Evaluation of hybrid run-time power models for the ARM big.LITTLE architecture

TL;DR

This paper evaluates hybrid run-time power models for ARM big.LITTLE architecture, demonstrating improved accuracy and applicability for energy-aware scheduling on a commercial platform.

Contribution

It proposes a new hybrid approach combining physical predictors, performance events, and CPU states for power modeling in heterogeneous systems.

Findings

The hybrid model outperforms existing models in accuracy.

The approach is effective for energy-aware workload scheduling.

Validated on Samsung Exynos 5 Octa platform.

Abstract

Heterogeneous processors, formed by binary compatible CPU cores with different microarchitectures, enable energy reductions by better matching processing capabilities and software application requirements. This new hardware platform requires novel techniques to manage power and energy to fully utilize its capabilities, particularly regarding the mapping of workloads to appropriate cores. In this paper we validate relevant published work related to power modelling for heterogeneous systems and propose a new approach for developing run-time power models that uses a hybrid set of physical predictors, performance events and CPU state information. We demonstrate the accuracy of this approach compared with the state-of-the-art and its applicability to energy aware scheduling. Our results are obtained on a commercially available platform built around the Samsung Exynos 5 Octa SoC, which features the ARM big.LITTLE heterogeneous architecture.