Minimizing energy on homogeneous processors with shared memory

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

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

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, processors 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
Pages (from-to)160-170
JournalTheoretical Computer Science
Volume866
DOIs
Publication statusPublished - 8 Apr 2021
Externally publishedYes

Bibliographical note

A preliminary version of this paper appeared in Proceedings of FAW'2020, LNCS 12340, p 83–95

Keywords

  • scheduling
  • shared memory
  • Approximation algorithm

Fingerprint

Dive into the research topics of 'Minimizing energy on homogeneous processors with shared memory'. Together they form a unique fingerprint.
  • Minimizing Energy on Homogeneous Processors with Shared Memory

    CHAU, V., FONG, C. K. K., LIU, S., WANG, E. Y. & ZHANG, Y., 23 Sep 2020, Frontiers in Algorithmics. FAW 2020. Springer, Cham, p. 83-95 Chapter 8. (Lecture Notes in Computer Science; vol. 12340).

    Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

    1 Citation (Scopus)

Cite this