Efficient parallel automata construction for hybrid resource-impelled data-matching |
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| Affiliation: | 1. Department of Computer, College of Mechatronic, Karaj Branch, Islamic Azad University, Alborz, Iran;2. Natural Language Processing (NLP) Research Lab., Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G. C., Tehran, Iran;1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, 350118, China;2. Fujian Provincial Key Laboratory of Big Data Mining and Applications (Fujian University of Technology), Fuzhou, Fujian, 350118, China |
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| Abstract: | 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. |
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| Keywords: | Hybrid-parallel Heterogeneous architecture Graphics processing unit Multiple pattern matching Aho–Corasick Commentz-Walter |
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