Towards Bespoke Optimizations of Energy Efficiency in HPC Environments

Abstract

We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: i) platform for collecting, storing and processing data from multiple sources across hardware and software stacks, ii) collections of regression machine learning (ML) algorithms for making workloads classifications and energy usage predictions, iii) agent-based decision-making framework for delivering control decisions to middleware and infrastructure thus supporting real time or near real energy efficiency optimizations. We will present some concrete examples of using our proposed approach in HPC environment.