a memory-efficient and modular approach for large-scale string pattern matching
Post on 01-Jan-2016
28 Views
Preview:
DESCRIPTION
TRANSCRIPT
1
A Memory-Efficient and Modular Approach for Large-Scale String
Pattern Matching
Author:Hoang Le, Viktor K. PrasannaPublisher: IEEE Transactions on Computers, 2012Presenter:Zi-Yang OuDate:2012/02/29
Introduction
2
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.
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 (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 horizontally
vertically
-intra-stream
-inter-stream
both
17
Experimental Setup
18
Memory Efficiency
19
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.
Memory Efficiency
20
Throughput
21
Performance Comparison
22
top related