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A parallel approximate string matching under Levenshtein distance on graphics processing units using warp-shuffle operations

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  • ThienLuan Ho
  • Seung-Rohk Oh
  • HyunJin Kim

Abstract

Approximate string matching with k-differences has a number of practical applications, ranging from pattern recognition to computational biology. This paper proposes an efficient memory-access algorithm for parallel approximate string matching with k-differences on Graphics Processing Units (GPUs). In the proposed algorithm, all threads in the same GPUs warp share data using warp-shuffle operation instead of accessing the shared memory. Moreover, we implement the proposed algorithm by exploiting the memory structure of GPUs to optimize its performance. Experiment results for real DNA packages revealed that the performance of the proposed algorithm and its implementation archived up to 122.64 and 1.53 times compared to that of sequential algorithm on CPU and previous parallel approximate string matching algorithm on GPUs, respectively.

Suggested Citation

  • ThienLuan Ho & Seung-Rohk Oh & HyunJin Kim, 2017. "A parallel approximate string matching under Levenshtein distance on graphics processing units using warp-shuffle operations," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-15, October.
    • Handle: RePEc:plo:pone00:0186251
    DOI: 10.1371/journal.pone.0186251
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    References listed on IDEAS

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    1. Kentaro Inoue & Shinichi Shimozono & Hideaki Yoshida & Hiroyuki Kurata, 2012. "Application of Approximate Pattern Matching in Two Dimensional Spaces to Grid Layout for Biochemical Network Maps," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-12, June.
    2. Marc-André Schulz & Barbara Schmalbach & Peter Brugger & Karsten Witt, 2012. "Analysing Humanly Generated Random Number Sequences: A Pattern-Based Approach," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-7, July.
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