Title

Methods For Parallelizing The Probabilistic Neural Network On A Beowulf Cluster Computer

Abstract

In this paper, we present three different methods for implementing the Probabilistic Neural Network on a Beowulf cluster computer. The three methods, Parallel Full Training Set (PFT-PNN), Parallel Split Training Set (PST-PNN) and the Pipelined PNN (PPNN) all present different performance tradeoffs for different applications. We present implementations for all three architectures that are fully equivalent to the serial version and analyze the tradeoffs governing their potential use in actual engineering applications. Finally we provide performance results for all three methods on a Beowulf cluster. © 2006 IEEE.

Publication Date

1-1-2006

Publication Title

IEEE International Conference on Neural Networks - Conference Proceedings

Number of Pages

2378-2385

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ijcnn.2006.247062

Socpus ID

40649114543 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/40649114543

This document is currently not available here.

Share

COinS