Themis: Fair and Efficient GPU Cluster Scheduling


Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, and Aditya Akella, University of Wisconsin-Madison; Amar Phanishayee, Microsoft Research; Shuchi Chawla, University of Wisconsin-Madison


Modern distributed machine learning (ML) training workloads benefit significantly from leveraging GPUs. However, significant contention ensues when multiple such workloads are run atop a shared cluster of GPUs. A key question is how to fairly apportion GPUs across workloads. We find that established cluster scheduling disciplines are a poor fit because of ML workloads' unique attributes: ML jobs have long-running tasks that need to be gang-scheduled, and their performance is sensitive to tasks' relative placement.

We propose Themis, a new scheduling framework for ML training workloads. It's GPU allocation policy enforces that ML workloads complete in a finish-time fair manner, a new notion we introduce. To capture placement sensitivity and ensure efficiency, Themis uses a two-level scheduling architecture where ML workloads bid on available resources that are offered in an auction run by a central arbiter. Our auction design allocates GPUs to winning bids by trading off fairness for efficiency in the short term, but ensuring finish-time fairness in the long term. Our evaluation on a production trace shows that Themis can improve fairness by more than 2.25X and is ~5% to 250% more cluster efficient in comparison to state-of-the-art schedulers.

NSDI '20 Open Access Sponsored by NetApp

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@inproceedings {246328,
author = {Kshiteej Mahajan and Arjun Balasubramanian and Arjun Singhvi and Shivaram Venkataraman and Aditya Akella and Amar Phanishayee and Shuchi Chawla},
title = {Themis: Fair and Efficient {GPU} Cluster Scheduling },
booktitle = {17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20)},
year = {2020},
isbn = {978-1-939133-13-7},
address = {Santa Clara, CA},
pages = {289--304},
url = {},
publisher = {USENIX Association},
month = feb

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