Problem
Research questions and friction points this paper is trying to address.
code modernization
legacy scientific codes
HPC
parallelization
software ecosystem migration
Structuring agentic AI for HPC code modernization
🤖 AI Summary
This work addresses the high cost and low efficiency associated with parallelizing and modernizing legacy scientific computing codes by proposing a structured AI agent approach. By integrating large language model agents, manual prompt engineering, and continuous integration—guided by human-provided examples, guaranteed buildability, and constrained dialogue scope—the method successfully refactored the 60,000-line single-threaded Fortran MPI code NMAP-RKPM into a C++ MPI tool with OpenMP support within two phases over several months. This effort demonstrates the feasibility and substantial effectiveness of a structured AI-assisted paradigm for large-scale high-performance computing (HPC) software modernization.
📝 Abstract
Modernization of legacy scientific codes is often necessary to keep up with the ever-evolving changes in the compute resource ecosystem. Parallelization and migration from poorly supported software ecosystems are two of the most time-consuming activities in the research software engineering field. This paper presents our experience in the successful, two-phase AI-assisted modernization of NMAP-RKPM, a roughly 60,000-line, 3D explicit solid mechanics physics engine based on the Reproducing Kernel Particle Method (RKPM). We converted this single-threaded, Fortran based MPI application into a OpenMP-parallel C++ based MPI tool in the span of a few months. While Large Language Model (LLM) based tools on their own proved inadequate, we developed a highly structured "hand-holding" agentic AI methodology, like providing manually created examples, ensuring continuous buildability and limiting session scope, that was instead highly effective. The paper provides both the AI-assisted steps that were successful and the problems that we had to overcome, alongside the reasoning behind the chosen path.
Innovation
Methods, ideas, or system contributions that make the work stand out.
agentic AI
code modernization
HPC
structured prompting
LLM-assisted refactoring
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