[DEV] add v1.66.0

This commit is contained in:
2018-01-12 21:47:58 +01:00
parent 87059bb1af
commit a97e9ae7d4
49032 changed files with 7668950 additions and 0 deletions

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# Copyright 2012 Karsten Ahnert
# Copyright 2012 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)
import os ;
import modules ;
import path ;
project
: requirements
<define>BOOST_ALL_NO_LIB=1
<include>../../../..
<cxxflags>-std=c++11
<toolset>gcc:<cxxflags>-ffast-math
<toolset>intel:<cxxflags>"-fast -inline-forceinline"
: default-build release
;
lib libgsl : : <name>gsl ;
lib libgslcblas : : <name>gslcblas ;
lib libmkl : : <name>mkl_intel_lp64 <link>shared ;
lib libmkl_core : : <name>mkl_core <link>shared ;
lib libmkl_intel_thread : : <name>mkl_intel_thread ;
lib libiomp5 : : <name>iomp5 ;
lib libpthread : : <name>pthread ;
exe odeint_rk4_array
: odeint_rk4_array.cpp
;

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# Copyright 2011-2014 Mario Mulansky
# Copyright 2011-2014 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)
# make sure BOOST_ROOT is pointing to your boost directory
# otherwise, set it here:
# BOOST_ROOT = /path/to/boost
INCLUDES += -I../../include/ -I$(BOOST_ROOT)
GCCFLAGS = -O3 -ffast-math -DNDEBUG
# disabling -ffast-math might give slightly better performance
ICCFLAGS = -Ofast -xHost -ip -inline-forceinline -DNDEBUG
# Possible options: -fp-model source -no-fma
GFORTFLAGS = -Ofast
bin/gcc:
mkdir -p bin/gcc
bin/intel:
mkdir -p bin/intel
bin/gfort:
mkdir -p bin/gfort
bin/gcc/odeint_rk4_array: odeint_rk4_array.cpp bin/gcc
g++ ${GCCFLAGS} ${INCLUDES} -o bin/gcc/odeint_rk4_array odeint_rk4_array.cpp
bin/gcc/c_lorenz: c_lorenz.c bin/gcc
gcc -std=c99 -Ofast -mtune=corei7-avx c_lorenz.c -o bin/gcc/c_lorenz
bin/intel/odeint_rk4_array: odeint_rk4_array.cpp bin/intel
icpc ${ICCFLAGS} ${INCLUDES} -o bin/intel/odeint_rk4_array odeint_rk4_array.cpp
bin/intel/c_lorenz: c_lorenz.c bin/intel
icc -std=c99 -Ofast -xHost -ansi-alias -o bin/intel/c_lorenz c_lorenz.c
bin/gfort/fortran_lorenz: fortran_lorenz.f90 bin/gfort
gfortran ${GFORTFLAGS} fortran_lorenz.f90 -o bin/gfort/fortran_lorenz
all: bin/gcc/odeint_rk4_array bin/intel/odeint_rk4_array bin/gcc/c_lorenz bin/intel/c_lorenz bin/gfort/fortran_lorenz

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# Copyright 2014 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)
# make sure BOOST_ROOT is pointing to your boost directory
# otherwise, set it here:
# BOOST_ROOT = /path/to/boost
# you also need NT2s SIMD libary available set the include path here:
# SIMD_INCLUDE = /path/to/simd/include
INCLUDES = -I$(BOOST_ROOT) -I${SIMD_INCLUDE}
# INTEL COMPILER
# change this if you want to cross-compile
ARCH = Host
# ARCH = AVX
# ARCH = SSE4.2
CXX = icpc
CC = icpc
CXXFLAGS = -O3 -x${ARCH} -std=c++0x -fno-alias -inline-forceinline -DNDEBUG ${INCLUDES}
# -ip
# GCC COMPILER
# change this if you want to cross-compile
# ARCH = native
# # ARCH = core-avx-i
# CXX = g++
# CC = g++
# CXXFLAGS = -O3 -ffast-math -mtune=${ARCH} -march=${ARCH} -std=c++0x -DNDEBUG ${INCLUDES}

