Wolter, Katinka; Freie Universität Berlin, Berlin, GERMANY
Quelle/Jahr
Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS). Annals of Computer Science and Information Systems: 43
(2025), 175
- 183
20th Conference on Computer Science and Intelligence Systems (FedCSIS), Kraków, 14-17 September, 2025, Poland
Zusammenfassung
HPC systems are shared between many users. Managing their resources and scheduling compute jobs is a central task of these clusters. Scheduling also allows to control the workload and energy consumption of an HPC system. A Digital Twin of an HPC cluster can aid in the scheduling process by providing energy measurements about the system and predict scheduling decisions with a simulation. For a real-world use case, an integration of the Digital Twin with the scheduler is necessary.
A possible use case are energy limitations as part of a demand response process between the HPC operator and energy supplier.
Therefore, this paper introduces a plugin for Slurm, an opensource scheduler, that implements a scheduling algorithm for
time-dependent cluster-wide power capping. It uses a node energy model to predict the energy consumption of jobs and can start
jobs at different frequencies to stay below the configured power limit. The plugin interfaces with the Digital Twin that provides
energy measurements for the compute nodes to track the system power consumption in real time and update the power limitations
if necessary.
The plugin is tested on a cluster and compared against a scheduling simulation of the algorithm. The analysis compares the power profile of the simulation and the real system and the allocation of the jobs over time. Differences in the execution and the power trace are analysed and discussed.
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Rechteinformation
CC BY 4.0 ; Creative Commons Attribution 4.0 License
Themenbereich der Metrologie
Mathematik und metrologische Informationstechnik
Innovationscluster
Digitalisierung
; Energie
Geschäftsfelder
Metrologie für die Wirtschaft
; Metrologie für die Gesellschaft
Zitierung
Kammeyer, A., Burger, F., Lübbert, D., & Wolter, K. (2025). Slurm plugin for HCP operation with time-dependent cluster-wide power capping. Annals of Computer Science and Information Systems, 43, 175–183. https://doi.org/10.15439/2025F0376
