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GPU Acceleration of Set Similarity Joins

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Database and Expert Systems Applications (Globe 2015, DEXA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9261))

Abstract

We propose a scheme of efficient set similarity joins on Graphics Processing Units (GPUs). Due to the rapid growth and diversification of data, there is an increasing demand for fast execution of set similarity joins in applications that vary from data integration to plagiarism detection. To tackle this problem, our solution takes advantage of the massive parallel processing offered by GPUs. Additionally, we employ MinHash to estimate the similarity between two sets in terms of Jaccard similarity. By exploiting the high parallelism of GPUs and the space efficiency provided by MinHash, we can achieve high performance without renouncing accuracy. Experimental results show that our proposed method is more than two orders of magnitude faster than the serial version of CPU implementation, and 25 times faster than the parallel version of CPU implementation, while generating highly precise query results.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml/datasets/.

  2. 2.

    http://trec.nist.gov/data/t9_filtering.html.

  3. 3.

    http://fimi.ua.ac.be/data/.

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Acknowledgments

We thank Neil Millar and the reviewers for their feedback. This research was partly supported by the Grant-in-Aid for Scientific Research (B) (#26280037) from the Japan Society for the Promotion of Science.

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Authors and Affiliations

  1. Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan

    Mateus S. H. Cruz & Yusuke Kozawa

  2. Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Japan

    Toshiyuki Amagasa & Hiroyuki Kitagawa

Authors
  1. Mateus S. H. Cruz
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  2. Yusuke Kozawa
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  3. Toshiyuki Amagasa
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  4. Hiroyuki Kitagawa
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Corresponding author

Correspondence to Mateus S. H. Cruz .

Editor information

Editors and Affiliations

  1. Hewlett-Packard Enterprise, Sunnyvale, California, USA

    Qiming Chen

  2. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  3. Blaise Pascal University, Aubiere, France

    Farouk Toumani

  4. University of Linz, Linz, Austria

    Roland Wagner

  5. Universidad Politécnica de Valencia, Valencia, Spain

    Hendrik Decker

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Cruz, M.S.H., Kozawa, Y., Amagasa, T., Kitagawa, H. (2015). GPU Acceleration of Set Similarity Joins. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_26

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  • DOI: https://doi.org/10.1007/978-3-319-22849-5_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22848-8

  • Online ISBN: 978-3-319-22849-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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