Methods for Parallelizing the Probabilistic Neural Network on a Beowulf Cluster Computer

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.