Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • harvard-cite-them-right
  • apa
  • 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
Accelerating Text Mining Workloads in a MapReduce-based Distributed GPU Environment
University of Borås, Swedish School of Library and Information Science.
University of Borås, Swedish School of Library and Information Science.
2013 (English)In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 73, no 2, p. 198-206Article in journal (Refereed) Published
Abstract [en]

Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data intensive, and the ease of deployment of algorithms is an important factor in developing advanced applications, we introduce a flexible, distributed, MapReduce-based text mining workflow that performs I/O-bound operations on CPUs with industry-standard tools and then runs compute-bound operations on GPUs which 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 M2050s attached to each, and we achieve considerable speedups for random projection and self-organizing maps.

Place, publisher, year, edition, pages
Elsevier Inc , 2013. Vol. 73, no 2, p. 198-206
Keywords [en]
GPU computing, MapReduce, Text mining, Self-organizing maps, Random projection, Library and Information Science
National Category
Computer and Information Sciences
Research subject
Library and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-1419DOI: 10.1016/j.jpdc.2012.10.001ISI: 000314139800008Local ID: 2320/11734OAI: oai:DiVA.org:hb-1419DiVA, id: diva2:869474
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-01-10

Open Access in DiVA

fulltext(387 kB)2412 downloads
File information
File name FULLTEXT01.pdfFile size 387 kBChecksum SHA-512
b03f117b6283b601ea5814b1137fc61e3d49a2be50142ae455b331965cbd27ea6d8d90a3d20a63f29572db7f5c5c50bba0b738baf69eb715b1ddbf7bc2c82450
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Wittek, PeterDarányi, Sándor

Search in DiVA

By author/editor
Wittek, PeterDarányi, Sándor
By organisation
Swedish School of Library and Information Science
In the same journal
Journal of Parallel and Distributed Computing
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 2412 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 893 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • harvard-cite-them-right
  • apa
  • 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