How to parallelize cellular neural networks on cluster architectures

Abstract

In this paper, we present "rules of thumb" for the efficient and straight-forward parallelization of cellular neural networks (CNNs) processing image data on cluster architectures. The rules result from the application and optimization of the simple but effective structural data parallel approach, which is based on the SPMD model. Digital gray-scale images were used to evaluate the optimized parallel cellular neural network program. The process of parallelizing the algorithm employs HPF to generate an MPI-based program.