DOE OSTI · 2585304
A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows
Abstract
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. Finally, 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.
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Domke, Jens [RIKEN Center for Computational Science, Kobe (Japan)] (ORCID:000000025343414X), Wahib, Mohamed [RIKEN Center for Computational Science, Kobe (Japan)] (ORCID:0000000271652095), Dubey, Anshu [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:0000000332997426), Ben-Nun, Tal [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000236576568), Draeger, Erik W. [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000340630253). 2025-05-28. A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows. https://doi.org/10.1109/mcse.2025.3543940
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