Implementation and evaluation of self-organizing map algorithm on a graphic processor

Akihiro Shitara, Yuri Nishikawa, Masato Yoshimi, Hideharu Amano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we introduce an implementation of algorithm for self-organizing maps(SOM) using GPUs and discuss its evaluation. We used CUDA provided by NVIDIA Corporation for parallel programming, profiling, and data flow optimization so as to exploit inherent data-level parallelism of the algorithm. By using three NVIDIA's graphic cards for evaluation, we investigated the relationships among the number of processor elements, amount of memory device and performance. As the result of performance evaluation with various parameter combinations, we found that implementation on GTX280 achieved 150 times higher performance of Intel Core 2 Quad 2.40 GHz when parameters of map size, dimension of vectors and learning size were 1372×1372, 128 and 128, respectively.

Original languageEnglish
Title of host publicationProceedings of the 21st IASTED International Conference on Parallel and Distributed Computing and Systems, PDCS 2009
Pages253-260
Number of pages8
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event21st IASTED International Conference on Parallel and Distributed Computing and Systems, PDCS 2009 - Cambridge, MA, United States
Duration: 2009 Nov 22009 Nov 4

Publication series

NameProceedings of the IASTED International Conference on Parallel and Distributed Computing and Systems
ISSN (Print)1027-2658

Other

Other21st IASTED International Conference on Parallel and Distributed Computing and Systems, PDCS 2009
CountryUnited States
CityCambridge, MA
Period09/11/209/11/4

Keywords

  • CUDA
  • GPGPU
  • GPU
  • Self-organizing Map

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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