Minimizing Energy on Homogeneous Processors with Shared Memory

Vincent CHAU, Chi Kit Ken FONG, Shengxin LIU, Elaine Yinling WANG, Yong ZHANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

Abstract

Energy efficiency is a crucial desideratum in the design of computer systems, from small-sized mobile devices with limited battery to large scale data centers. In such computing systems, processor and memory are considered as two major power consumers among all the system components. One recent trend to reduce power consumption is using shared memory in multi-core systems, such architecture has become ubiquitous nowadays. However, implementing the energy-efficient methods to the multi-core processor and the shared memory separately is not trivial. In this work, we consider the energy-efficient task scheduling problem, which coordinates the power consumption of both the multi-core processor and the shared memory, especially focus on the general situation in which the number of tasks is more than the number of cores. We devise an approximation algorithm with guaranteed performance in the multiple cores system. We tackle the problem by first presenting an optimal algorithm when the assignment of tasks to cores is given. Then we propose an approximation assignment for the general task scheduling.
Original languageEnglish
Title of host publicationFrontiers in Algorithmics : 14th International Workshop, FAW 2020, Proceedings
EditorsMinming LI
PublisherSpringer, Cham
Chapter8
Pages83-95
Number of pages13
ISBN (Electronic)9783030599010
ISBN (Print)9783030599003
DOIs
Publication statusPublished - 23 Sept 2020
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12340
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Funding

This work is supported by the CAS President’s International Fellowship Initiative No 2020FYT0002, 2018PT0004.

Keywords

  • Approximation algorithm
  • Energy
  • Scheduling
  • Shared memory

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