Pyccel stands for Python extension language using accelerators.

The aim of Pyccel is to provide a simple way to generate automatically, parallel low level code. The main uses would be:

1. Convert a Python code (or project) into a Fortran or C code.
2. Accelerate Python functions by converting them to Fortran or C functions.

Pyccel can be viewed as:

• Python-to-Fortran/C converter
• a compiler for a Domain Specific Language with Python syntax

Pyccel comes with a selection of extensions allowing you to convert calls to some specific Python packages to Fortran/C. The following packages will be (partially) covered:

• numpy
• scipy

Pyccel's acceleration capabilities lead to much faster code. Comparisons of Python vs Pyccel or other tools can be found in the benchmarks repository. The results for the devel branch currently show the following performance on Python 3.10:

If you are eager to try Pyccel out, we recommend reading our quick-start guide.

If Pyccel has been significant in your research, and you would like to acknowledge the project in your academic publication, we would ask that you cite the following paper:

Bourne, Güçlü, Hadjout and Ratnani (2023). Pyccel: a Python-to-X transpiler for scientific high-performance computing. Journal of Open Source Software, 8(83), 4991, https://doi.org/10.21105/joss.04991

The associated bibtex can be found here.

Pyccel has a few system requirements to ensure that the system where it is installed is capable of compiling Fortran code. These requirements are detailed in the documentation. Once all requirements are satisfied, the simplest way to install Pyccel is using PyPI. Simply run:

python3 -m pip install --user pyccel

Alternative installation methods such as installing from source, or installing with a docker are described in the documentation.

We welcome any and all contributions.

There are many ways to help with the Pyccel project which are more or less involved. A summary can be found in the documentation.

We can also be contacted via the Pyccel Discord Server.

• Quick-start Guide

• Installation

• Contributing

• C/Fortran Compilers

• Decorators

• Header Files

• Templates

• N-dimensional Arrays

• Function-pointers as arguments

• Const keyword

• Supported libraries/APIs

  • OpenMP
  • NumPy

• Overview
• How to solve an issue
• Review Process
• Development Conventions
• Tips and Tricks
• Scope
• Type Inference
• Array Ordering