Parallel Bayesian ARTMAP and Its OpenCL Implementation
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
7-6-2017
Abstract
The Bayesian ARTMAP neural network, introduced by Vigdor and Lerner, is an incremental learning algorithm which can efficiently process massive datasets for classification, regression, and probabilistic inference tasks. We introduce the parallelized version of the BA neural network and implement it in OpenCL. Our implementation runs on both multi-core CPUs and GPUs architectures. We test the Parallel Bayesian ARTMAP on several classification and regression benchmarks focusing on speedup and scalability. In some cases, the parallel BA runs by an order of magnitude faster than the sequential implementation. Our implementation has the potential to scale for OpenCL devices with increasing number of compute units.
Recommended Citation
Lőrentz, I., Andonie, R., & Sasu, L. M. (2017). Parallel Bayesian ARTMAP and Its OpenCL Implementation. Neural Processing Letters, 47, 491–507. https://doi.org/10.1007/s11063-017-9663-x
Journal
Neural Processing Letters
Rights
Copyright © 2017, Springer Science Business Media, LLC
Comments
This article was originally published in Neural Processing Letters. The full-text article from the publisher can be found here.
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