Work

Python-Assisted Development of High-Performance Fortran Codes: A Hybrid Methodology Integrating Symbolic Mathematics and Large Language Models

Year: 2026

Type: article

Abstract: The development of high-performance Fortran code for large-scale scientific simulations is inherently challenging: direct Fortran implementation demands substantial expertise in numerical methods, optimization and system architecture. Manual derivation of numerical schemes is error-prone and time-consuming. This paper advocates a four-stage development methodology involving Python prototyping and symbolic derivation. Systematic validation at each step of incremental transition from symbolic specification to Fortran code produces numerically correct maintainable code faster than by direct manual implementation without sacrificing the resultant performance or code quality. Large Language Models effectively accelerate Python prototyping and boilerplate generation but require rigorous verification of the generated Fortran code. We suggest practical implementation guidelines including validation strategies. Python prototyping and symbolic code generation provide effective instruments for developing efficient production-ready Fortran implementations.

Source: Computation

DOI: 10.3390/computation14040086

Authors: Daniil Tolmachev, Roman Chertovskih

Institutions: Russian Academy of Sciences, Institute of Earthquake Prediction Theory and Mathematical Geophysics, Universidade do Porto, Centro de Investigação em Sistemas e Tecnologias

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funder: Fundação para a Ciência e a Tecnologia