NDLI: Optimizing Regular Expression Matching with SR-NFA on Multi-Core Systems

Content Provider	IEEE Xplore Digital Library
Author	Yang, Y.-H.E. Prasanna, V.K.
Copyright Year	2011
Abstract	Conventionally, regular expression matching (REM) has been performed by sequentially comparing the regular expression (regex) to the input stream, which can be slow due to excessive backtracking (smith:acsac06). Alternatively, the regex can be converted to a deterministic finite automaton (DFA) for efficient matching, which however may require an extremely large state transition table (STT) due to exponential state explosion (meyer:swat71, yu:ancs06). We propose the segmented regex-NFA (SR-NFA) architecture, where the regex is first compiled into modular nondeterministic finite automata (NFA), then partitioned, optimized, and matched efficiently on modern multi-core processors. SR-NFA offers attack-resilient multi-gigabit per second matching throughput, does not suffer from either backtracking or state explosion, and can be rapidly constructed. For regex sets that construct a DFA with moderate state explosion, i.e., on average 200k states in the STT, the proposed SR-NFA is 367k times faster to construct and update and use 23k times less memory than the DFA approach. Running on an 8-core 2.6 GHz Opteron platform, our prototype achieves 2.2 Gbps average matching throughput for regex sets with up to 4,000 SR-NFA states per regex set.
Starting Page	424
Ending Page	433
File Size	443435
Page Count	10
File Format	PDF
ISBN	9781457717949
ISSN	1089795X
DOI	10.1109/PACT.2011.73
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Doped fiber amplifiers Explosions Throughput Vectors Multicore processing Automata Complexity theory multi-core processor Regular expression NFA bit-level parallelism thread-level parallelism
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Hardware and Architecture Software

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