Exploring different automata representations for efficient regular expression matching on GPUs

Xiaodong Yu, Michela Becchi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Regular expression matching is a central task in several networking (and search) applications and has been accelerated on a variety of parallel architectures. All solutions are based on finite automata (either in deterministic or non-deterministic form), and mostly focus on effective memory representations for such automata. Recently, a handful of work has proposed efficient regular expression matching designs for GPUs; however, most of them aim at achieving good performance on small datasets. Nowadays, practical solutions must support the increased size and complexity of real world datasets. In this work, we explore the deployment and optimization of different GPU designs of regular expression matching engines, focusing on large datasets containing a large number of complex patterns.

Original languageEnglish
Title of host publicationPPoPP 2013 - Proceedings of the 2013 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Pages287-288
Number of pages2
DOIs
StatePublished - 2013
Event18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2013 - Shenzhen, China
Duration: 23 Feb 201327 Feb 2013

Publication series

NameProceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP

Conference

Conference18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2013
Country/TerritoryChina
CityShenzhen
Period23/02/1327/02/13

Keywords

  • cuda
  • deep packet inspection
  • finite automata
  • gpgpu
  • regular expression matching

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