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AI agents autonomously modernize legacy Fortran code for HPC

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an autonomous AI workflow capable of modernizing legacy Fortran codebases for high-performance computing (HPC) environments. This agentic system uses specialized large language models (LLMs) to translate, validate, compile, run, test, debug, and optimize Fortran kernels into portable Kokkos C++ programs. The workflow successfully modernized benchmark kernels, producing optimized Kokkos codes that outperformed their Fortran baselines, with paid models like GPT-5 proving more effective than open-source alternatives such as Llama4-Maverick. AI

IMPACT Demonstrates LLM-driven agentic systems can autonomously modernize legacy scientific applications for improved performance and portability on diverse supercomputers.

RANK_REASON The cluster describes a research paper detailing a novel AI workflow for code modernization. [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 autonomously modernize legacy Fortran code for HPC

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The cluster describes a research paper detailing a novel AI workflow for code modernization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sparsh Gupta, Kamalavasan Kamalakkannan, Maxim Moraru, Galen Shipman, Patrick Diehl ·

    From Legacy Fortran to Portable Kokkos: An Autonomous Agentic AI Workflow

    arXiv:2509.12443v4 Announce Type: replace-cross Abstract: Scientific applications continue to rely on legacy Fortran codebases originally developed for homogeneous, CPU-based systems. As High-Performance Computing (HPC) shifts toward heterogeneous GPU-accelerated architectures, m…

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