Graphic processing unit (GPU)-based non-deterministic finite automation (NFA) matching method and device

Abstract

The invention discloses a GPU-based NFA matching method and a device. The matching method includes calculating pairwise compatibility of all the states in the NFA, encoding each state according to the compatibility to form virtual NFA, and obtaining a virtual NFA state transition table corresponding to the virtual NFA, wherein the compatibility refers to that if two states in the NFA are not in an active state simultaneously, they are compatible, otherwise, they are not compatible; and then storing the virtual NFA state transition table in a global memory of a GPU, and matching data in a to-be-processed data package after an interleaving process based on the virtual NFA state transition table. According to an embodiment, the complexity during the matching process can be reduced effectively, the matching speed of the NFA can be improved, and problems in the prior art are well solved.

Description

Technical field [0001] The present invention relates to the technical field of computer applications, in particular to a GPU (graphic processing unit, graphics processing unit)-based NFA (non-deterministic finite automaton, non-deterministic finite automaton) matching method and device. Background technique [0002] In the field of computer technology, regular expressions are used to describe or match a single string of strings that conform to a certain syntax rule. In many text editors or other tools, regular expressions are usually used to retrieve and / or replace text content that meets a certain pattern. At present, many programming languages ​​support string manipulation using regular expressions. Regular expressions are widely used in modern computer applications. Regular expressions are generally recognized and adopted because of their simplicity, efficiency, and powerful text processing capabilities. [0003] A regular expression is a pattern that describes a string that c...

AI Technical Summary

Problems solved by technology

[0005] Compared with DFA, the storage volume of the corresponding NFA does not have the problem of exponential expansion, and it shows a linear growth relationship with the size of the regular expression rule set. However, during the NFA matching process, each state contained in the NFA may have more than one input character. A target state is activated, so there will be an uncertain number of states in the active state during the operation of the NFA. These active states form an active state set. Therefore, in the NFA matching process, it is necessary to maintain an active state set for The conversion of input characters is represented by the conversion between state sets, and the corresponding matching process is more complicated than that under DFA, resulting in lower matching efficiency of NFA

At present, there is no way to reduce the complexity of the NFA matching process to effectively improve the efficiency of the corresponding NFA matching process

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