Tiresias: A GPU Cluster Manager for Distributed Deep Learning

Abstract

Deep learning (DL) training jobs bring some unique challenges to existing cluster managers, such as unpredictable training times, an all-or-nothing execution model, and inflexibility in GPU sharing. Our analysis of a large GPU cluster in production shows that existing big data schedulers cause long queueing delays and low overall performance. We present Tiresias, a GPU cluster manager tailored for distributed DL training jobs, which efficiently schedules and places DL jobs to reduce their job completion times (JCTs). Given that a DL job’s execution time is often unpredictable, we propose two scheduling algorithms – Discretized Two- Dimensional Gittins index relies on partial information and Discretized Two-Dimensional LAS is information-agnostic – that aim to minimize the average JCT. Additionally, we describe when the consolidated placement constraint can be relaxed, and present a placement algorithm to leverage these observations without any user input. Experiments on the Michigan ConFlux cluster with 60 P100 GPUs and large-scale trace-driven simulations show that Tiresias improves the average JCT by up to 5:5 over an Apache YARN-based resource manager used in production. More importantly, Tiresias’s performance is comparable to that of solutions assuming perfect knowledge.