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A CUDA Implementation of Random Forests: Early Results
School of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.
School of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.ORCID iD: 0000-0002-0535-1761
School of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.
School of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.
2010 (English)Conference paper, Published paper (Refereed)
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 Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37929OAI: oai:DiVA.org:hj-37929DiVA: diva2:1160019
Conference
Third Swedish Workshop on Multi-core Computing
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2017-11-24Bibliographically approved

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fulltext(378 kB)4 downloads
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Lavesson, Niklas

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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