Automatic Energy-Efficient Job Scheduling in HPC: A Novel SLURM Plugin Approach

Anders Aaen Springborg, Michele Albano, Samuel Xavier-de-Souza

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

This paper presents a novel approach to enable energy-efficient job scheduling in High-Performance Computing (HPC) environments through application-specific energy models. We propose an architecture that decouples scheduling heuristics to a Python plugin of the HPC scheduler SLURM. The approach leverages the principles of Service-Oriented Architecture and Clean Architecture to create a proof-of-concept system that is adaptable for production setups, providing a platform for integrating various energy-efficient scheduling models. We demonstrate the approach in a single-node HPC system with an energy saving of 11% for the High-Performance Conjugate Gradients (HPCG) benchmark, which represents modern applications’ data access patterns and computation. The proposed approach opens up possibilities for more complex setups, such as automatically scheduling jobs when energy is cheap and renewable, a practice already used in companies utilizing HPC.
Original languageEnglish
Title of host publicationProceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
Number of pages8
PublisherAssociation for Computing Machinery
Publication date12 Nov 2023
Pages1831-1838
ISBN (Electronic)979-8-4007-0785-8
DOIs
Publication statusPublished - 12 Nov 2023
EventWorkshops of The International Conference on High Performance Computing, Network, Storage, and Analysis 2023 - Denver, United States
Duration: 12 Nov 202317 Nov 2023
https://sc23.supercomputing.org/program/workshops/

Conference

ConferenceWorkshops of The International Conference on High Performance Computing, Network, Storage, and Analysis 2023
Country/TerritoryUnited States
CityDenver
Period12/11/202317/11/2023
Internet address

Keywords

  • Energy-efficient resource scheduling
  • green computing
  • high-performance computing
  • sustainable computing

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