A decision tree using CUDA GPUs

Chun Chieh Chiu*, Guo Heng Luo, Shyan-Ming Yuan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Classification is an important issue both in Machine Learning and Data Mining. Decision tree is one of the famous classification models. In the reality case, the dimension of data is high and the data size is huge. Building a decision in large data base cost much time in computation. It is a computationally expensive problem. GPU is a special design processor of graphic. The highly parallel features of graphic processing made today's GPU architecture. GPGPU means use GPU to solve non-graphic problems which need amounts of computation power. Since the high performance and capacity/price ratio, many researches use GPU to process lots computation. Compute Unified Device Architecture (CUDA) is a GPGPU solution provided by NVIDIA. This paper provides a new parallel decision tree algorithm base on CUDA. The algorithm parallel computes building phase of decision tree.

Original languageEnglish
Title of host publicationiiWAS2011 - 13th International Conference on Information Integration and Web-Based Applications and Services
Pages399-402
Number of pages4
DOIs
StatePublished - 1 Dec 2011
Event13th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2011 - Ho Chi Minh City, Viet Nam
Duration: 5 Dec 20117 Dec 2011

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2011
CountryViet Nam
CityHo Chi Minh City
Period5/12/117/12/11

Keywords

  • CUDA
  • GPGPU
  • classification
  • data mining
  • decision tree

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  • Cite this

    Chiu, C. C., Luo, G. H., & Yuan, S-M. (2011). A decision tree using CUDA GPUs. In iiWAS2011 - 13th International Conference on Information Integration and Web-Based Applications and Services (pp. 399-402). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2095536.2095615