A Study on Cross-Architectural Modelling of Power Consumption Using Neural Networks

Abstract

On the path to Exascale, the goal of High Performance Computing (HPC) to achieve maximum performance becomes the goal of achieving maximum performance under strict power constraint. Novel approaches to hardware and software co-design of modern HPC systems have to be developed to address such challenges. In this paper, we study prediction of power consumption of HPC systems using metrics obtained from hardware performance counters. We argue that this methodology is portable across different micro-architecture implementations and compare results obtained on Intel R 64, IBM R POWER TM and Cavium ThunderX R ARMv8 microarchitectures. We discuss optimal number and type of hardware performance counters required to accurately predict power consumption. We compare accuracy of power predictions provided by models based on Linear Regression (LR) and Neural Networks (NN). We find that the NN-based model provides better accuracy of predictions than the LR model. We also find, that presently it is not yet possible to predict power consumption on a given microarchitecture using data obtained on a different microarchitecture. Results of our work can be used as a starting point for developing unified, cross-architectural models for predicting power consumption.