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arXiv · 2505.08135

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·Tze Meng Low·Het Mankad·Narasinga Rao Miniskar·Mohammad Alaul Haque Monil·Daniel Nichols·Konstantinos Parasyris·Swaroop Pophale·Pedro Valero-Lara·Jeffrey S. Vetter·Samuel Williams·Aaron Young
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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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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, Tze Meng Low, Het Mankad, Narasinga Rao Miniskar, Mohammad Alaul Haque Monil, Daniel Nichols, Konstantinos Parasyris, Swaroop Pophale, Pedro Valero-Lara, Jeffrey S. Vetter, Samuel Williams, Aaron Young. 2025-05-13. Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions. https://doi.org/10.1007/978-3-032-07612-0_47

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