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#!/bin/bash
echo "Running on ${HOSTNAME}"
out_dir=perf_${HOSTNAME}
mkdir -p ${out_dir}
for N in 256 1024 4096 16384 65536 262144 1048576 4194304 16777216 67108864
do
steps=`expr 4 \* 67108864 / ${N}`
for exe in "roessler" "roessler_simd"
do
rm -f ${out_dir}/${exe}_N${N}.times
for i in {0..4}
do
likwid-pin -cS0:0 ./${exe} ${N} ${steps} >> ${out_dir}/${exe}_N${N}.times
done
for perf_ctr in "FLOPS_DP" "FLOPS_AVX" "L2" "L3" "MEM"
do
likwid-perfctr -CS0:0 -g ${perf_ctr} ./${exe} ${N} ${steps} > ${out_dir}/${exe}_N${N}_${perf_ctr}.perf
done
done
done

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/*
* Simulation of an ensemble of Roessler attractors
*
* Copyright 2014 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)
*
*/
#include <iostream>
#include <vector>
#include <random>
#include <boost/timer.hpp>
#include <boost/array.hpp>
#include <boost/numeric/odeint.hpp>
namespace odeint = boost::numeric::odeint;
typedef boost::timer timer_type;
typedef double fp_type;
//typedef float fp_type;
typedef boost::array<fp_type, 3> state_type;
typedef std::vector<state_type> state_vec;
//---------------------------------------------------------------------------
struct roessler_system {
const fp_type m_a, m_b, m_c;
roessler_system(const fp_type a, const fp_type b, const fp_type c)
: m_a(a), m_b(b), m_c(c)
{}
void operator()(const state_type &x, state_type &dxdt, const fp_type t) const
{
dxdt[0] = -x[1] - x[2];
dxdt[1] = x[0] + m_a * x[1];
dxdt[2] = m_b + x[2] * (x[0] - m_c);
}
};
//---------------------------------------------------------------------------
int main(int argc, char *argv[]) {
if(argc<3)
{
std::cerr << "Expected size and steps as parameter" << std::endl;
exit(1);
}
const size_t n = atoi(argv[1]);
const size_t steps = atoi(argv[2]);
//const size_t steps = 50;
const fp_type dt = 0.01;
const fp_type a = 0.2;
const fp_type b = 1.0;
const fp_type c = 9.0;
// random initial conditions on the device
std::vector<fp_type> x(n), y(n), z(n);
std::default_random_engine generator;
std::uniform_real_distribution<fp_type> distribution_xy(-8.0, 8.0);
std::uniform_real_distribution<fp_type> distribution_z(0.0, 20.0);
auto rand_xy = std::bind(distribution_xy, std::ref(generator));
auto rand_z = std::bind(distribution_z, std::ref(generator));
std::generate(x.begin(), x.end(), rand_xy);
std::generate(y.begin(), y.end(), rand_xy);
std::generate(z.begin(), z.end(), rand_z);
state_vec state(n);
for(size_t i=0; i<n; ++i)
{
state[i][0] = x[i];
state[i][1] = y[i];
state[i][2] = z[i];
}
std::cout.precision(16);
std::cout << "# n: " << n << std::endl;
std::cout << x[0] << std::endl;
// Stepper type - use never_resizer for slight performance improvement
odeint::runge_kutta4_classic<state_type, fp_type, state_type, fp_type,
odeint::array_algebra,
odeint::default_operations,
odeint::never_resizer> stepper;
roessler_system sys(a, b, c);
timer_type timer;
fp_type t = 0.0;
for (int step = 0; step < steps; step++)
{
for(size_t i=0; i<n; ++i)
{
stepper.do_step(sys, state[i], t, dt);
}
t += dt;
}
std::cout << "Integration finished, runtime for " << steps << " steps: ";
std::cout << timer.elapsed() << " s" << std::endl;
// compute some accumulation to make sure all results have been computed
fp_type s = 0.0;
for(size_t i = 0; i < n; ++i)
{
s += state[i][0];
}
std::cout << state[0][0] << std::endl;
std::cout << s/n << std::endl;
}

