Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914171615969280 |
|---|---|
| author | Teranishi, Keita Menon, Harshitha Godoy, William F. Balaprakash, Prasanna Bau, David Ben-Nun, Tal Bhatele, Abhinav Franchetti, Franz Franusich, Michael Gamblin, Todd Georgakoudis, Giorgis Goldstein, Tom Guha, Arjun Hahn, Steven Iancu, Costin Jin, Zheming Jones, Terry Low, Tze Meng Mankad, Het Miniskar, Narasinga Rao Monil, Mohammad Alaul Haque Nichols, Daniel Parasyris, Konstantinos Pophale, Swaroop Valero-Lara, Pedro Vetter, Jeffrey S. Williams, Samuel Young, Aaron |
| author_facet | Teranishi, Keita Menon, Harshitha Godoy, William F. Balaprakash, Prasanna Bau, David Ben-Nun, Tal Bhatele, Abhinav Franchetti, Franz Franusich, Michael Gamblin, Todd Georgakoudis, Giorgis Goldstein, Tom Guha, Arjun Hahn, Steven Iancu, Costin Jin, Zheming Jones, Terry Low, Tze Meng Mankad, Het Miniskar, Narasinga Rao Monil, Mohammad Alaul Haque Nichols, Daniel Parasyris, Konstantinos Pophale, Swaroop Valero-Lara, Pedro Vetter, Jeffrey S. Williams, Samuel Young, Aaron |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08135 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions Teranishi, Keita Menon, Harshitha Godoy, William F. Balaprakash, Prasanna Bau, David Ben-Nun, Tal Bhatele, Abhinav Franchetti, Franz Franusich, Michael Gamblin, Todd Georgakoudis, Giorgis Goldstein, Tom Guha, Arjun Hahn, Steven Iancu, Costin Jin, Zheming Jones, Terry Low, Tze Meng Mankad, Het Miniskar, Narasinga Rao Monil, Mohammad Alaul Haque Nichols, Daniel Parasyris, Konstantinos Pophale, Swaroop Valero-Lara, Pedro Vetter, Jeffrey S. Williams, Samuel Young, Aaron Software Engineering Artificial Intelligence Distributed, Parallel, and Cluster Computing Performance 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. |
| title | Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions |
| topic | Software Engineering Artificial Intelligence Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2505.08135 |