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Multi-tenant machine learning services have become emerging data-intensiveworkloads in data centers with heavy usage of GPU resources. Due to the largescale, many tuning parameters and heavy resource usage, it is usuallyimpractical to evaluate and benchmark those machine learning services on realclusters. In this demonstration, we present AnalySIM, a cluster simulator thatallows efficient design explorations for multi-tenant machine learningservices. Specifically, by trace-driven cluster workload simulation, AnalySIMcan easily test and analyze various scheduling policies in a number ofperformance metrics such as GPU resource utilization. AnalySIM simulates thecluster computational resource based on both physical topology and logicalpartition. The tool has been used in SenseTime to understand the impact ofdifferent scheduling policies with the trace from a real production cluster ofover 1000 GPUs. We find that preemption and migration are able to significantlyreduce average job completion time and mitigate the resource fragmentationproblem.

A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs