A Hopfield neural network based task mapping method

Authors:
W. Zhu;T. -Y. Liang;C. -K. Shieh
Affiliations:
Department of Computer Science and Electrical Engineering, The University of Queensland, Queensland 4072, Australia;Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan
Venue:
Computer Communications
Year:
1999

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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.