Parallel Implementation of a Machine Learning Algorithm on GPU - Cuomo, Salvatore; De Michele, Pasquale; Di Nardo, Emanuel; Marcellino, Livia (International journal of parallel programming, 2018)
The capability for understanding data passes through the ability of producing an effective and fast classification of the information in a time frame that allows to keep and preserve the value of the information itself and its potential. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. A powerful tool is provided by self-organizing maps (SOM). The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. Because of its time complexity, often using this method is a critical challenge. In this paper we propose a parallel implementation for the SOM algorithm, using parallel processor architecture, as modern graphics processing units by CUDA. Experimental results show improvements in terms of execution time, with a promising speed up, compared to the CPU version and the widely used package SOM_PAK.