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Efficient parallel algorithm for computing rough set approximation on GPU

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Abstract

Computation of rough set approximation (RSA) is a critical step for attribute reduction and knowledge acquisition in rough set theory. Continuously improving computation efficiency of RSA is very meaningful, because it can enhance user experience of existing applications. Furthermore, it is helpful to apply rough sets to some fields with high performance requirement. Graphics processing unit (GPU) has gained a lot of attention from scientific communities for its applicability in high-performance computing. Different from existing works, this paper tries to apply GPU to accelerate a state-of-the-art serial algorithm of RSA computation, which is based on radix sorting. Three key steps of the serial algorithm are parallel designed, including object sorting, computation of equivalence classes, and computation of RSA. The experimental results show that the parallel method can accelerate the computation process efficiently.

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Acknowledgements

This study was funded by the National Science Foundation of China (Grand No. 61702128); the Scientific Research Fund of Sichuan Provincial Department (Grand No. 17ZA0201); the Scientific Research Fund of Leshan Normal University (Grand No. Z1325).

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Authors and Affiliations

  1. School of Computer Science, Leshan Normal University, Leshan, 614000, China

    Si-Yuan Jing & Cai-Ming Liu

  2. Sichuan Province University Key Laboratory of Internet Natural Language Intelligent Processing, Leshan Normal University, Leshan, 614000, China

    Si-Yuan Jing & Cai-Ming Liu

  3. Institute of Computing Applications, China Academy of Engineering Physics, Mianyang, 621000, China

    Gong-Liang Li

  4. Faculty of Information Engineering, Guizhou Institute of Technology, Guiyang, 550003, China

    Kai Zeng

  5. School of Computer, China West Normal University, Nanchong, 637009, China

    Wei Pan

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  1. Si-Yuan Jing
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  2. Gong-Liang Li
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  3. Kai Zeng
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  4. Wei Pan
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  5. Cai-Ming Liu
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Correspondence to Si-Yuan Jing.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by A. Di Nola.

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Jing, SY., Li, GL., Zeng, K. et al. Efficient parallel algorithm for computing rough set approximation on GPU. Soft Comput 22, 7553–7569 (2018). https://doi.org/10.1007/s00500-018-3050-z

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  • DOI: https://doi.org/10.1007/s00500-018-3050-z

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