Evaluation of Hybrid Run-Time Power Models for the ARM Big.LITTLE Architecture

Krastin Nikov, Jose L. Nunez-Yanez, Matthew Horsnell

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

13 Citations (Scopus)
429 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2015 IEEE 13th International Conference on Embedded and Ubiquitous Computing (EUC 2015)
Subtitle of host publicationProceedings of a meeting held 21-23 October 2015, Porto, Portugal
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages205-210
Number of pages6
ISBN (Electronic)9781467382991
ISBN (Print)9781467383004
DOIs
Publication statusPublished - Mar 2016
Event13th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2015 - Porto, Portugal
Duration: 21 Oct 201523 Oct 2015

Conference

Conference13th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2015
Country/TerritoryPortugal
CityPorto
Period21/10/1523/10/15

Keywords

  • ARM big.LITTLE
  • Linux
  • Performance counters
  • Power modelling

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