PSyclone 3.3.0

PSyclone - a source-to-source and DSL Fortran compiler for HPC applications

PSyclone is a source-to-source Fortran compiler designed to programmatically optimise, parallelise and instrument HPC applications via user-provided transformation scripts. By encapsulating the performance-portability aspects (e.g. whether to parallelise with OpenMP or OpenACC), these scripts enable a separation of concerns between the scientific implementation and the optimisation choices. This allows each aspect to be explored and developed largely independently. Additionally, PSyclone supports the development of kernel-based Fortran-embedded DSLs following the PSyKAl model developed in the GungHo project.

PSyclone is currently used to support the LFRic mixed finite-element PSyKAl DSL for the UK MetOffice's next generation modelling system and the GOcean finite-difference PSyKAl DSL for a prototype 2D ocean modelling system. It is also used to insert GPU offloading directives into existing directly-addressed MPI applications such as the NEMO ocean model.

For more detailed information see the PSyclone Documentation.

Installation

You can install the latest release of psyclone from PyPI by using:

$ pip install psyclone

or, if you want an isolated installation in a python virtual environment:

$ python -m venv <virtual_env_name>
$ source <virtual_env_name>/bin/activate
$ pip install psyclone

Alternatively, you can install the latest upstream version of psyclone by cloning this repository and using:

$ pip install .

or in developer (editable) mode using

$ pip install -e .

PSyclone is also available in the Conda and Spack package managers.

For more information about the installation process see the Getting Going. section of the User Guide.

Structure

Path	Description
bin/	Top-level driver scripts for PSyclone and the PSyclone kernel tool
changelog	Information on changes between releases
doc/	Documentation source using Sphinx
examples/	Simple examples
README.md	This file
src/psyclone	The Python source code
src/psyclone/tests/	Unit and functional tests using pytest
tutorial/practicals	Hands-on exercises using a local installation of PSyclone