Performance of a backpropagation trained feedforward network on an MIMD architecture
Hazem M. Abbas, Mohamed M. Bayoumi · Concurrency Practice and Experience · 1998
Training of feedforward networks on sequential machines is a computationally expensive process. This has motivated the implementation of parallel versions of the backpropagation training algorithm on different parallel platforms in order to decrease the processing time required for training. In this paper, we are investigating the implementation of backpropagation on the Alex AVX-2 coarse-grained MIMD machine. A master–slave parallel implementation is carried out for the encoder–decoder benchmark problem. A communication model for the broadcasting/gathering is used to study the effect of using different topologies. Then the performance of the backpropagation algorithms is analyzed for different network sizes and numbers of processors when the nodes are arranged as a pipeline array and in a mesh topology. © 1998 John Wiley & Sons, Ltd.