Adaptive Power Reallocation for Value-Oriented Schedulers in Power-Constrained HPC

Abstract

In the exascale era, HPC systems are expected to operate under different system-wide power-constraints. For such power-constrained systems, improving per-job flops-per-watt may not be sufficient to improve the total HPC productivity as more number of scientific applications with different compute intensities are migrating to the HPC systems. To measure HPC productivity for such applications, we utilize a monotonically decreasing time-dependent value function, called job-value, with each application. A job-value function represents the value of completing a job for an organization. We begin by exploring the trade-off between two commonly used static power allocation strategies (uniform and greedy) in a power-constrained oversubscribed system. We simulate a large-scale system and demonstrate that, at the tightest power constraint, the greedy allocation can lead to 30% higher productivity compared to the uniform allocation whereas, the uniform allocation can gain up to 6% higher productivity at the relaxed power constraint. We then propose a new dynamic power allocation strategy that utilizes power-performance models derived from offline data. We use these models for reallocating power from running jobs to newly arrived jobs to increase overall system utilization and productivity. In our simulation study, we show that compared to static allocation, the dynamic power allocation policy improves node utilization and job completion rates by 20% and 9%, respectively, at the tightest power constraint. Our dynamic approach consistently earns up to 8% higher productivity compared to the best performing static strategy under different power constraints.