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Parallel tree-ensemble algorithms for GPUs using CUDA
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present two new parallel implementations of the tree-ensemble algorithms Random Forest (RF) and Extremely randomized trees (ERT) for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). Random Forest and Extremely randomized trees are ensemble learners for classification and regression. 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 Random Forests in the literature are either designed for traditional multi-core CPU platforms or early history GPUs with simpler hardware architecture and relatively few number of cores. 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 as memory hierarchy and thread scheduling. They are implemented using the C/C++ language and the CUDA interface for best possible performance on NVidia-based GPUs. An experimental study comparing with the most important previous solutions for CPU and GPU platforms shows significant improvement for the new implementations, often with several magnitudes.

Place, publisher, year, edition, pages
2013.
Keywords [en]
Random Forest, GPGPU, CUDA, Machine Learning, Parallel Algorithms
Keywords [sv]
Datavetenskap
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-221511Local ID: 2320/13067OAI: oai:DiVA.org:kth-221511DiVA, id: diva2:1175282
Conference
Sixth Swedish Workshop on Multicore Computing (MCC13), 2013
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish Foundation for Strategic Research
Note

QC 20180123

Available from: 2015-12-22 Created: 2018-01-17 Last updated: 2018-01-23Bibliographically approved

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http://www.hh.se/download/18.4cc60a491424e61ad932fe5/1385997376898/MCC132.2.pdf

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Boström, Henrik
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CiteExportLink to record
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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