \phi_N-2 )
|
||||
*/
|
||||
//->
|
||||
class phase_oscillators
|
||||
{
|
||||
|
||||
public:
|
||||
|
||||
struct sys_functor
|
||||
{
|
||||
template< class Tuple >
|
||||
__host__ __device__
|
||||
void operator()( Tuple t ) // this functor works on tuples of values
|
||||
{
|
||||
// first, unpack the tuple into value, neighbors and omega
|
||||
const value_type phi = thrust::get<0>(t);
|
||||
const value_type phi_left = thrust::get<1>(t); // left neighbor
|
||||
const value_type phi_right = thrust::get<2>(t); // right neighbor
|
||||
const value_type omega = thrust::get<3>(t);
|
||||
// the dynamical equation
|
||||
thrust::get<4>(t) = omega + sin( phi_right - phi ) + sin( phi - phi_left );
|
||||
}
|
||||
};
|
||||
|
||||
phase_oscillators( const state_type &omega )
|
||||
: m_omega( omega ) , m_N( omega.size() ) , m_prev( omega.size() ) , m_next( omega.size() )
|
||||
{
|
||||
// build indices pointing to left and right neighbours
|
||||
thrust::counting_iterator<size_t> c( 0 );
|
||||
thrust::copy( c , c+m_N-1 , m_prev.begin()+1 );
|
||||
m_prev[0] = 0; // m_prev = { 0 , 0 , 1 , 2 , 3 , ... , N-1 }
|
||||
|
||||
thrust::copy( c+1 , c+m_N , m_next.begin() );
|
||||
m_next[m_N-1] = m_N-1; // m_next = { 1 , 2 , 3 , ... , N-1 , N-1 }
|
||||
}
|
||||
|
||||
void operator() ( const state_type &x , state_type &dxdt , const value_type dt )
|
||||
{
|
||||
thrust::for_each(
|
||||
thrust::make_zip_iterator(
|
||||
thrust::make_tuple(
|
||||
x.begin() ,
|
||||
thrust::make_permutation_iterator( x.begin() , m_prev.begin() ) ,
|
||||
thrust::make_permutation_iterator( x.begin() , m_next.begin() ) ,
|
||||
m_omega.begin() ,
|
||||
dxdt.begin()
|
||||
) ),
|
||||
thrust::make_zip_iterator(
|
||||
thrust::make_tuple(
|
||||
x.end() ,
|
||||
thrust::make_permutation_iterator( x.begin() , m_prev.end() ) ,
|
||||
thrust::make_permutation_iterator( x.begin() , m_next.end() ) ,
|
||||
m_omega.end() ,
|
||||
dxdt.end()) ) ,
|
||||
sys_functor()
|
||||
);
|
||||
}
|
||||
|
||||
private:
|
||||
|
||||
const state_type &m_omega;
|
||||
const size_t m_N;
|
||||
index_vector_type m_prev;
|
||||
index_vector_type m_next;
|
||||
};
|
||||
//]
|
||||
|
||||
const size_t N = 32768;
|
||||
const value_type pi = 3.1415926535897932384626433832795029;
|
||||
const value_type epsilon = 6.0 / ( N * N ); // should be < 8/N^2 to see phase locking
|
||||
const value_type dt = 0.1;
|
||||
|
||||
int main( int arc , char* argv[] )
|
||||
{
|
||||
//[ thrust_phase_chain_integration
|
||||
// create initial conditions and omegas on host:
|
||||
vector< value_type > x_host( N );
|
||||
vector< value_type > omega_host( N );
|
||||
for( size_t i=0 ; i<N ; ++i )
|
||||
{
|
||||
x_host[i] = 2.0 * pi * drand48();
|
||||
omega_host[i] = ( N - i ) * epsilon; // decreasing frequencies
|
||||
}
|
||||
|
||||
// copy to device
|
||||
state_type x = x_host;
|
||||
state_type omega = omega_host;
|
||||
|
||||
// create stepper
|
||||
runge_kutta4< state_type , value_type , state_type , value_type > stepper;
|
||||
|
||||
// create phase oscillator system function
|
||||
phase_oscillators sys( omega );
|
||||
|
||||
// integrate
|
||||
integrate_const( stepper , sys , x , 0.0 , 10.0 , dt );
|
||||
|
||||
thrust::copy( x.begin() , x.end() , std::ostream_iterator< value_type >( std::cout , "\n" ) );
|
||||
std::cout << std::endl;
|
||||
//]
|
||||
}
|
||||
@@ -0,0 +1,280 @@
|
||||
/*
|
||||
The example how the phase_oscillator ensemble can be implemented using CUDA and thrust
|
||||
|
||||
Copyright 2011-2013 Mario Mulansky
|
||||
Copyright 2011 Karsten Ahnert
|
||||
|
||||
Distributed under the Boost Software License, Version 1.0.
