Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing

TL;DR

This paper explores how large language models and GenAI can assist in code translation and development for scientific computing, introducing a tool called CodeScribe that improves legacy code modernization.

Contribution

We developed CodeScribe, a novel tool combining prompt engineering and supervision to facilitate efficient code translation and integration in scientific computing workflows.

Findings

CodeScribe effectively translates Fortran to C++

It generates APIs for legacy-modern system integration

AI-assisted translation enhances productivity in scientific computing

Abstract

The emergence of foundational models and generative artificial intelligence (GenAI) is poised to transform productivity in scientific computing, especially in code development, refactoring, and translating from one programming language to another. However, because the output of GenAI cannot be guaranteed to be correct, manual intervention remains necessary. Some of this intervention can be automated through task-specific tools, alongside additional methodologies for correctness verification and effective prompt development. We explored the application of GenAI in assisting with code translation, language interoperability, and codebase inspection within a legacy Fortran codebase used to simulate particle interactions at the Large Hadron Collider (LHC). In the process, we developed a tool, CodeScribe, which combines prompt engineering with user supervision to establish an efficient process for code conversion. In this paper, we demonstrate how CodeScribe assists in converting Fortran code to C++, generating Fortran-C APIs for integrating legacy systems with modern C++ libraries, and providing developer support for code organization and algorithm implementation. We also address the challenges of AI-driven code translation and highlight its benefits for enhancing productivity in scientific computing workflows.