Optimal task clustering using Hopfield net

Weiping Zhu, Tyng Yeu Liang, Ce Kuen Shieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

To achieve high performance in a distributed system, the tasks of a program have to be carefully clustered and assigned to processors. In this paper we present a static method to cluster tasks and allocate them to processors. The proposed method relies on the Hopfield neural network to achieve optimum or near-optimum task clustering in terms of load balancing and communication cost. Experimental studies show that this method indeed can find optimal or near-optimal mapping for those programs used in our tests.

Original languageEnglish
Title of host publication1997 3rd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 1997
EditorsWanlei Zhou, Andrzej Goscinski, Michael Hobbs
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages451-464
Number of pages14
ISBN (Electronic)0780342291, 9780780342293
DOIs
Publication statusPublished - 1997 Jan 1
Event3rd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 1997 - Melbourne, Australia
Duration: 1997 Dec 101997 Dec 12

Publication series

Name1997 3rd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 1997

Other

Other3rd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 1997
CountryAustralia
CityMelbourne
Period97-12-1097-12-12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

Fingerprint Dive into the research topics of 'Optimal task clustering using Hopfield net'. Together they form a unique fingerprint.

Cite this