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International Conference on Vector and Parallel Processing

VECPAR 2018: High Performance Computing for Computational Science – VECPAR 2018 pp 45–61Cite as

Optimizing Packed String Matching on AVX2 Platform

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11333))

Abstract

Exact string matching, searching for all occurrences of given pattern P on a text T, is a fundamental issue in computer science with many applications in natural language processing, speech processing, computational biology, information retrieval, intrusion detection systems, data compression, and etc. Speeding up the pattern matching operations benefiting from the SIMD parallelism has received attention in the recent literature, where the empirical results on previous studies revealed that SIMD parallelism significantly helps, while the performance may even be expected to get automatically enhanced with the ever increasing size of the SIMD registers. In this paper, we provide variants of the previously proposed EPSM and SSEF algorithms, which are originally implemented on Intel SSE4.2 (Streaming SIMD Extensions 4.2 version with 128-bit registers). We tune the new algorithms according to Intel AVX2 platform (Advanced Vector Extensions 2 with 256-bit registers) and analyze the gain in performance with respect to the increasing length of the SIMD registers. Profiling the new algorithms by using the Intel Vtune Amplifier for detecting performance bottlenecks led us to consider the cache friendliness and shared-memory access issues in the AVX2 platform. We applied cache optimization techniques to overcome the problems particularly addressing the search algorithms based on filtering.

Experimental comparison of the new solutions with the previously known-to-be-fast algorithms on small, medium, and large alphabet text files with diverse pattern lengths showed that the algorithms on AVX2 platform optimized cache obliviously outperforms the previous solutions.

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Acknowledgements

Computing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM) under grant number 4004492017.

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Authors and Affiliations

  1. Informatics Institute, Istanbul Technical University, Istanbul, Turkey

    Mehmet Akif Aydoğmuş & M. Oğuzhan Külekci

  2. TUBITAK, The Scientific and Technological Research Council of Turkey, Kocaeli, Turkey

    Mehmet Akif Aydoğmuş

Authors
  1. Mehmet Akif Aydoğmuş
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  2. M. Oğuzhan Külekci
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Corresponding author

Correspondence to Mehmet Akif Aydoğmuş .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, São Paulo, Brazil

    Hermes Senger

  2. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Osni Marques

  3. Universidade Estadual Paulista Júlio de Mesquita Filho, Presidente Prudente, São Paulo, Brazil

    Rogerio Garcia

  4. Universidade Estadual Paulista Júlio de Mesquita Filho, São Paulo, São Paulo, Brazil

    Tatiana Pinheiro de Brito

  5. Universidade Estadual Paulista Júlio de Mesquita Filho, São Paulo, São Paulo, Brazil

    Rogério Iope

  6. Universidade Estadual Paulista Júlio de Mesquita Filho, São Paulo, São Paulo, Brazil

    Silvio Stanzani

  7. Universidad Nacional de San Luis, San Luis, Argentina

    Veronica Gil-Costa

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Aydoğmuş, M.A., Külekci, M.O. (2019). Optimizing Packed String Matching on AVX2 Platform. In: Senger, H., et al. High Performance Computing for Computational Science – VECPAR 2018. VECPAR 2018. Lecture Notes in Computer Science(), vol 11333. Springer, Cham. https://doi.org/10.1007/978-3-030-15996-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-15996-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15995-5

  • Online ISBN: 978-3-030-15996-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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