HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling

Conference · Sat Nov 01 04:00:00 EDT 2025

DOI:https://doi.org/10.1145/3731599.376755· OSTI ID:3004292

Maiterth, Matthias ^[1]; Brewer, Wes ^[1]; Kuruvella, Jaya S. ^[2]; Dey, Arunavo ^[2]; Islam, Tanzima Z. ^[2]; Kabir, Rashadul ^[3]; Menear, Kevin ^[4]; Duplyakin, Dmitry ^[4]; Patki, Tapasya ^[5]; Jones, Terry ^[1]; Wang, Feiyi ^[1]

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1. ORNL
2. Texas State University
3. Colorado State University, Fort Collins
4. National Renewable Energy Laboratory (NREL)
5. Lawrence Livermore National Laboratory (LLNL)

Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate sched- ulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understand the impact of parameter configurations and scheduling decisions on the physical assets, even before deployment, or regarching changes not easily realizable in production. We (1) provide the first digital twin framework extended with scheduling capabilities, (2) integrate various top-tier HPC systems given their publicly available datasets, (3) implement extensions to integrate external scheduling simulators. Finally, we show how to (4) implement and evaluate incentive structures, as- well-as (5) evaluate machine learning based scheduling, in such novel digital-twin based meta-framework to prototype scheduling. Our work enables what-if scenarios of HPC systems to evaluate sustainability, and the impact on the simulated system.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 3004292
• Country of Publication: United States
• Language: English