PERFORMANCE EVALUATION OF BACKPROPAGATION NEURAL NETWORK IMPLEMENTATION ON SINGLE PROGRAM MULTIPLE DATA (SPMD) ARCHITECTURE

Abstract

The Single Program Multiple Data (SPMD) approach has been developed to reduce the system overheads, to entail minimal computation time and to reduce the programming complexity of parallel programs. The SPMD computational model is based on the premise that all the processes execute the same program; however, at any instant of time, these processes may be executing different instructions and may be operating on different data. This work uses the SPMD computational model to implement a backpropagation neural network and the implementation is done on a multiprocessor. We present the distributed backpropagation forward and backward execution algorithms in SPMD mode. The performance of the SPMD-implemented ANN is demonstrated by the Automatic Target Recognition (ATR) problem, which involves identification of a class of targets depending on a set of input data values. The performance of the SPMD-implemented ANN is studied by observing the speedups achieved for training times as the number of processors is increased. The saving in training times is found to be quite impressive. The ATR problem up to 3 classes of targets has been tried and the ANN is found to perform the recognition easily.

Keywords::

• SPMD
• ANN
• Multiprocessor
• back propagation

C. R. CATEGORIES:

• C.1.2
• I.5.0