Exploring different automata representations for efficient regular expression matching on GPUs

Xiaodong Yu, Michela Becchi

Research output: Contribution to journalArticlepeer-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. Copyright is held by the author/owner(s).

Original languageEnglish
Pages (from-to)287-288
Number of pages2
JournalACM SIGPLAN Notices
Volume48
Issue number8
DOIs
StatePublished - Aug 2013

Keywords

  • CUDA
  • Deep packet inspection
  • Finite automata
  • GPGPU
  • Regular expression matching

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