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Domain-specific compiler and code transformation system for Finite Difference/Volume/Element Earth-system models in Fortran

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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 .

For more information about the installation process see this 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/notebooks Tutorial using Jupyter notebooks
tutorial/practicals Hands-on exercises using a local installation of PSyclone