Efficient parallel automata construction for hybrid resource-impelled data-matching

•We present an innovative heterogeneous architecture for multiple pattern matching.•We show how to optimize the performance of the construction stages significantly.•First known hybrid-parallel, full-duplex model for fast self-adjustable automata.•Support for on-demand regular-expression scanning and custom heuristics.•Includes resource monitor watchdog for limiting resource consumption on GPUs.

We are presenting an innovative, massively-parallel heterogeneous architecture for the very fast construction and implementation of very large Aho–Corasick and Commentz-Walter pattern-matching automata, commonly used in data-matching applications, and validate its use with large sets of data actively used in intrusion detection systems. Our approach represents the first known hybrid-parallel model for the construction of such automata and the first to allow self-adjusting pattern-matching automata in real-time by allowing full-duplex transfers at maximum throughput between the host (CPU) and the device (GPU). The architecture we propose is easily scalable to multi-GPU and multi-CPU systems and benefits greatly from GPU acceleration, also relying on a highly-efficient storage model for the automata and includes on-demand support for regular-expression matching, as well as support for custom heuristics to be built on top of the architecture, at different processing stages.