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Accelerating Set Similarity Joins Using GPUs

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9940))

Abstract

We propose a scheme for 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 sacrificing 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 the editors and the reviewers for their remarks and suggestions. 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. IRIT, Paul Sabatier University , Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz , Linz, Austria

    Josef Küng

  3. FAW, University of Linz , Linz, Austria

    Roland Wagner

  4. HP Labs , Sunnyvale, California, USA

    Qimin Chen

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Cruz, M.S.H., Kozawa, Y., Amagasa, T., Kitagawa, H. (2016). Accelerating Set Similarity Joins Using GPUs. In: Hameurlain, A., Küng, J., Wagner, R., Chen, Q. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII. Lecture Notes in Computer Science(), vol 9940. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53455-7_1

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  • DOI: https://doi.org/10.1007/978-3-662-53455-7_1

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