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Minimizing energy on homogeneous processors with shared memory

  • Vincent Chau
  • , Chi Kit Ken Fong
  • , Shengxin Liu*
  • , Elaine Yinling Wang
  • , Yong Zhang
  • *Corresponding author for this work
  • Shenzhen Institute of Advanced Technology
  • Hong Kong Chu Hai College
  • Nanyang Technological University
  • Dalian University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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, 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 Science and Business Media Deutschland GmbH
Pages83-95
Number of pages13
ISBN (Print)9783030599003
DOIs
StatePublished - 2020
Externally publishedYes
Event14th International Workshop on Frontiers in Algorithmics, FAW 2020 - Haikou, China
Duration: 19 Oct 202021 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12340 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Workshop on Frontiers in Algorithmics, FAW 2020
Country/TerritoryChina
CityHaikou
Period19/10/2021/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Approximation algorithm
  • Energy
  • Scheduling
  • Shared memory

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