Heterogeneity-Aware Resource Allocation in HPC Systems

Abstract

In their march towards exascale performance, HPC systems are becoming increasingly more heterogeneous in an effort to keep power consumption at bay. Exploiting accelerators such as GPUs and MICs together with traditional processors to their fullest requires heterogeneous HPC systems to employ intelligent job dispatchers that go beyond the capabilities of those that have been developed for homogeneous systems. In this paper, we propose three new heterogeneity-aware resource allocation algorithms suitable for building job dispatchers for any HPC system. We use real workload traces extracted from the Eurora HPC system to analyze the performance of our allocators when they are coupled with different schedulers. Our experimental results show that significant improvements can be obtained in job response times and system throughput over solutions developed for homogeneous systems. Our study also helps to characterize the operating conditions in which heterogeneity-aware resource allocation becomes crucial for heterogeneous HPC systems.