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/*
* Simulation of an ensemble of Roessler attractors using NT2 SIMD library
* This requires the SIMD library headers.
*
* Copyright 2014 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)
*
*/
#include <iostream>
#include <vector>
#include <random>
#include <boost/timer.hpp>
#include <boost/array.hpp>
#include <boost/numeric/odeint.hpp>
#include <boost/simd/sdk/simd/pack.hpp>
#include <boost/simd/sdk/simd/io.hpp>
#include <boost/simd/memory/allocator.hpp>
#include <boost/simd/include/functions/splat.hpp>
#include <boost/simd/include/functions/plus.hpp>
#include <boost/simd/include/functions/multiplies.hpp>
namespace odeint = boost::numeric::odeint;
namespace simd = boost::simd;
typedef boost::timer timer_type;
static const size_t dim = 3; // roessler is 3D
typedef double fp_type;
//typedef float fp_type;
typedef simd::pack<fp_type> simd_pack;
typedef boost::array<simd_pack, dim> state_type;
// use the simd allocator to get properly aligned memory
typedef std::vector< state_type, simd::allocator< state_type > > state_vec;
static const size_t pack_size = simd_pack::static_size;
//---------------------------------------------------------------------------
struct roessler_system {
const fp_type m_a, m_b, m_c;
roessler_system(const fp_type a, const fp_type b, const fp_type c)
: m_a(a), m_b(b), m_c(c)
{}
void operator()(const state_type &x, state_type &dxdt, const fp_type t) const
{
dxdt[0] = -1.0*x[1] - x[2];
dxdt[1] = x[0] + m_a * x[1];
dxdt[2] = m_b + x[2] * (x[0] - m_c);
}
};
//---------------------------------------------------------------------------
int main(int argc, char *argv[]) {
if(argc<3)
{
std::cerr << "Expected size and steps as parameter" << std::endl;
exit(1);
}
const size_t n = atoi(argv[1]);
const size_t steps = atoi(argv[2]);
const fp_type dt = 0.01;
const fp_type a = 0.2;
const fp_type b = 1.0;
const fp_type c = 9.0;
// random initial conditions on the device
std::vector<fp_type> x(n), y(n), z(n);
std::default_random_engine generator;
std::uniform_real_distribution<fp_type> distribution_xy(-8.0, 8.0);
std::uniform_real_distribution<fp_type> distribution_z(0.0, 20.0);
auto rand_xy = std::bind(distribution_xy, std::ref(generator));
auto rand_z = std::bind(distribution_z, std::ref(generator));
std::generate(x.begin(), x.end(), rand_xy);
std::generate(y.begin(), y.end(), rand_xy);
std::generate(z.begin(), z.end(), rand_z);
state_vec state(n/pack_size);
for(size_t i=0; i<n/pack_size; ++i)
{
for(size_t p=0; p<pack_size; ++p)
{
state[i][0][p] = x[i*pack_size+p];
state[i][1][p] = y[i*pack_size+p];
state[i][2][p] = z[i*pack_size+p];
}
}
std::cout << "Systems: " << n << std::endl;
std::cout << "Steps: " << steps << std::endl;
std::cout << "SIMD pack size: " << pack_size << std::endl;
std::cout << state[0][0] << std::endl;
// Stepper type
odeint::runge_kutta4_classic<state_type, fp_type, state_type, fp_type,
odeint::array_algebra, odeint::default_operations,
odeint::never_resizer> stepper;
roessler_system sys(a, b, c);
timer_type timer;
fp_type t = 0.0;
for(int step = 0; step < steps; step++)
{
for(size_t i = 0; i < n/pack_size; ++i)
{
stepper.do_step(sys, state[i], t, dt);
}
t += dt;
}
std::cout.precision(16);
std::cout << "Integration finished, runtime for " << steps << " steps: ";
std::cout << timer.elapsed() << " s" << std::endl;
// compute some accumulation to make sure all results have been computed
simd_pack s_pack = 0.0;
for(size_t i = 0; i < n/pack_size; ++i)
{
s_pack += state[i][0];
}
fp_type s = 0.0;
for(size_t p=0; p<pack_size; ++p)
{
s += s_pack[p];
}
std::cout << state[0][0] << std::endl;
std::cout << s/n << std::endl;
}

