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Sia: Heterogeneity-aware, goodput-optimized ML-cluster scheduling

Authors:

Suhas Jayaram Subramanya,

Daiyaan Arfeen,

Gregory R. GangerAuthors Info & Claims

SOSP '23: Proceedings of the 29th Symposium on Operating Systems Principles

Pages 642 - 657

https://doi.org/10.1145/3600006.3613175

Published: 23 October 2023 Publication History

Abstract

The Sia scheduler efficiently assigns heterogeneous deep learning (DL) cluster resources to elastic resource-adaptive jobs. Although some recent schedulers address one aspect or another (e.g., heterogeneity or resource-adaptivity), none addresses all and most scale poorly to large clusters and/or heavy workloads even without the full complexity of the combined scheduling problem. Sia introduces a new scheduling formulation that can scale to the search-space sizes and intentionally match jobs and their configurations to GPU types and counts, while adapting to changes in cluster load and job mix over time. Sia also introduces a low-profiling-overhead approach to bootstrapping (for each new job) throughput models used to evaluate possible resource assignments, and it is the first cluster scheduler to support elastic scaling of hybrid parallel jobs.

Extensive evaluations show that Sia outperforms state-of-the-art schedulers. For example, even on relatively small 44- to 64-GPU clusters with a mix of three GPU types, Sia reduces average job completion time (JCT) by 30--93%, 99th percentile JCT and makespan by 28--95%, and GPU hours used by 12--55% for workloads derived from 3 real-world environments. Additional experiments demonstrate that Sia scales to at least 2000-GPU clusters, provides improved fairness, and is not over-sensitive to scheduler parameter settings.

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Sia: Heterogeneity-aware, goodput-optimized ML-cluster scheduling

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cover image ACM Conferences

SOSP '23: Proceedings of the 29th Symposium on Operating Systems Principles

October 2023

802 pages

ISBN:9798400702297

DOI:10.1145/3600006

Conference Chairs:
Jason Flinn
Meta
,
Margo Seltzer
University of British Columbia
,
General Chairs:
Peter Druschel
Max Planck Institute for Software Systems (MPI-SWS)
,
Antoine Kaufmann
Max Planck Institute for Software Systems (MPI-SWS)
,
Jonathan Mace
Max Planck Institute for Software Systems (MPI-SWS) and Microsoft Research

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Published: 23 October 2023

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Overall Acceptance Rate 174 of 961 submissions, 18%

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Um TOh BKang MLee WKim GKim DKim YMuzzammil MJeon MBagchi SZhang Y(2024)MetisProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692027(563-578)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692027
Shubha SShen HIyer AGavrilovska ATerry D(2024)USHERProceedings of the 18th USENIX Conference on Operating Systems Design and Implementation10.5555/3691938.3691989(947-964)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691938.3691989
Lin CHan ZZhang CYang YYang FChen CQiu LGavrilovska ATerry D(2024)ParrotProceedings of the 18th USENIX Conference on Operating Systems Design and Implementation10.5555/3691938.3691988(929-945)Online publication date: 10-Jul-2024
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