A MemoryEfficient and Modular Approach for LargeScale String

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 1

Introduction n n 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 throughput by exploiting its simple architecture. 2

Definitions 3

Leaf-Attaching Algorithm 4

Leaf-Attaching Algorithm 5

BST String Matching Algorithm 6

Memory Efficiency of The BST String Matching Algorithm 7

Cascading approach 8

Cascading approach 9

Cascading approach 10

Arbitrary-Length String Matching Algorithm 11

Arbitrary-Length String Matching Algorithm 12

Overall Architecture 13

Pattern Matching Module (PMM) Architecture 14

Label Matching Module (LMM) Architecture 15

Dictionary Update n n (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 16

Modular Extensibility n n n horizontally vertically -intra-stream -inter-stream both 17

Experimental Setup 18

Memory Efficiency n n 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. 19

Memory Efficiency 20

Throughput 21

Performance Comparison 22