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A CUDA Implementation of Random Forests: Early Results
Responsible organisation
2010 (English)Conference paper, (Refereed) Published
Abstract [en]

Machine learning algorithms are frequently applied in data mining applications. Many of the tasks in this domain concern high-dimensional data. Consequently, these tasks are often complex and computationally expensive. This paper presents a GPU-based parallel implementation of the Random Forests algorithm. In contrast to previous work, the proposed algorithm is based on the compute unified device architecture (CUDA). An experimental comparison between the CUDA-based algorithm (CudaRF), and state-of-the-art parallel (FastRF) and sequential (LibRF) Random forests algorithms shows that CudaRF outperforms both FastRF and LibRF for the studied classification task.

Place, publisher, year, edition, pages
Göteborg: Chalmers Institute of Technology , 2010.
Keyword [en]
machine learning, graphics processing unit, random forests
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-7705Local ID: oai:bth.se:forskinfo7C7A825038DE6570C12577E3004FFB7COAI: oai:DiVA.org:bth-7705DiVA: diva2:835353
Conference
Third Swedish Workshop on Multi-core Computing
Available from: 2012-09-18 Created: 2010-11-22 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(378 kB)53 downloads
File information
File name FULLTEXT01.pdfFile size 378 kBChecksum SHA-512
c108f366873f2d3be7172e8431e4c053b51bce1d2836a0a0a8030d4f2247be60a53ea562e8781fcaf1ced1130bd819dd0395767843e7ae19f19082f83ace2d06
Type fulltextMimetype application/pdf

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Grahn, HåkanLavesson, Niklas
Computer Science

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CiteExportLink to record
Permanent link

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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