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CudaRF: A CUDA-based Implementation of Random Forests
Responsible organisation
2011 (English)Conference paper, Published 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 Random Forests algorithms (FastRF and LibRF) shows that CudaRF outperforms both FastRF and LibRF for the studied classification task.

Place, publisher, year, edition, pages
Sharm El-Sheikh, Egypt: IEEE , 2011.
Keyword [en]
Random forests, Machine learning, Parallel computing, Graphics processing units, GPGPU
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-7343Local ID: oai:bth.se:forskinfo7680811940312F67C125797C002D7E3DOAI: oai:DiVA.org:bth-7343DiVA: diva2:834950
Conference
9th ACS/IEEE Int'l Conference on Computer Systems And Applications (AICCSA 2011)
Available from: 2012-09-18 Created: 2012-01-05 Last updated: 2015-06-30Bibliographically approved

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fulltext(436 kB)188 downloads
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Grahn, HåkanLavesson, Niklas
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • 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