Parallel batch training of the self-organizing map using openCL

Authors:
Masahiro Takatsuka;Michael Bui
Affiliations:
ViSLAB, School of Information Technologies, The University of Sydney, NSW, Australia;ViSLAB, School of Information Technologies, The University of Sydney, NSW, Australia
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

The Self-Organizing Maps (SOMs) are popular artificial neural networks that are often used for data analyses through clustering and visualisation. SOM's mathematical model is inherently parallel. However, many implementations have not successfully exploited its parallelism because previous attempts often required cluster-like infrastructures. This article presents the parallel implementation of SOMs, particularly the batch map variant using Graphics Processing Units (GPUs) through the use of Open Computing Language (OpenCL).