|
||||
(See accompanying file LICENSE_1_0.txt or
|
||||
copy at http://www.boost.org/LICENSE_1_0.txt)
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
#include <cmath>
|
||||
#include <utility>
|
||||
|
||||
#include <thrust/device_vector.h>
|
||||
#include <thrust/reduce.h>
|
||||
#include <thrust/functional.h>
|
||||
|
||||
#include <boost/numeric/odeint.hpp>
|
||||
#include <boost/numeric/odeint/external/thrust/thrust.hpp>
|
||||
|
||||
#include <boost/timer.hpp>
|
||||
#include <boost/random/cauchy_distribution.hpp>
|
||||
|
||||
using namespace std;
|
||||
|
||||
using namespace boost::numeric::odeint;
|
||||
|
||||
/*
|
||||
* Sorry for that dirty hack, but nvcc has large problems with boost::random.
|
||||
*
|
||||
* Nevertheless we need the cauchy distribution from boost::random, and therefore
|
||||
* we need a generator. Here it is:
|
||||
*/
|
||||
struct drand48_generator
|
||||
{
|
||||
typedef double result_type;
|
||||
result_type operator()( void ) const { return drand48(); }
|
||||
result_type min( void ) const { return 0.0; }
|
||||
result_type max( void ) const { return 1.0; }
|
||||
};
|
||||
|
||||
//[ thrust_phase_ensemble_state_type
|
||||
//change this to float if your device does not support double computation
|
||||
typedef double value_type;
|
||||
|
||||
//change this to host_vector< ... > of you want to run on CPU
|
||||
typedef thrust::device_vector< value_type > state_type;
|
||||
// typedef thrust::host_vector< value_type > state_type;
|
||||
//]
|
||||
|
||||
|
||||
//[ thrust_phase_ensemble_mean_field_calculator
|
||||
struct mean_field_calculator
|
||||
{
|
||||
struct sin_functor : public thrust::unary_function< value_type , value_type >
|
||||
{
|
||||
__host__ __device__
|
||||
value_type operator()( value_type x) const
|
||||
{
|
||||
return sin( x );
|
||||
}
|
||||
};
|
||||
|
||||
struct cos_functor : public thrust::unary_function< value_type , value_type >
|
||||
{
|
||||
__host__ __device__
|
||||
value_type operator()( value_type x) const
|
||||
{
|
||||
return cos( x );
|
||||
}
|
||||
};
|
||||
|
||||
static std::pair< value_type , value_type > get_mean( const state_type &x )
|
||||
{
|
||||
//[ thrust_phase_ensemble_sin_sum
|
||||
value_type sin_sum = thrust::reduce(
|
||||
thrust::make_transform_iterator( x.begin() , sin_functor() ) ,
|
||||
thrust::make_transform_iterator( x.end() , sin_functor() ) );
|
||||
//]
|
||||
value_type cos_sum = thrust::reduce(
|
||||
thrust::make_transform_iterator( x.begin() , cos_functor() ) ,
|
||||
thrust::make_transform_iterator( x.end() , cos_functor() ) );
|
||||
|
||||
cos_sum /= value_type( x.size() );
|
||||
sin_sum /= value_type( x.size() );
|
||||
|
||||
value_type K = sqrt( cos_sum * cos_sum + sin_sum * sin_sum );
|
||||
value_type Theta = atan2( sin_sum , cos_sum );
|
||||
|
||||
return std::make_pair( K , Theta );
|
||||
}
|
||||
};
|
||||
//]
|
||||
|
||||
|
||||
|
||||
//[ thrust_phase_ensemble_sys_function
|
||||
class phase_oscillator_ensemble
|
||||
{
|
||||
|
||||
public:
|
||||
|
||||
struct sys_functor
|
||||
{
|
||||
value_type m_K , m_Theta , m_epsilon;
|
||||
|
||||
sys_functor( value_type K , value_type Theta , value_type epsilon )
|
||||
: m_K( K ) , m_Theta( Theta ) , m_epsilon( epsilon ) { }
|
||||
|
||||
template< class Tuple >
|
||||
__host__ __device__
|
||||
void operator()( Tuple t )
|
||||
{
|
||||
thrust::get<2>(t) = thrust::get<1>(t) + m_epsilon * m_K * sin( m_Theta - thrust::get<0>(t) );
|
||||
}
|
||||
};
|
||||
|
||||
// ...
