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An Efficient Parallel String Matching Algorithm Based on DFA

Authors: Yujian Fan; Hongli Zhang; Jiahui Liu; Dongliang Xu;

An Efficient Parallel String Matching Algorithm Based on DFA

Abstract

The classical string matching algorithms are facing a great challenge on speed due to the rapid growth of information on Internet. Meanwhile, multi-core CPU has been widespread on computers. But classical string matching algorithms does not apply to multi-core CPU flexibly. It not only affects the run-time speed, but also makes a waste of the resource on CPU. In this paper, we proposed a parallel string matching algorithm based on DFA, it solved the problem effectively. By classification on the first letter of each pattern, all CPU cores could work at the same time, which do not conflict. Experiments demonstrate whether the hit rate is high or low, the algorithm has an ideal performance.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
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