Parallel MPI implementation of training algorithms for medium-size feedforward neural networks

Author

Fedorova, N.N. ; Terekhoff, S.A.

Author_Institution

Fed. Nucl. Center, VNIITF

Volume

4

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

2378

Abstract

Artificial neural networks provide a feasible approach to model complex engineering systems. Computational parallelism is assumed as a basis of the neural architectures. In the Russian Federal Nuclear Center VNIFTF there exists a neural simulator Nimfa. In the framework of this project, parallel versions of training algorithms for feedforward neural networks based on the MPI standard are developed. In this paper we present our experience with this implementations on shared-memory multiprocessors and on Linux PC clusters

Keywords

feedforward neural nets; learning (artificial intelligence); neural net architecture; parallel processing; MPI standard; Russian Federal Nuclear Center VNIFTF; feedforward neural networks; learning algorithms; neural architectures; parallel processing; shared-memory multiprocessors; Artificial neural networks; Clustering algorithms; Computational modeling; Computer architecture; Concurrent computing; Feedforward neural networks; Neural networks; Parallel processing; Standards development; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.833438

Filename

833438