|
||||
//<-
|
||||
phase_oscillator_ensemble( size_t N , value_type g = 1.0 , value_type epsilon = 1.0 )
|
||||
: m_omega() , m_N( N ) , m_epsilon( epsilon )
|
||||
{
|
||||
create_frequencies( g );
|
||||
}
|
||||
|
||||
void create_frequencies( value_type g )
|
||||
{
|
||||
boost::cauchy_distribution< value_type > cauchy( 0.0 , g );
|
||||
// boost::variate_generator< boost::mt19937&, boost::cauchy_distribution< value_type > > gen( rng , cauchy );
|
||||
drand48_generator d48;
|
||||
vector< value_type > omega( m_N );
|
||||
for( size_t i=0 ; i<m_N ; ++i )
|
||||
omega[i] = cauchy( d48 );
|
||||
// generate( omega.begin() , omega.end() , gen );
|
||||
m_omega = omega;
|
||||
}
|
||||
|
||||
void set_epsilon( value_type epsilon ) { m_epsilon = epsilon; }
|
||||
|
||||
value_type get_epsilon( void ) const { return m_epsilon; }
|
||||
//->
|
||||
|
||||
void operator() ( const state_type &x , state_type &dxdt , const value_type dt ) const
|
||||
{
|
||||
std::pair< value_type , value_type > mean_field = mean_field_calculator::get_mean( x );
|
||||
|
||||
thrust::for_each(
|
||||
thrust::make_zip_iterator( thrust::make_tuple( x.begin() , m_omega.begin() , dxdt.begin() ) ),
|
||||
thrust::make_zip_iterator( thrust::make_tuple( x.end() , m_omega.end() , dxdt.end()) ) ,
|
||||
sys_functor( mean_field.first , mean_field.second , m_epsilon )
|
||||
);
|
||||
}
|
||||
|
||||
// ...
