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diff --git a/src/boost/libs/numeric/odeint/examples/thrust/phase_oscillator_ensemble.cu b/src/boost/libs/numeric/odeint/examples/thrust/phase_oscillator_ensemble.cu
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+++ b/src/boost/libs/numeric/odeint/examples/thrust/phase_oscillator_ensemble.cu
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+/*
+ 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;
+}