A Memory-Efficient and Modular Approach for Large-Scale String Pattern Matching

1. A Memory-Efficient and Modular Approach for Large-Scale String Pattern Matching Author: Hoang Le, Viktor K. Prasanna Publisher: IEEE Transactions on Computers, 2012 Presenter: Zi-Yang Ou Date: 2012/02/29

2. Introduction • An algorithm called leaf-attaching to efficiently disjoint a given dictionary without increasing the number of patterns. • An architecture that achieves a memory efficiency of 0.56 (for Rogets) and 1.32 byte/char (for Snort). State-of-the-art designs can only achieve the memory efficiency of over 2 byte/char in the best case. • The implementation on ASIC and FPGA shows a sustained aggregated throughput of 24 Gbps and 3.2 Gbps, respectively. • The design can be duplicated to improve the throughput by exploiting its simple architecture.

3. Definitions

4. Leaf-Attaching Algorithm

5. Leaf-Attaching Algorithm

6. BST String Matching Algorithm

7. Memory Efficiency of The BST String Matching Algorithm

8. Cascading approach

9. Cascading approach

10. Cascading approach

11. Arbitrary-Length String Matching Algorithm

12. Arbitrary-Length String Matching Algorithm

13. Overall Architecture

14. Pattern Matching Module (PMM) Architecture

15. Label Matching Module (LMM) Architecture

16. Dictionary Update • (1) pattern deletion -(a) including more than one pattern -(b) including only one pattern -lazy deletion -complete deletion • (2) new pattern insertion -has parent pattern(s) -has no parent pattern

17. Modular Extensibility • horizontally • vertically -intra-stream -inter-stream • both

18. Experimental Setup

19. Memory Efficiency • The window size L should be greater than or equal to the matching latency of the LMM. • Hence, 3 values of L(16, 20, 24) are used in our analysis.

20. Memory Efficiency

21. Throughput

22. Performance Comparison