EFFICIENT NEURAL NETWORK TRAINING ON A CRAY Y-MP
Siu Leung Chung, Rudy Setiono · International Journal of High Speed Computing · 1995
An efficient implementation of a quasi-Newton algorithm for training feed-forward neural network on a Cray Y-MP is presented. The most time-consuming step of a neural network training using the quasi-Newton algorithm is the computation of the error function and its gradient. Parallelization embedded in these computations can be exploited through vectorization in a Cray Y-MP supercomputer. We show how they can be carried out such that the overall performance of the neural network training process can be enhanced substantially.