Predicting System-level Power for a Hybrid Supercomputer

TL;DR

This paper introduces a two-layer power consumption model for a hybrid supercomputer that predicts system-level power usage based on workload, aiding in energy-efficient scheduling and anomaly detection.

Contribution

The paper presents a novel power prediction model for hybrid supercomputers combining CPU, GPU, and MIC technologies, enabling power-aware scheduling and system optimization.

Findings

Model accurately predicts power consumption based on workload configuration.

Application of the model improves energy efficiency in supercomputing tasks.

Model can identify potential anomalous power behavior.

Abstract

For current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of hardware and software advances. Being able to model and accurately predict the power consumption of large computational systems is necessary for software-level innovations such as proactive and power-aware scheduling, resource allocation and fault tolerance techniques. In this paper we present a 2-layer model of power consumption for a hybrid supercomputer (which held the top spot of the Green500 list on July 2013) that combines CPU, GPU and MIC technologies to achieve higher energy efficiency. Our model takes as input workload information - the number and location of resources that are used by each job at a certain time - and calculates the resulting system-level power consumption. When jobs are submitted to the system, the workload configuration can be foreseen based on the scheduler policies, and our model can then be applied to predict the ensuing system-level power consumption. Additionally, alternative workload configurations can be evaluated from a power perspective and more efficient ones can be selected. Applications of the model include not only power-aware scheduling but also prediction of anomalous behavior.