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© Oxford University Press

A parallel neural network simulator on the connection machine CM-5

M. Reczko , A. Hatzigeorigiou , N. Mache 1, A. Zell 1 and S. Suhai

Molecular Biophysics Department, German Cancer Research Centre ImNeuenheimer Feld 280, D-69120 Heidelberg, Germany
1Institute for Parallel and Distrib. High Performance Systems (IPVR), University of Stuttgart Breitwiesenstr, 20–22, D-70565 Stuttgart, Germany

We here present a parallel implementation of art neural networks on the connection machine CM-5 and compare it with other parallel implementations on SIMD and MIMD architectures. This parallel implementation was developed with the goal of efficiently training large neural networks with huge training pattern sets for applications in molecular biology, in particular the prediction of coding regions in DNA sequences. The implementation uses training pattern parallelism and makes use of the parallel I/O facilities of the CM-5 and its efficient reduction operations available within the control network to achieve a high scalability. The parallel simulator obtains a maximum speed of 149.25 MCUPS for training feed-forward networks with backpropagation on a 512 processor CM-5 system without using the CM-5 vector facility. The implementation poses no restriction on the type of network topology and works with different batch training algorithms like BP, Quickprop and Rprop.

Received on November 22, 1994; revised on March 6, 1995; accepted on March 7, 1995

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