New approach for task clustering

Weiping Zhu, Tyng Yeu Liang, Ce Kuen Shieh

Research output: Contribution to conferencePaperpeer-review

Abstract

Static clustering has been used to group tasks for parallel processing. Most clustering methods used for current multithreaded DSM systems only consider the workload balance. In contrast, in this paper we present a static method to cluster closely related tasks of an application onto a multithreaded DSM system. This method relies on the Hopfield neural network to find optimal or near-optimal clusters. An optimal solution identified by this method tends to minimize load imbalance and communication overhead. We have implemented this method on Cohesion which is a multithreaded DSM system. Three programs, SOR, Nbody, and Gaussian Elimination, are used to test the effectiveness of this method. The result shows that our method indeed can find optimal or near-optimal clustering for these programs.

Original languageEnglish
Pages538-542
Number of pages5
Publication statusPublished - 1998 Jan 1
EventProceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2) - Beijing, China
Duration: 1997 Oct 281997 Oct 31

Other

OtherProceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2)
CityBeijing, China
Period97-10-2897-10-31

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

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