GPU acceleration of regular expression matching for large datasets
Xiaodong Yu, Michela Becchi · 2013
Regular expression matching is a central task in several networking (and search) applications and has been accelerated on a variety of parallel architectures, including general purpose multi-core processors, network processors, field programmable gate arrays, and ASIC- and TCAM-based systems. All of these solutions are based on finite automata (either in deterministic or non-deterministic form) and mostly focus on effective memory representations for such automata. More recently, a handful of proposals have exploited the parallelism intrinsic in regular expression matching (i.e., coarse-grained packet-level parallelism and fine-grained data structure parallelism) to propose efficient regex-matching designs for GPUs. However, most GPU solutions aim at achieving good performance on small datasets, which are far less complex and problematic than those used in real-world applications.