Communication Parameter Tests and Parallel Back Propagation Algorithms on iPSC/2 Hypercube Multiprocessor

Abstract

The communication complexity on Intel’s second generation iPSC/2 hypercube and its effect on parallelization of Back Propagation type training algorithms for neural networks are explored. On iPSC/2 , different broadcasting methods are tested and three inter-node communication schemes are evaluated based on their performance on vector addition. These communication schemes are then utilized on parallel versions of the Back Propagation training algorithm. The performance of the resulting parallel variants of Back Propagation are analyzed using two medium size problems: vowel classification and English text-to-speech conversion (NETtalk data).