|
||||
//<-
|
||||
private:
|
||||
|
||||
state_type m_omega;
|
||||
const size_t m_N;
|
||||
value_type m_epsilon;
|
||||
//->
|
||||
};
|
||||
//]
|
||||
|
||||
|
||||
//[ thrust_phase_ensemble_observer
|
||||
struct statistics_observer
|
||||
{
|
||||
value_type m_K_mean;
|
||||
size_t m_count;
|
||||
|
||||
statistics_observer( void )
|
||||
: m_K_mean( 0.0 ) , m_count( 0 ) { }
|
||||
|
||||
template< class State >
|
||||
void operator()( const State &x , value_type t )
|
||||
{
|
||||
std::pair< value_type , value_type > mean = mean_field_calculator::get_mean( x );
|
||||
m_K_mean += mean.first;
|
||||
++m_count;
|
||||
}
|
||||
|
||||
value_type get_K_mean( void ) const { return ( m_count != 0 ) ? m_K_mean / value_type( m_count ) : 0.0 ; }
|
||||
|
||||
void reset( void ) { m_K_mean = 0.0; m_count = 0; }
|
||||
};
|
||||
//]
|
||||
|
||||
|
||||
|
||||
// const size_t N = 16384 * 128;
|
||||
const size_t N = 16384;
|
||||
const value_type pi = 3.1415926535897932384626433832795029;
|
||||
const value_type dt = 0.1;
|
||||
const value_type d_epsilon = 0.1;
|
||||
const value_type epsilon_min = 0.0;
|
||||
const value_type epsilon_max = 5.0;
|
||||
const value_type t_transients = 10.0;
|
||||
const value_type t_max = 100.0;
|
||||
|
||||
int main( int arc , char* argv[] )
|
||||
{
|
||||
// initial conditions on host
|
||||
vector< value_type > x_host( N );
|
||||
for( size_t i=0 ; i<N ; ++i ) x_host[i] = 2.0 * pi * drand48();
|
||||
|
||||
//[ thrust_phase_ensemble_system_instance
|
||||
phase_oscillator_ensemble ensemble( N , 1.0 );
|
||||
//]
|
||||
|
||||
|
||||
|
||||
boost::timer timer;
|
||||
boost::timer timer_local;
|
||||
double dopri5_time = 0.0 , rk4_time = 0.0;
|
||||
{
|
||||
//[thrust_phase_ensemble_define_dopri5
|
||||
typedef runge_kutta_dopri5< state_type , value_type , state_type , value_type > stepper_type;
|
||||
//]
|
||||
|
||||
ofstream fout( "phase_ensemble_dopri5.dat" );
|
||||
timer.restart();
|
||||
for( value_type epsilon = epsilon_min ; epsilon < epsilon_max ; epsilon += d_epsilon )
|
||||
{
|
||||
ensemble.set_epsilon( epsilon );
|
||||
statistics_observer obs;
|
||||
state_type x = x_host;
|
||||
|
||||
timer_local.restart();
|
||||
|
||||
// calculate some transients steps
|
||||
//[ thrust_phase_ensemble_integration
|
||||
size_t steps1 = integrate_const( make_controlled( 1.0e-6 , 1.0e-6 , stepper_type() ) , boost::ref( ensemble ) , x , 0.0 , t_transients , dt );
|
||||
//]
|
||||
|
||||
// integrate and compute the statistics
|
||||
size_t steps2 = integrate_const( make_dense_output( 1.0e-6 , 1.0e-6 , stepper_type() ) , boost::ref( ensemble ) , x , 0.0 , t_max , dt , boost::ref( obs ) );
|
||||
|
||||
fout << epsilon << "\t" << obs.get_K_mean() << endl;
|
||||
cout << "Dopri5 : " << epsilon << "\t" << obs.get_K_mean() << "\t" << timer_local.elapsed() << "\t" << steps1 << "\t" << steps2 << endl;
|
||||
}
|
||||
dopri5_time = timer.elapsed();
|
||||
}
|
||||
|
||||
|
||||
|
||||
{
|
||||
//[ thrust_phase_ensemble_define_rk4
|
||||
typedef runge_kutta4< state_type , value_type , state_type , value_type > stepper_type;
|
||||
//]
|
||||
|
||||
ofstream fout( "phase_ensemble_rk4.dat" );
|
||||
timer.restart();
|
||||
for( value_type epsilon = epsilon_min ; epsilon < epsilon_max ; epsilon += d_epsilon )
|
||||
{
|
||||
ensemble.set_epsilon( epsilon );
|
||||
statistics_observer obs;
|
||||
state_type x = x_host;
|
||||
|
||||
timer_local.restart();
|
||||
|
||||
// calculate some transients steps
|
||||
size_t steps1 = integrate_const( stepper_type() , boost::ref( ensemble ) , x , 0.0 , t_transients , dt );
|
||||
|
||||
// integrate and compute the statistics
|
||||
size_t steps2 = integrate_const( stepper_type() , boost::ref( ensemble ) , x , 0.0 , t_max , dt , boost::ref( obs ) );
|
||||
fout << epsilon << "\t" << obs.get_K_mean() << endl;
|
||||
cout << "RK4 : " << epsilon << "\t" << obs.get_K_mean() << "\t" << timer_local.elapsed() << "\t" << steps1 << "\t" << steps2 << endl;
|
||||
}
|
||||
rk4_time = timer.elapsed();
|
||||
}
|
||||
|
||||
cout << "Dopri 5 : " << dopri5_time << " s\n";
|
||||
cout << "RK4 : " << rk4_time << "\n";
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -0,0 +1,81 @@
|
||||
/*
|
||||
Copyright 2011-2012 Karsten Ahnert
|
||||
Copyright 2013 Mario Mulansky
|
||||
|
||||
Distributed under the Boost Software License, Version 1.0.
