Authors:
Omar Rivera-Morales
and
Lutz Hamel
Affiliation:
Department of Computer Science, University of Rhode Island, College Road, South Kingstown, Rhode Island, U.S.A.
Keyword(s):
SOM, VSOM, GPU, Parallel Computing, Self-organizing Map, Stochastic Training, Vector Optimization.
Abstract:
This work proposes Par-VSOM, a novel parallel version of VSOM, a very efficient implementation of stochastic training for self-organizing maps inspired by ideas from tensor algebra. The new algorithm is implemented using parallel kernels on GPU accelerators. It provides performance increases over the original VSOM algorithm, PyTorch Quicksom parallel version, Tensorflow Xpysom parallel variant, as well as Kohonen’s classic iterative implementation. Here we develop the algorithm in some detail and then demonstrate its performance on several real-world datasets. We also demonstrate that our new algorithm does not sacrifice map quality for speed using the convergence index quality assessment.