Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
Abstract
We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular large language models, have transformed every aspect of software development. For its part, HPC software is recognized as a highly specialized scientific field of its own. We discuss the challenges associated with leveraging state-of-the-art AI technologies to develop such a unique and niche class of software and outline our research directions in the two US Department of Energy--funded projects for advancing HPC Software via AI: Ellora and Durban.
Keywords
Cite
@article{arxiv.2505.08135,
title = {Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions},
author = {Keita Teranishi and Harshitha Menon and William F. Godoy and Prasanna Balaprakash and David Bau and Tal Ben-Nun and Abhinav Bhatele and Franz Franchetti and Michael Franusich and Todd Gamblin and Giorgis Georgakoudis and Tom Goldstein and Arjun Guha and Steven Hahn and Costin Iancu and Zheming Jin and Terry Jones and Tze Meng Low and Het Mankad and Narasinga Rao Miniskar and Mohammad Alaul Haque Monil and Daniel Nichols and Konstantinos Parasyris and Swaroop Pophale and Pedro Valero-Lara and Jeffrey S. Vetter and Samuel Williams and Aaron Young},
journal= {arXiv preprint arXiv:2505.08135},
year = {2025}
}
Comments
12 pages, 1 Figure, Accepted at "The 1st International Workshop on Foundational Large Language Models Advances for HPC" LLM4HPC to be held in conjunction with ISC High Performance 2025