Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q

Data Science: Journal of Computing and Applied Informatics

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Field Value
 
Title Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q
 
Creator Sibero, Alexander F.K.
Sitompul, Opim Salim
Nasution, Mahyuddin K.M.
 
Description Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. Even though this algorithm is known to be an appealing clustering method,many efforts to improve its performance are still pursued in various research works. In order to gain faster computation time, for instance, running SOM in parallel had been focused in many previous research works. Utilization of the Graphics Processing Unit (GPU) as a parallel calculation engine is also continuously improved. However, total computation time in parallel SOM is still not optimal on processing large dataset. In this research, we propose a combination of Dynamic Parallel and Hyper-Q to further improve the performance of parallel SOM in terms of faster computing time. Dynamic Parallel and Hyper-Q are utilized on the process of calculating distance and searching best-matching unit (BMU), while updating weight and its neighbors are performed using Hyper-Q only. Result of this study indicates an increase in SOM parallel performance up to two times faster compared to those without using Dynamic Parallel and Hyper-Q.
 
Publisher Talenta Publisher
 
Date 2018-08-03
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://talenta.usu.ac.id/index.php/JoCAI/article/view/324
10.32734/jocai.v2.i2-324
 
Source Data Science: Journal of Computing and Applied Informatics; Vol 2 No 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI); 62-73
2580-829X
2580-6769
 
Language eng
 
Relation https://talenta.usu.ac.id/index.php/JoCAI/article/view/324/176
 
Rights Copyright (c) 2018 Data Science: Journal of Computing and Applied Informatics
 

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