A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows
Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. The current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.
Authors
- Mohamed Wahib (ORCID: https://orcid.org/0000-0002-7165-2095)
- Tal Ben‐Nun (ORCID: https://orcid.org/0000-0002-3657-6568)
- Jens Domke (ORCID: https://orcid.org/0000-0002-5343-414X)
- Anshu Dubey (ORCID: https://orcid.org/0000-0003-3299-7426)
- Erik W. Draeger (ORCID: https://orcid.org/0000-0003-4063-0253)
Institutions
- Argonne National Laboratory (US)
- Lawrence Livermore National Laboratory (US)
- RIKEN Center for Computational Science (JP)
Publication Details
- Journal
- Computing in Science & Engineering
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/mcse.2025.3543940
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- Office of Science
- Argonne National Laboratory
- Lawrence Livermore National Laboratory