Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
Keita Teranishi, Harshitha Menon, William F. Godoy, Prasanna Balaprakash, David Bau, Tal Ben-Nun, Abhinav Bhatele, Franz Franchetti, Michael Franusich, Todd Gamblin, Giorgis Georgakoudis, Tom Goldstein, Arjun Guha, Steven Hahn, Costin Iancu, Zheming Jin, Terry Jones
TL;DR
This paper explores the potential of AI, especially large language models, to transform HPC software development, addressing unique challenges and proposing future research directions in this specialized scientific domain.
Contribution
It identifies key challenges and outlines research directions for integrating AI into HPC software development, supported by two US Department of Energy projects.
Findings
AI can significantly improve HPC software development processes.
Large language models face unique challenges in HPC contexts.
Research directions include tailored AI techniques for HPC applications.
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.
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