How to parallelize cellular neural networks on cluster architectures

Author

Weishäupl, Thomas ; Schikuta, Erich

Author_Institution

Dept. of Comput. Sci. & Bus. Informatics, Vienna Univ., Austria

fYear

2004

fDate

10-12 May 2004

Firstpage

439

Lastpage

444

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.

Keywords

cellular neural nets; image processing; message passing; neural net architecture; parallel algorithms; parallel architectures; HPF; MPI-based program; cellular neural network parallelization; cluster architectures; digital gray-scale images; image data processing; parallel algorithm; structural data parallel approach; Cellular neural networks; Clustering algorithms; Computational modeling; Computer architecture; Computer science; Image edge detection; Neural networks; Parallel processing; Parallel programming; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel Architectures, Algorithms and Networks, 2004. Proceedings. 7th International Symposium on

ISSN

1087-4089

Print_ISBN

0-7695-2135-5

Type

conf

DOI

10.1109/ISPAN.2004.1300519

Filename

1300519