Power variation aware Configuration Adviser for scalable HPC schedulers

Abstract

Efficient scheduling is crucial for time and cost-effective utilization of compute resources especially for high end systems. A variety of factors need to be considered during the scheduling decisions. Power variation across the compute resources of homogeneous large-scale systems has not been considered so far. This paper discusses the impact of the power variation for parallel application scheduling. It addresses the problem of finding the optimal resource configuration for a given application that will minimize the amount of consumed energy, under pre-defined constraints on application execution time and instantaneous average power consumption. This paper presents an efficient algorithm to do so, which also considers the existing power diversity among the compute nodes (modified also at different operating CPU frequencies) of a given homogeneous High Performance Computing system. Based on this algorithm, the paper presents a plug-in, referred to as Configuration Adviser, which operates on top of a given resource management and scheduling system to advise on energy-wise optimal resource configuration for a given application, execution using which, will adhere to the specified execution time and power consumption constraints. The main goal of this plug-in is to enhance the current resource management and scheduling tools for the support of power capping for future Exascale systems, where a data center might not be able to provide cooling or electrical power for system peak consumption but only for the expected power bands.