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A GPU-Accelerated Algorithm for Self-Organizing Maps in a Distributed Environment.
University of Borås, Swedish School of Library and Information Science.
University of Borås, Swedish School of Library and Information Science.
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we introduce a MapReduce-based implementation of self-organizing maps that performs compute-bound operations on distributed GPUs. The kernels are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050 attached to each, and we achieve a 10x speedup for self-organizing maps over a distributed CPU algorithm.

Place, publisher, year, edition, pages
2012.
Keyword [en]
self-organizing maps, gpu computing, mapreduce, graphics hardware, Computer Science
National Category
Computational Mathematics
Research subject
Library and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-6782Local ID: 2320/10966OAI: oai:DiVA.org:hb-6782DiVA: diva2:887484
Conference
20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium.
Note

Sponsorship:

Amazon Web Services

Available from: 2015-12-22 Created: 2015-12-22

Open Access in DiVA

fulltext(224 kB)162 downloads
File information
File name FULLTEXT01.pdfFile size 224 kBChecksum SHA-512
30ec12215b02054df4f628378bed8944efdc3e584e3006f275dd1ffdfff65ae2dcc347363a78dd27ea019d877bc13434e33888748dad3a848a67ac77701d04ac
Type fulltextMimetype application/pdf

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Wittek, PeterDarányi, Sándor
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf