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gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for Decision Tree Ensembles
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present two new parallel implementations of the ensemble learning methods Random Forests (RF) and Extremely Randomized Trees (ERT), called gpuRF and gpuERT, for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). RF and ERT are two ensemble methods for generating predictive models that are of high importance within machine learning. They operate by constructing a multitude of decision trees at training time and outputting a prediction by comparing the outputs of the individual trees. Thanks to the inherent parallelism of the task, an obvious platform for its computation is to employ contemporary GPUs with a large number of processing cores. Previous parallel algorithms for RF in the literature are either designed for traditional multi-core CPU platforms or early history GPUs with simpler architecture and relatively few cores. For ERT, only briefly sketched parallelization attempts exist in the literature. The new parallel algorithms are designed for contemporary GPUs with a large number of cores and take into account aspects of the newer hardware architectures, such as memory hierarchy and thread scheduling. They are implemented using the C/C++ language and the CUDA interface to attain the best possible performance on NVidia-based GPUs. An experimental study comparing the most important previous solutions for CPU and GPU platforms to the novel implementations shows significant advantages in the aspect of efficiency for the latter, often with several orders of magnitude.

Place, publisher, year, edition, pages
IEEE Computer Society , 2014.
Keyword [en]
GPGPU, Decision Tree Ensambles, Parallel Processing, Machine Learning, Random Forest, Extremely Randomized Trees, Computer Science
National Category
Computer Sciences Software Engineering
Identifiers
URN: urn:nbn:se:hb:diva-7253Local ID: 2320/14447ISBN: 978-1-4799-4116-2 (print)OAI: oai:DiVA.org:hb-7253DiVA: diva2:887964
Conference
IEEE International Parallel & Distributed Processing Symposium 2014, Phoenix, USA
Funder
Swedish Foundation for Strategic Research
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10Bibliographically approved

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Jansson, KarlSundell, Håkan

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CiteExportLink to record
Permanent link

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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
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