Optimal task clustering using Hopfield net
Weiping Zhu, Tyng‐Yeu Liang, Ce-Kuen Shieh · 2002
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.