A Scalable Framework for Online Power Modelling of High-Performance Computing Nodes in Production

Abstract

Power and thermal design and management are critical components of high performance computing (HPC) systems, due to their cutting-edge position in terms of high power density and large total power consumption. Many HPC power manage¬ment strategies rely on the availability of accurate compact power models, capable of predicting power consumption and tracking its sensitivity to workload parameters and operating points. In this paper we describe a methodology and a framework for training power models derived with two of the best-in-class procedures directly on the online in production nodes and without requiring dedicated training instances. The compact power models are obtained using an online regression-based approach which can track non-stationary workloads and hardware variability. Our experiments on a real-life HPC system demonstrate that the models achieve very high accuracy over all operating modes. We also demonstrate the scalability of our approach and the small amount of resources needed for the online modeling, for both the training and inference phases.