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Accelerating Pattern Matching with CPU-GPU Collaborative Computing

  • Victoria Sanz
  • Adrián Pousa
  • Marcelo Naiouf
  • Armando De Giusti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11334)

Abstract

Pattern matching algorithms are used in several areas such as network security, bioinformatics and text mining. In order to support large data and pattern sets, these algorithms have to be adapted to take advantage of the computing power of emerging parallel architectures. In this paper, we present a parallel algorithm for pattern matching on CPU-GPU heterogeneous systems, which is based on the Parallel Failureless Aho-Corasick algorithm (PFAC) for GPU. We evaluate the performance of the proposed algorithm on a machine with 36 CPU cores and 1 GPU, using data and pattern sets of different size, and compare it with that of PFAC for GPU and the multithreaded version of PFAC for shared-memory machines. The results reveal that our proposal achieves higher performance than the other two approaches for data sets of considerable size, since it uses both CPU and GPU cores.

Keywords

Pattern matching CPU-GPU collaborative computing CPU-GPU heterogeneous systems Hybrid programming Aho-Corasick 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Victoria Sanz
    • 1
    • 2
  • Adrián Pousa
    • 1
  • Marcelo Naiouf
    • 1
  • Armando De Giusti
    • 1
    • 3
  1. 1.III-LIDI, School of Computer SciencesNational University of La PlataLa PlataArgentina
  2. 2.CICBuenos AiresArgentina
  3. 3.CONICETBuenos AiresArgentina

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