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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.