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AI agents automate modernization of legacy Fortran codebases

Researchers have developed an agentic workflow to modernize legacy High-Performance Computing (HPC) codebases, specifically targeting Fortran. This approach uses specialized AI agents to automate the conversion of large codebases, with humans overseeing key decision points. In a case study, this workflow successfully converted 56,448 lines of Fortran 77 code within the GAMESS quantum chemistry package to Fortran 2008, achieving bit-for-bit reproduction of canonical energies and passing all validation tests. AI

IMPACT Demonstrates a scalable approach for AI-driven code modernization, potentially accelerating the adoption of advanced computational methods.

RANK_REASON The cluster contains an academic paper detailing a novel research methodology for code modernization using AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents automate modernization of legacy Fortran codebases

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yuzhong Shen, Masha Sosonkina, Peng Xu, Mark S. Gordon ·

    An Agentic Workflow for Legacy HPC Modernization: Converting the Two-Electron-Integral Core of GAMESS

    arXiv:2608.12249v1 Announce Type: new Abstract: Modernizing legacy Fortran is a problem of volume: the transformations are individually routine, but the codebases can be enormous, and across much of computational science the work simply goes undone. We propose an agentic workflow…