Impact of memory frequency scaling on user-centric smartphone workloads

H. R. Mendis, Wei-Ming Chen, L. S. Indrusiak, Tei-Wei Kuo, Pi-Cheng Hsiu

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Citations (Scopus)

Abstract

Improving battery life in mobile phones has become a top concern with the increase in memory and computing requirements of applications with tough quality-of-service needs. Many energy-efficient mobile solutions vary the CPU and GPU voltage/frequency to save power consumption. However, energy-aware control over the memory bus connecting the various on-chip subsystems has had much less interest. This measurement-based study first analyse the CPU, GPU and memory cost (i.e. product of utilisation and frequency) of user-centric smartphone workloads. The impact of memory frequency scaling on power consumption and quality-of-service is also measured. We also present a preliminary analysis into the frequency levels selected by the different default governors of the CPU/GPU/memory components. We show that an interdependency exists between the CPU and memory governors and that it may cause unnecessary increase in power consumption, due to interference with the CPU frequency governor. The observations made in this measurement-based study can also reveal some design insights to system designers.
Original languageEnglish
Title of host publicationSAC '18
Subtitle of host publicationProceedings of the 33rd Annual ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Pages567-574
ISBN (Print)978-1-4503-5191-1
DOIs
Publication statusPublished - Apr 2018
Externally publishedYes
Event33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Pau, France
Duration: 9 Apr 201813 Apr 2018

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference33rd Annual ACM Symposium on Applied Computing, SAC 2018
Country/TerritoryFrance
CityPau
Period9/04/1813/04/18

Research Keywords

  • Memory frequency
  • Smartphone workload characterisation

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