Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
K. Teranishi
Harshitha Menon
William F. Godoy
Prasanna Balaprakash
David Bau
Tal Ben-Nun
Abhinav Bathele
F. Franchetti
M. Franusich
T. Gamblin
Giorgis Georgakoudis
Tom Goldstein
Arjun Guha
Steven E. Hahn
Costin Iancu
Zheming Jin
Terry Jones
Tze Meng Low
Het Mankad
Narasinga Rao Miniskar
Mohammad Alaul Haque Monil
Daniel Nichols
K. Parasyris
Swaroop Pophale
Pedro Valero-Lara
Jeffrey S. Vetter
Samuel Williams
Aaron R. Young

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.
View on arXiv@article{teranishi2025_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 Bathele 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 } }
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