Parallel Bayesian ARTMAP and Its OpenCL Implementation

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.

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    The ratio between sequential and parallel execution times.

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Correspondence to István Lőrentz.

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Lőrentz, I., Andonie, R. & Sasu, L.M. Parallel Bayesian ARTMAP and Its OpenCL Implementation. Neural Process Lett 47, 491–507 (2018). https://doi.org/10.1007/s11063-017-9663-x

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Keywords

  • Bayesian learning
  • Incremental learning
  • Fuzzy ARTMAP
  • OpenCL
  • Parallel architectures
  • Massive datasets