Parallel Implementation of a Machine Learning Algorithm on GPU

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 algori...

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Vydané v:International journal of parallel programming Ročník 46; číslo 5; s. 923 - 942
Hlavní autori: Cuomo, Salvatore, De Michele, Pasquale, Di Nardo, Emanuel, Marcellino, Livia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.10.2018
Springer Nature B.V
Predmet:
Algorithms
Classification
Computer programming
Computer Science
Graphics processing units
Machine learning
Microprocessors
Parallel processing
Processor Architectures
Self organizing maps
Software Engineering/Programming and Operating Systems
Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm
Theory of Computation
Self-organization map
Fast data analysis and retrieval
GP-GPU
Machine learning
ISSN:0885-7458, 1573-7640
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-017-0554-6