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Compact State Machines for High Performance Pattern Matching

Compact State Machines for High Performance Pattern Matching. Authors: Piyachon, P. Yan Luo Publisher: Design Automation Conference, 2007. DAC '07. 44th ACM/IEEE 4-8 June 2007 Page(s):493 - 496 Present: Chia-Ming ,Chuang Date: 10, 22, 2008.

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Compact State Machines for High Performance Pattern Matching

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  1. Compact State Machines for High Performance Pattern Matching Authors:Piyachon, P. Yan Luo Publisher:Design Automation Conference, 2007. DAC '07. 44th ACM/IEEE 4-8 June 2007 Page(s):493 - 496 Present:Chia-Ming ,Chuang Date:10, 22, 2008 Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C. 1

  2. Outline • 1. Introduction • 2. State re-rebeling & memory partition • 3. Architecture • 4. Experiments • 5. Conclusion 2

  3. Introduction (1/3) References [8]Efficient Memory Utilization on Network Processors for Deep Packet Inspection Authors: Piti Piyachon and Yan Luo Publisher: Architecture for networking and communications systems, Proceedings of the 2006 ACM/IEEE symposium 3

  4. Bit-level AC state machine Introduction (2/3) 0001 0010 1000 0100 4

  5. Introduction (3/3) 5

  6. Outline • 1. Introduction • 2. State re-rebeling & memory partition • 3. Architecture • 4. Experiments • 5. Conclusion 6

  7. State re-rebeling & memory partition (1/3) re-labeling algorithm 7

  8. State re-rebeling & memory partition (2/3) λ is non-zero keywordID vectors 8

  9. State re-rebeling & memory partition (3/3) we propose to separate the storage of keywordID matrix from the storage of next-state matrix. Number of k patterns 9

  10. Outline • 1. Introduction • 2. State re-rebeling & memory partition • 3. Architecture • 4. Experiments • 5. Conclusion 10

  11. Architecture (1/3) 11

  12. Architecture (2/3) 12

  13. Outline • 1. Introduction • 2. State re-rebeling & memory partition • 3. Architecture • 4. Experiments • 5. Conclusion 13

  14. Conclusion (1/2) Number of k patterns 14

  15. Conclusion (2/2) K*k K*λ 15

  16. Outline • 1. Introduction • 2. State re-rebeling & memory partition • 3. Architecture • 4. Experiments • 5. Conclusion 16

  17. Conclusion (1/1) (ㄧ) We propose to re-label the states in a state machine such that the matching states are clustered at the beginning of a memory space (二) The experiment results show significant reduction (up to 80.1%) on the memory consumption of state machines. 17

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