Extending SLURM for Dynamic Resource-Aware Adaptive Batch Scheduling

Mohak Chadha, Jophin John, Michael Gerndt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

With the growing constraints on power budget and increasing hardware failure rates, the operation of future exascale systems faces several challenges. Towards this, resource awareness and adaptivity by enabling malleable jobs has been actively researched in the HPC community. Malleable jobs can change their computing resources at runtime and can significantly improve HPC system performance. However, due to the rigid nature of popular parallel programming paradigms such as MPI and lack of support for dynamic resource management in batch systems, malleable jobs have been largely unrealized. In this paper, we extend the SLURM batch system to support the execution and batch scheduling of malleable jobs. The malleable applications are written using a new adaptive parallel paradigm called Invasive MPI which extends the MPI standard to support resource-adaptivity at runtime. We propose two malleable job scheduling strategies to support performance-aware and power-aware dynamic reconfiguration decisions at runtime. We implement the strategies in SLURM and evaluate them on a production HPC system. Results for our performance-aware scheduling strategy show improvements in makespan, average system utilization, average response, and waiting times as compared to other scheduling strategies. Moreover, we demonstrate dynamic power corridor management using our power-aware strategy.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics, HiPC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages223-232
Number of pages10
ISBN (Electronic)9780738110356
DOIs
StatePublished - Dec 2020
Event27th IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2020 - Virtual, Pune, India
Duration: 16 Dec 202018 Dec 2020

Publication series

NameProceedings - 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics, HiPC 2020

Conference

Conference27th IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2020
Country/TerritoryIndia
CityVirtual, Pune
Period16/12/2018/12/20

Keywords

  • Dynamic resource-management
  • SLURM
  • malleability
  • performance-aware
  • power-aware scheduling

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