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Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads

Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla
University of Wisconsin – Madison
arXiv:1907.01484 [cs.DC], (2 Jul 2019)

@misc{mahajan2019themis,

   title={Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads},

   author={Mahajan, Kshiteej and Singhvi, Arjun and Balasubramanian, Arjun and Batra, Varun and Chavali, Surya Teja and Venkataraman, Shivaram and Akella, Aditya and Phanishayee, Amar and Chawla, Shuchi},

   year={2019},

   eprint={1907.01484},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

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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 while ensuring overall cluster efficiency. We find that established cluster scheduling disciplines that provide instantaneous fair share of resources are a poor fit because of ML workloads’ unique attributes. ML jobs are typically long running, have coarse grained tasks that need to be gang-scheduled, and their performance is sensitive to tasks’ relative placement. These properties cannot be captured by existing fair sharing schemes. 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 efficiency for fairness in the short term but compensating for finish-time fairness in the long term. Our evaluation on a number of machine learning models shows that Themis can ensure greater fairness while providing more efficient allocations compared to state-of-the-art schedulers.
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