Hopfield neural network based task mapping method

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.