Parallel training of artificial neural networks using multithreaded and multicore CPUs

Authors:
Olena Schuessler;Diego Loyola
Affiliations:
German Aerospace Center, Institute of Remote Sensing, Weßling, Germany;German Aerospace Center, Institute of Remote Sensing, Weßling, Germany
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Year:
2011

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Abstract

This paper reports on methods for the parallelization of artificial neural networks algorithms using multithreaded and multicore CPUs in order to speed up the training process. The developed algorithms were implemented in two common parallel programming paradigms and their performances are assessed using four datasets with diverse amounts of patterns and with different neural network architectures. All results show a significant increase in computation speed, which is reduced nearly linear with the number of cores for problems with very large training datasets.