Hyperion: An AI-powered HPC cluster for sciences and humanities research that utilizes ML for predicting job turnaround time

Jun Zhou; Nathan Elgar; Tawnee Benedetto; John Richards; Ming Hu; Greg Wilsbacher; Lawrence Miao; Paul Sagona

cs.DCSep 14, 2026

Hyperion: An AI-powered HPC cluster for sciences and humanities research that utilizes ML for predicting job turnaround time

Authors: Jun Zhou, Nathan Elgar, Tawnee Benedetto, John Richards, Ming Hu, Greg Wilsbacher, Lawrence Miao, Paul Sagona

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.

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May 5, 2026 · cs.DCJ/K move · Enter open · S save

A Workflow-Oriented Framework for Asynchronous Human-AI Collaboration in Hybrid and Compute-Intensive HPC Environments

Sergio Mendoza, Cedric Bhihe, Natalia Zamora, David Modesto+4

1Barcelona Supercomputing Center, Spain · 2NTT DATA, Spain

Human involvement is critical in training and deploying AI systems in high-stakes defence and security contexts. However, real-time interaction is impractical in HPC environments due to compute intensity and resource constraints. We present a workflow framework that enables asynchronous human-AI collaboration across hybrid infrastructures, including HPC clusters, local machines, and cloud platforms. Workflows can pause at defined checkpoints for human input without halting underlying compute jobs, preventing idle resources and enabling non-blocking supervision. The framework supports interaction with SLURM-based scheduling, containerized and native tasks, and is customized for scenarios requiring human judgment and adaptability. We demonstrate its application in model training on systems like MareNostrum 5, highlighting benefits in portability, efficiency, and oversight in operational AI workflows.

High-Performance Computing Human-Ai Collaboration Agentic Workflows Workflow Automation and Optimization Collaboration Hybrid Process-Level Supervision

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Abstract

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