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The Journal of Supercomputing

, Volume 69, Issue 1, pp 273–292 | Cite as

Scalable CAIM discretization on multiple GPUs using concurrent kernels

  • Alberto Cano
  • Sebastián VenturaEmail author
  • Krzysztof J. Cios
Article

Abstract

Class-attribute interdependence maximization (CAIM) is one of the state-of-the-art algorithms for discretizing data for which classes are known. However, it may take a long time when run on high-dimensional large-scale data, with large number of attributes and/or instances. This paper presents a solution to this problem by introducing a graphic processing unit (GPU)-based implementation of the CAIM algorithm that significantly speeds up the discretization process on big complex data sets. The GPU-based implementation is scalable to multiple GPU devices and enables the use of concurrent kernels execution capabilities of modern GPUs. The CAIM GPU-based model is evaluated and compared with the original CAIM using single and multi-threaded parallel configurations on 40 data sets with different characteristics. The results show great speedup, up to 139 times faster using four GPUs, which makes discretization of big data efficient and manageable. For example, discretization time of one big data set is reduced from 2 h to \(<\)2 min.

Keywords

Supervised discretization Parallel implementation of CAIM algorithm GPU CUDA 

Notes

Acknowledgments

This work has been supported by the Grant from the National Institutes of Health 1R01HD056235-01A1 (KJC), the Regional Government of Andalusia and the Ministry of Science and Technology project TIN-2011-22408 (SV), and the Ministry of Education FPU AP2010-0042 (AC). The authors also thank Duane Merrill and Sean Baxter from NVIDIA for their advise on improving efficiency of the sorting methods.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Alberto Cano
    • 1
  • Sebastián Ventura
    • 1
    • 2
    Email author
  • Krzysztof J. Cios
    • 3
    • 4
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCórdobaSpain
  2. 2.Information Systems Department, Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJiddaSaudi Arabia
  3. 3.Department of Computer ScienceVirginia Commonwealth UniversityRichmondUSA
  4. 4.IITiS Polish Academy of SciencesGliwicePoland

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