Workload Interference Prevention with Intelligent Routing and Flexible Job Placement on Dragonfly

Yao Kang, Xin Wang, Zhiling Lan

Abstract: Dragonfly is an indispensable interconnect topology for exascale HPC systems. To link tens of thousands of compute nodes at a reasonable cost, Dragonfly shares network resources with the entire system such that network bandwidth is not exclusive to any single job. Since HPC systems are usually shared between multiple co-running workloads at the same time, network competition between co-existing workloads is inevitable. This network contention appears as workload interference, where a job’s network communication can be severely delayed by other jobs. Recent studies show that, compared with the deployed adaptive routing algorithms, an intelligent routing solution based on reinforcement learning named Q-adaptive routing can reduce workload interference. In addition to improving routing efficiency, job placement is a simple yet effective method to mitigate workload interference. In this study, we leverage the well-known parallel discrete event simulation toolkit, SST, to investigate workload interference on Dragonfly with three contributions. We first develop an automatic module that serves as the bridge between SST and HPC job scheduler for automatic simulation configuration and automated simulation launching. Next, we propose a flexible job placement strategy that can mitigate workload interference based on workload communication characteristics. Finally, we extensively examine the workload interference under various job placement and routing configurations.