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This research presents Hyperion, an HPC cluster at the University of South Carolina that integrates trained ML models directly into Slurm to predict job wait and wall times. By automating turnaround time estimation for diverse scientific and humanities workloads, the system aims to optimize resource scheduling without requiring researchers to manually estimate completion metrics. The study highlights a practical application of machine learning in distributed computing environments to improve cluster efficiency.
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Hyperion: An AI-powered HPC cluster for sciences and humanities research that utilizes ML for predicting job turnaround time

arXiv1 min read

arXiv:2609.11946v1 Announce Type: cross Abstract: Hyperion is an innovative high-performance computing (HPC) cluster developed for researchers in both science and humanities disciplines at the University of South Carolina (USC). Our approach involved constructing a HPC cluster designed to meet the current research needs while accommodating future expansion. Additionally, we developed and trained two machine learning (ML) models to predict turnaround time, including wait time and wall time, and seamlessly integrated them into the Slurm job submission. Finally, we showcase a variety of sample applications hosted on the Hyperion platform.

Abstract:Hyperion is an innovative high-performance computing (HPC) cluster developed for researchers in both science and humanities disciplines at the University of South Carolina (USC). Our approach involved constructing a HPC cluster designed to meet the current research needs while accommodating future expansion. Additionally, we developed and trained two machine learning (ML) models to predict turnaround time, including wait time and wall time, and seamlessly integrated them into the Slurm job submission. Finally, we showcase a variety of sample applications hosted on the Hyperion platform.

Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)

arXiv:2609.11946 [cs.DC]

(or arXiv:2609.11946v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2609.11946 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 182 words

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