Towards OS Heterogeneity Aware Cluster Management for HPC

Abstract

To achieve extremely high performance in HPC, many researchers have proposed customized operating systems that are tailored to HPC workload characteristics and emerging hardware. Hence, we argue that the HPC cluster will move away from the single OS environment to a cluster with numerous heterogeneous OSes. However, existing HPC cluster management still assumes that all nodes are equipped with the same OS and fails to consider OS heterogeneity during job scheduling. As a result, such unawareness loses most performance benefits provided by specialized OSes. This paper quantitatively investigates the problem of ignoring OS heterogeneity in the current HPC cluster management and analyzes performance trade-offs inside heterogeneous OSes. Preliminary results on a variety of HPC OSes and applications confirm the performance penalty of the existing cluster scheduler. We then propose a cluster scheduler prototype that incorporates OS heterogeneity into cluster configuration, resource monitoring, and job placement. We also present open challenges for future research on OS heterogeneity aware HPC clusters.