Parallelization of cellular neural networks for image processing on cluster architectures
Thomas Weishäupl, Erich Schikuta · 2004
In this paper a simple but effective approach for parallelization of cellular neural networks for image processing is developed. Digital gray-scale images were used to evaluate the program. The approach uses the SPMD (single-program multiple-data) model and is based on the structural data parallel approach (Schikuta et al, 1996). The process of parallelizing the algorithm employs HPF to generate an MPI-based program and the performance behavior was analyzed on two different cluster architectures.