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#include <stdio.h>
#include <time.h>
#include <math.h>
void lorenz(const double *x, double *restrict y) {
y[0] = 10.0 * (x[1] - x[0]);
y[1] = 28.0 * x[0] - x[1] - x[0] * x[2];
y[2] = x[0] * x[1] - (8.0 / 3.0) * x[2];
}
int main(int argc, const char *argv[])
{
const int nb_steps = 20000000;
const double h = 1.0e-10;
const double h2 = 0.5 * h;
const double nb_loops = 21;
double x[3];
double y[3];
double f1[3];
double f2[3];
double f3[3];
double f4[3];
double min_time = 1E6;
clock_t begin, end;
double time_spent;
for (int j = 0; j < nb_loops; j++) {
x[0] = 8.5;
x[1] = 3.1;
x[2] = 1.2;
begin = clock();
for (int k = 0; k < nb_steps; k++) {
lorenz(x, f1);
for (int i = 0; i < 3; i++) {
y[i] = x[i] + h2 * f1[i];
}
lorenz(y, f2);
for (int i = 0; i < 3; i++) {
y[i] = x[i] + h2 * f2[i];
}
lorenz(y, f3);
for (int i = 0; i < 3; i++) {
y[i] = x[i] + h * f3[i];
}
lorenz(y, f4);
for (int i = 0; i < 3; i++) {
x[i] = x[i] + h * (f1[i] + 2 * (f2[i] + f3[i]) + f4[i]) / 6.0;
}
}
end = clock();
min_time = fmin(min_time, (double)(end-begin)/CLOCKS_PER_SEC);
printf("Result: %f\t runtime: %f\n", x[0], (double)(end-begin)/CLOCKS_PER_SEC);
}
printf("Minimal Runtime: %f\n", min_time);
return 0;
}

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program main
implicit none
integer, parameter :: dp = 8
real(dp), dimension(1:3) :: x
integer, parameter :: nstep = 20000000
real(dp) :: t = 0.0_dp
real(dp) :: h = 1.0e-10_dp
integer, parameter :: nb_loops = 21
integer, parameter :: n = 3
integer :: k
integer :: time_begin
integer :: time_end
integer :: count_rate
real(dp) :: time
real(dp) :: min_time = 100.0
do k = 1, nb_loops
x = [ 8.5_dp, 3.1_dp, 1.2_dp ]
call system_clock(time_begin, count_rate)
call rk4sys(n, t, x, h, nstep)
call system_clock(time_end, count_rate)
time = real(time_end - time_begin, dp) / real(count_rate, dp)
min_time = min(time, min_time)
write (*,*) time, x(1)
end do
write (*,*) "Minimal Runtime:", min_time
contains
subroutine xpsys(x,f)
real(dp), dimension(1:3), intent(in) :: x
real(dp), dimension(1:3), intent(out) :: f
f(1) = 10.0_dp * ( x(2) - x(1) )
f(2) = 28.0_dp * x(1) - x(2) - x(1) * x(3)
f(3) = x(1) * x(2) - (8.0_dp / 3.0_dp) * x(3)
end subroutine xpsys
subroutine rk4sys(n, t, x, h, nstep)
integer, intent(in) :: n
real(dp), intent(in) :: t
real(dp), dimension(1:n), intent(inout) :: x
real(dp), intent(in) :: h
integer, intent(in) :: nstep
! Local variables
real(dp) :: h2
real(dp), dimension(1:n) :: y, f1, f2, f3, f4
integer :: i, k
h2 = 0.5_dp * h
do k = 1, nstep
call xpsys(x, f1)
y = x + h2 * f1
call xpsys(y, f2)
y = x + h2 * f2
call xpsys(y, f3)
y = x + h * f3
call xpsys(y, f4)
x = x + h * (f1 + 2.0_dp * (f2 + f3) + f4) / 6.0_dp
end do
end subroutine rk4sys
end program main

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/*
* lorenz.hpp
*
* Copyright 2011 Mario Mulansky
* Copyright 2012 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)
*/
#ifndef LORENZ_HPP_
#define LORENZ_HPP_
#include <boost/array.hpp>
struct lorenz
{
template< class state_type >
void inline operator()( const state_type &x , state_type &dxdt , const double t ) const
{
const double sigma = 10.0;
const double R = 28.0;
const double b = 8.0 / 3.0;
dxdt[0] = sigma * ( x[1] - x[0] );
dxdt[1] = R * x[0] - x[1] - x[0] * x[2];
dxdt[2] = x[0]*x[1] - b * x[2];
}
};
#endif /* LORENZ_HPP_ */

