Migrating Legacy Fortran to Python While Retaining Fortran-Level Performance through Transpilation and Type Hints

Abstract

We propose a method of accelerating Python code by just-in-time compilation leveraging type hints mechanism introduced in Python 3.5. In our approach performance-critical kernels are expected to be written as if Python was a strictly typed language, however without the need to extend Python syntax. This approach can be applied to any Python application, however we focus on a special case when legacy Fortran applications are automatically translated into Python for easier maintenance. We developed a framework implementing two-way transpilation and achieved performance equivalent to that of Python manually translated to Fortran, and better than using other currently available JIT alternatives (up to 5x times faster than Numba in some experiments).