|
||||
(See accompanying file LICENSE_1_0.txt or
|
||||
copy at http://www.boost.org/LICENSE_1_0.txt)
|
||||
*/
|
||||
|
||||
|
||||
/*
|
||||
* Solves many relaxation equations dxdt = - a * x in parallel and for different values of a.
|
||||
* The relaxation equations are completely uncoupled.
|
||||
*/
|
||||
|
||||
#include <thrust/device_vector.h>
|
||||
|
||||
#include <boost/ref.hpp>
|
||||
|
||||
#include <boost/numeric/odeint.hpp>
|
||||
#include <boost/numeric/odeint/external/thrust/thrust.hpp>
|
||||
|
||||
|
||||
using namespace std;
|
||||
using namespace boost::numeric::odeint;
|
||||
|
||||
// change to float if your GPU does not support doubles
|
||||
typedef double value_type;
|
||||
typedef thrust::device_vector< value_type > state_type;
|
||||
typedef runge_kutta4< state_type , value_type , state_type , value_type > stepper_type;
|
||||
|
||||
struct relaxation
|
||||
{
|
||||
struct relaxation_functor
|
||||
{
|
||||
template< class T >
|
||||
__host__ __device__
|
||||
void operator()( T t ) const
|
||||
{
|
||||
// unpack the parameter we want to vary and the Lorenz variables
|
||||
value_type a = thrust::get< 1 >( t );
|
||||
value_type x = thrust::get< 0 >( t );
|
||||
thrust::get< 2 >( t ) = -a * x;
|
||||
}
|
||||
};
|
||||
|
||||
relaxation( size_t N , const state_type &a )
|
||||
: m_N( N ) , m_a( a ) { }
|
||||
|
||||
void operator()( const state_type &x , state_type &dxdt , value_type t ) const
|
||||
{
|
||||
thrust::for_each(
|
||||
thrust::make_zip_iterator( thrust::make_tuple( x.begin() , m_a.begin() , dxdt.begin() ) ) ,
|
||||
thrust::make_zip_iterator( thrust::make_tuple( x.end() , m_a.end() , dxdt.end() ) ) ,
|
||||
relaxation_functor() );
|
||||
}
|
||||
|
||||
size_t m_N;
|
||||
const state_type &m_a;
|
||||
};
|
||||
|
||||
const size_t N = 1024 * 1024;
|
||||
const value_type dt = 0.01;
|
||||
|
||||
int main( int arc , char* argv[] )
|
||||
{
|
||||
// initialize the relaxation constants a
|
||||
vector< value_type > a_host( N );
|
||||
for( size_t i=0 ; i<N ; ++i ) a_host[i] = drand48();
|
||||
state_type a = a_host;
|
||||
|
||||
// initialize the intial state x
|
||||
state_type x( N );
|
||||
thrust::fill( x.begin() , x.end() , 1.0 );
|
||||
|
||||
// integrate
|
||||
relaxation relax( N , a );
|
||||
integrate_const( stepper_type() , boost::ref( relax ) , x , 0.0 , 10.0 , dt );
|
||||
|
||||
return 0;
|
||||
}
|
||||
Reference in New Issue
Block a user