Efficient task allocation algorithms and its use to parallelize irregular Gauss-Seidel type algorithms

G. Huang, W. Ongsakul

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

1 Citation (Scopus)

Abstract

In our earlier papers, the parallelization and implementations of Gauss-Seidel(G-S) power flow analysis have been investigated on both shared memory (SM) and distributed memory (DM) machines. The desired properties to maximize the speedup, such as the minimum communication overhead and the balancing computational load, have been described. In this paper, we investigate a two stage parallelization scheme to achieve the desired properties for the DM type machines. In the first stage, we introduce a new efficient heuristic clustering algorithm which reduces the communication time and balances the computational load. In the second stage, we devise a coloring algorithm which intends to minimize the synchronization overhead and coordinates the information exchange among processors. It is shown that the parallelization scheme effectively increases the speedups and the associated upper bound of G-S algorithm on the nCUBE2 machine.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Parallel Processing
PublisherPubl by IEEE
Pages497-501
Number of pages5
ISBN (Print)0818656026
Publication statusPublished - 1 Jan 1994
EventProceedings of the 8th International Parallel Processing Symposium - Cancun, Mex
Duration: 26 Apr 199429 Apr 1994

Publication series

NameProceedings of the International Conference on Parallel Processing
ISSN (Print)0190-3918

Other

OtherProceedings of the 8th International Parallel Processing Symposium
CityCancun, Mex
Period26/4/9429/4/94

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ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Huang, G., & Ongsakul, W. (1994). Efficient task allocation algorithms and its use to parallelize irregular Gauss-Seidel type algorithms. In Proceedings of the International Conference on Parallel Processing (pp. 497-501). (Proceedings of the International Conference on Parallel Processing). Publ by IEEE.