Accelerating pattern matching using a novel parallel algorithm on gpus

Cheng Hung Lin, Chen Hsiung Liu, Lung Sheng Chien, Shih Chieh Chang

Research output: Contribution to journalArticlepeer-review

70 Citations (Scopus)


Graphics processing units (GPUs) have attracted a lot of attention due to their cost-effective and enormous power for massive data parallel computing. In this paper, we propose a novel parallel algorithm for exact pattern matching on GPUs. A traditional exact pattern matching algorithm matches multiple patterns simultaneously by traversing a special state machine called an Aho-Corasick machine. Considering the particular parallel architecture of GPUs, in this paper, we first propose an efficient state machine on which we perform very efficient parallel algorithms. Also, several techniques are introduced to do optimization on GPUs, including reducing global memory transactions of input buffer, reducing latency of transition table lookup, eliminating output table accesses, avoiding bank-conflict of shared memory, coalescing writes to global memory, and enhancing data transmission via peripheral component interconnect express. We evaluate the performance of the proposed algorithm using attack patterns from Snort V2.8 and input streams from DEFCON. The experimental results show that the proposed algorithm performed on NVIDIA GPUs achieves up to 143.16-Gbps throughput, 14.74 times faster than the Aho-Corasick algorithm implemented on a 3.06-GHz quad-core CPU with the OpenMP. The library of the proposed algorithm is publically accessible through Google Code.

Original languageEnglish
Article number12
Pages (from-to)1906-1916
Number of pages11
JournalIEEE Transactions on Computers
Issue number10
Publication statusPublished - 2013


  • Aho-Corasick
  • Graphics processing units
  • Parallel algorithm
  • Pattern matching

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


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