FGPT: A Fortran Transpiler Framework to Bridge the Expertise Gap in Scientific Computing

A new proof-of-concept compiler framework, FGPT, aims to address the growing expertise gap in Fortran, a language still dominant in high-performance computing but increasingly unfamiliar to younger scientists. The framework, presented in a paper on arXiv (2608.00130), provides a systematic pipeline to transpile Fortran code into GPU-adapted Fortran, auto-differentiable Fortran via Tapenade, or Python modules using NumPy and JAX. This addresses the problem of 'Fython'—Python code written with a Fortran mindset—which fails to leverage modern frameworks. The architecture consists of three stages: a frontend that parses Fortran and extracts target procedures with cross-module dependencies, a middle-end that lowers code into an intermediate representation, and a backend that generates the target outputs. The paper is a cross-listed announcement, indicating its relevance across multiple disciplines. This development is significant for the scientific computing community, as it could enable a new generation of researchers to work with legacy Fortran code while also allowing experienced developers to transition to modern ecosystems like JAX. The framework is Python-based, making it accessible to a broad audience. The paper does not specify a release date or availability of the tool, but it represents a step toward modernizing Fortran workflows.