Parallel simulation of multilayered neural networks on distributed-memory multiprocessors

Abstract

In this paper, we present a parallel simulation of a fully connected multilayered neural network using the backpropagation learning algorithm on a distributed-memory multiprocessor system. In our system, the neurons on each layer are partitioned into p disjoint sets and each set is mapped on a processor of a p-processor system. A fully distributed backpropagation algorithm, necessary communication pattern among the processors, and their time/space complexities are investigated. The p-processor speed-up of the backpropagation algorithm over a single processor is also analyzed theoretically which can be used as a basis in determining the most cost-effective or optimal number of processors. The experimental results with a network of Transputers are also presented to demonstrate the usefulness of our system.