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PHYPSO

A set of very simple hyperbolic solvers using techniques to use efficient computing kernels called from a python main program.

Table of content

Installation

Download the source repository

Either use git:

git clone https://git.unistra.fr/m.boileau/phypso.git

or download zip file.

Using docker

To get a full environment ready for running, Docker is a solution. From the host, clone the repository:

[host] git clone https://git.unistra.fr/m.boileau/phypso.git

then run a docker container using the boileaum/phypso:env image:

[host] docker run -ti -v $(pwd):/home/euler/phypso boileaum/phypso:env

Note: to run docker from a Mac with support for matplotlib display, simply run:

[host] ./run_docker_mac.sh

Now from the container:

[container] cd phypso
[container] make

Using pip

The docker/Dockerfile-deps file provides the first required dependencies such as python3 and gfortran.

Install the additional python libraries with pip:

pip install -r requirements.txt

From the project root directory, compile the C-executable, C-library, Cython and pythran versions of all programs using:

make

General Godunov solver

Usage

  1. Go to ./godunov subdirectory.
  • Run using the python main program:
./godunov.py -h
usage: godunov.py [-h] [--problem {burgers,stvenant}] [--tmax final_time]
                  [--nmax number_of_pts] [--profile] [--plot]
                  [--kernel {python,numpy,pythran,bench}]

Solve hyperbolic problem

optional arguments:
  -h, --help            show this help message and exit
  --problem {burgers,stvenant}
                        select Problem type
  --tmax final_time     simulation final time
  --nmax number_of_pts  number of grid points
  --profile             activate profiling
  --plot                activate plotting
  --kernel {python,numpy,pythran,bench}
                        select kernel type

For example, run a benchmark to compare different kernel versions:

./godunov.py --kernel bench

Saint-Venant's equation solver

Usage

  1. Go to ./stvenant subdirectory.
  • Run using the python main program:
./stvenant.py [-h] [--noplot]

Burgers' equation solver

Basic usage

  1. Go to ./burgers subdirectory.
  • Run using the python main program:
./burgers.py -h
usage: burgers.py [-h] [--tmax final_time] [--nmax number_of_pts] [--profile]
                  [--plot] [--kernel {pythran,numba,python,numpy,fortran}]

Solve Burgers problem

optional arguments:
  -h, --help            show this help message and exit
  --tmax final_time     simulation final time
  --nmax number_of_pts  number of grid points
  --profile             activate profiling
  --plot                activate plotting
  --kernel {pythran,numba,python,numpy,fortran}
                        select kernel type

Examples:

  • Using a basic python kernel (very unefficient):
$ ./burgers.py --nmax 1000 --profile
  • Using the native numpy kernel (much faster but not optimal):
$ ./burgers.py --nmax 1000 --profile --kernel numpy

Use pythran to accelerate Python

From Pythran website:

Pythran is a Python to c++ compiler for a subset of the Python language, with a focus on scientific computing. It takes a Python module annotated with a few interface description and turns it into a native python module with the same interface, but (hopefully) faster.

Howto

Pythran simply requires to explicit the signatures of python functions as comments in python file header:

$ head  godunov.py 
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#pythran export sol_exact(float, float)
#pythran export riemann(float, float, float)
#pythran export timeloop(float, int)
#pythran export xmin, xmax
"""
Godunov solver for the Burgers equation
"""
  • Use pythran to compile the python submodule godunov.py as a pythran_godunov.so object file:
make pythran
  • Execute as if it were standard python
./burgers.py --kernel pythran

Acceleration

  • Example of execution output using the pythran module:
$ ./burgers.py --nmax 1000 --profile --kernel pythran
tmax = 1.0
nmax = 1000
kernel: pythran
Mean time [s] over 10 executions = 0.002362
L2 error = 0.018877
  • Example of execution output using the native python module:
$ ./burgers.py --nmax 1000 --profile --kernel python
tmax = 1.0
nmax = 1000
kernel: python
Mean time [s] over 10 executions = 1.071154
L2 error = 0.018877

Use f2py to accelerate python

Howto

  • Compile the fortran file godunov.f90 with f2py
make fortran
  • Run with the --kernel fortran option to use the compiled module
./burgers.py --profile --nmax 1000 --kernel fortran

Acceleration

$ ./burgers.py --profile --nmax 1000 --kernel fortran
tmax = 1.0
nmax = 1000
kernel: fortran
Mean time [s] over 10 executions = 0.004342
L2 error = 0.018877

Developers' corner

Build the docker images

cd docker
docker-compose build

Test suite

Tests are performed with pytest. To run the tests with verbose level and standard output:

pytest -vs