DyPO: Dynamic Pareto-optimal configuration selection for heterogeneous MpSoCs

Ujjwal Gupta, Chetan Arvind Patil, Ganapati Bhat, Prabhat Mishra, Umit Ogras

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Modern multiprocessor systems-on-chip (MpSoCs) offer tremendous power and performance optimization opportunities by tuning thousands of potential voltage, frequency and core configurations. As the workload phases change at runtime, different configurations may become optimal with respect to power, performance or other metrics. Identifying the optimal configuration at runtime is infeasible due to the large number of workloads and configurations. This paper proposes a novel methodology that can find the Pareto-optimal configurations at runtime as a function of the workload. To achieve this, we perform an extensive offline characterization to find classifiers that map performance counters to optimal configurations. Then, we use these classifiers and performance counters at runtime to choose Pareto-optimal configurations. We evaluate the proposed methodology by maximizing the performance per watt for 18 single- and multi-threaded applications. Our experiments demonstrate an average increase of 93%, 81% and 6% in performance per watt compared to the interactive, ondemand and powersave governors, respectively.

Original languageEnglish (US)
Article number123
JournalACM Transactions on Embedded Computing Systems
Volume16
Issue number5s
DOIs
StatePublished - Sep 2017

Keywords

  • Basic blocks
  • Clang
  • DPM
  • DVFS
  • Energy
  • LLVM
  • Logistic regression
  • Mobile platforms
  • Multi-cores
  • PAPI
  • Pareto optimization
  • Performance per watt
  • Power

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'DyPO: Dynamic Pareto-optimal configuration selection for heterogeneous MpSoCs'. Together they form a unique fingerprint.

  • Cite this