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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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Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions

  • 会议论文

  • 发表日期：

    2026年

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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 E. 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

摘要

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.