Advertisement

Revisiting Multiple Pattern Matching Algorithms for Multi-Core Architecture

  • Guang-Ming TanEmail author
  • Ping Liu
  • Dong-Bo Bu
  • Yan-Bing Liu
Article

Abstract

Due to the huge size of patterns to be searched, multiple pattern searching remains a challenge to several newly-arising applications like network intrusion detection. In this paper, we present an attempt to design efficient multiple pattern searching algorithms on multi-core architectures. We observe an important feature which indicates that the multiple pattern matching time mainly depends on the number and minimal length of patterns. The multi-core algorithm proposed in this paper leverages this feature to decompose pattern set so that the parallel execution time is minimized. We formulate the problem as an optimal decomposition and scheduling of a pattern set, then propose a heuristic algorithm, which takes advantage of dynamic programming and greedy algorithmic techniques, to solve the optimization problem. Experimental results suggest that our decomposition approach can increase the searching speed by more than 200% on a 4-core AMD Barcelona system.

Keywords

parallel algorithm multi-core multiple pattern matching 

Supplementary material

11390_2011_185_MOESM1_ESM.pdf (81 kb)
(PDF 80.6 KB)

References

  1. [1]
    Snort: Open-source network ids/ips. http://www.snort.org, 2011.
  2. [2]
    Villa O, Scarpazza D P, Petrini F. Accelerating real-time string searching with multicore processors. Computer, 2008, 41(4): 42–50.CrossRefGoogle Scholar
  3. [3]
    Bu L, Chandy J A. A cam-based keyword match processor architecture. Journal of Microelectronics, 2006, 37(8): 828–836.CrossRefGoogle Scholar
  4. [4]
    Sourdis I, Pnevmatikatos D. Pre-decoded cams for efficient and high-speed nids pattern matching. In Proc. the 12th Ann. IEEE Symp. Field-Programmable Custom Computing Machines (FCCM2004), Napa, USA, Apr. 20–23, 2004, pp.258-267.Google Scholar
  5. [5]
    Antonatos S, Anagnostakis K G, Markatos E P, Polychronakis M. Performance analysis of content matching intrusion detection systems. In Proc. 2004 Symp. Applications and the Internet (SAINT 2004), Tokyo, Japan, Jan. 26–30, 2004, pp.208-218.Google Scholar
  6. [6]
    Chang C, Paige R. From regular expressions to DFA's using compressed NFA's. In Proc. the 3 rd Ann. Symp. Combinatorial Pattern Matching (CPM1992), Tucson, USA, Apr. 29-May 1, 1992, pp.88-108.Google Scholar
  7. [7]
    Sidhu R, Prasanna V K. Fast regular expression matching using FPGAs. In Proc. the 9th Ann. IEEE Symp. Field-Programmable Custom Computing Machines (FCCM2001), Rohnert, USA, Mar. 29-Apr. 2, 2001, pp.227-238.Google Scholar
  8. [8]
    Hutchings B L, Franklin R, Carver D. Assisting network intrusion detection with recon¯gurable hardware. In Proc. the 10th Ann. IEEE Symp. Field-Programmable Custom Computing Machines (FCCM2002), Napa, USA, Apr. 22–24, 2002, pp.111-120.Google Scholar
  9. [9]
    Jung H J, Baker Z K, Prasanna V K. Performance of FPGA implementation of bit-split architecture for intrusion detection systems. In Proc. the 20th Int. Symp. Parallel and Distributed Processing Symp. (IPDPS 2006), Rhodes Island, Greece, Apr. 25–29, 2006, p.189.Google Scholar
  10. [10]
    Kaushik R, Govindarajan R. Two-level mapping based cache index selection for packet forwarding engines. In Proc. the 15th International Conference on Parallel Architectures and Compilation Techniques (PACT2006), Seattle, USA, Sept. 16–20, 2006, pp.212-221.Google Scholar
  11. [11]
    Scarpazza D P, Villa O, Petrini F. Peak-performance DFA-based string matching on the cell processor. In Proc. the 21st Int. Parallel and Distributed Processing Symp. (IPDPS 2007), Long Beach, USA, Mar. 26–30, 2007, pp.1-8.Google Scholar
  12. [12]
    Gonzalo N, Mathieu R. Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences. Cambridge University Press, New York, NY, USA, 2002.zbMATHGoogle Scholar
  13. [13]
    Aho A V, Corasick M J. Efficient string matching: An aid to bibliographic search. Commun. ACM, 1975, 18(6): 333–340.MathSciNetzbMATHCrossRefGoogle Scholar
  14. [14]
    Wu S, Manber U. A fast algorithm for multi-pattern searching. Technical Report TR-94-17, 1994.Google Scholar
  15. [15]
    Beate C W. A string matching algorithm fast on the average. In Proc. the 6th Colloquium on Automata, Languages and Programming, Graz, Austria, Jul. 14–18, 1979, pp.118-132.Google Scholar
  16. [16]
    Liu P, Liu Y, Tan J. A partition-based efficient algorithm for large scale multiple-strings matching. In Proc. the 12th Int. Conf. String Processing and Information Retrieval, Buenos Aires, Argentina, Nov. 2–4, 2005, pp.399-404.Google Scholar
  17. [17]
    Coffman E G. Computer and Job-Shop Scheduling Theory. Wiley, 1976.Google Scholar
  18. [18]
    Lenstra J K, Kan A R. Complexity of scheduling under precedence constraints. Oper. Res., 1978, 26(1): 22–35.zbMATHCrossRefGoogle Scholar
  19. [19]
    Cormen T H, Leiserson C E, Rivest R L. Introduction to Algorithms. The MIT Press, 2002.Google Scholar

Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2011

Authors and Affiliations

  • Guang-Ming Tan
    • 1
    Email author
  • Ping Liu
    • 2
  • Dong-Bo Bu
    • 2
  • Yan-Bing Liu
    • 2
  1. 1.Key Laboratory of Computer System and Architecture, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Key Laboratory of Network Technology, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

Personalised recommendations