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/*
* odeint_rk4_array
*
* Copyright 2011 Mario Mulansky
* Copyright 2012 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 <boost/timer.hpp>
#include <boost/array.hpp>
#include <boost/numeric/odeint/stepper/runge_kutta4_classic.hpp>
#include <boost/numeric/odeint/stepper/runge_kutta4.hpp>
#include <boost/numeric/odeint/algebra/array_algebra.hpp>
#include "lorenz.hpp"
typedef boost::timer timer_type;
typedef boost::array< double , 3 > state_type;
using namespace boost::numeric::odeint;
//typedef boost::numeric::odeint::runge_kutta4_classic< state_type > rk4_odeint_type;
// use the never resizer explicitely for optimal performance with gcc,
// for the intel compiler this doesnt matter and the above definition
// gives the same performance
typedef runge_kutta4_classic< state_type , double , state_type , double ,
array_algebra, default_operations, never_resizer > rk4_odeint_type;
const int loops = 21;
const int num_of_steps = 20000000;
const double dt = 1E-10;
int main()
{
double min_time = 1E6; // something big
rk4_odeint_type stepper;
std::clog.precision(16);
std::cout.precision(16);
for( int n=0; n<loops; n++ )
{
state_type x = {{ 8.5, 3.1, 1.2 }};
double t = 0.0;
timer_type timer;
for( size_t i = 0 ; i < num_of_steps ; ++i )
{
stepper.do_step( lorenz(), x, t, dt );
t += dt;
}
min_time = std::min( timer.elapsed() , min_time );
std::clog << timer.elapsed() << '\t' << x[0] << std::endl;
}
std::cout << "Minimal Runtime: " << min_time << std::endl;
}

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"""
Copyright 2011-2014 Mario Mulansky
Copyright 2011-2014 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)
"""
import numpy as np
from matplotlib import pyplot as plt
plt.rc("font", size=16)
def get_runtime_from_file(filename):
gcc_perf_file = open(filename, 'r')
for line in gcc_perf_file:
if "Minimal Runtime:" in line:
return float(line.split(":")[-1])
t_gcc = [get_runtime_from_file("perf_workbook/odeint_rk4_array_gcc.perf"),
get_runtime_from_file("perf_ariel/odeint_rk4_array_gcc.perf"),
get_runtime_from_file("perf_lyra/odeint_rk4_array_gcc.perf")]
t_intel = [get_runtime_from_file("perf_workbook/odeint_rk4_array_intel.perf"),
get_runtime_from_file("perf_ariel/odeint_rk4_array_intel.perf"),
get_runtime_from_file("perf_lyra/odeint_rk4_array_intel.perf")]
t_gfort = [get_runtime_from_file("perf_workbook/rk4_gfort.perf"),
get_runtime_from_file("perf_ariel/rk4_gfort.perf"),
get_runtime_from_file("perf_lyra/rk4_gfort.perf")]
t_c_intel = [get_runtime_from_file("perf_workbook/rk4_c_intel.perf"),
get_runtime_from_file("perf_ariel/rk4_c_intel.perf"),
get_runtime_from_file("perf_lyra/rk4_c_intel.perf")]
print t_c_intel
ind = np.arange(3) # the x locations for the groups
width = 0.15 # the width of the bars
fig = plt.figure()
ax = fig.add_subplot(111)
rects1 = ax.bar(ind, t_gcc, width, color='b', label="odeint gcc")
rects2 = ax.bar(ind+width, t_intel, width, color='g', label="odeint intel")
rects3 = ax.bar(ind+2*width, t_c_intel, width, color='y', label="C intel")
rects4 = ax.bar(ind+3*width, t_gfort, width, color='c', label="gfort")
ax.axis([-width, 2.0+5*width, 0.0, 0.85])
ax.set_ylabel('Runtime (s)')
ax.set_title('Performance for integrating the Lorenz system')
ax.set_xticks(ind + 1.5*width)
ax.set_xticklabels(('Core i5-3210M\n3.1 GHz',
'Xeon E5-2690\n3.8 GHz',
'Opteron 8431\n 2.4 GHz'))
ax.legend(loc='upper left', prop={'size': 16})
plt.savefig("perf.pdf")
plt.savefig("perf.png", dpi=50)
plt.show()