VAHRM: Variation-Aware Resource Management in Heterogeneous Supercomputing Systems

Abstract

In this article, we propose a novel resource management technique for heterogeneous supercomputing systems affected by manufacturing variability. Our proposed technique called VAHRM (Variation-Aware Heterogeneous Resource Management) takes a holistic approach to job scheduling on highly heterogeneous computing resources. VAHRM preferentially allocates energy-efficient computing resources to an energy-consuming job in a job queue, considering the impact on both the job turnaround time and the power consumption of individual resources. Furthermore, we have developed a novel approach to modeling the power consumption of computing resources that have manufacturing variability. Our approach called TSMVA (Two-Stage Modeling with Variation Awareness) enables us to generate the first variation-aware GPU power models, which can correctly estimate the power consumption of each GPU for a given job. Our experimental results show that, compared to conventional first-come-first-serve (FCFS) and state-of-the-art variation-aware scheduling algorithms, VAHRM can achieve respective improvements in system energy efficiency of up to 5.8% and 5.4% (4.5% and 4.2% on average) while reducing the average turnaround time of 21.2% and 11.9%, respectively, for various workloads obtained from a production system.