Hopfield neural network based task mapping method

W. Zhu, T. Y. Liang, C. K. Shieh

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

With a prior knowledge of a program, static mapping aims to identify an optimal clustering strategy that can produce the best performance. In this paper we present a static method that uses Hopfield neural network to cluster the tasks of a parallel program for a given system. This method takes into account both load balancing and communication minimization. The method has been tested on a distributed shared memory system against other three clustering methods. Four programs, SOR, N-body, Gaussian Elimination and VQ, are used in the test. The result shows that our method is superior to the other three.

Original languageEnglish
Pages (from-to)1068-1079
Number of pages12
JournalComputer Communications
Volume22
Issue number11
DOIs
Publication statusPublished - 1999 Jul 15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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