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A GPU-based Bit-Parallel Multiple Pattern Matching Algorithm

Authors: Che-Lun Hung; Tzu-Hung Hsu; Hsiao-Hsi Wang; Chun-Yuan Lin;

A GPU-based Bit-Parallel Multiple Pattern Matching Algorithm

Abstract

String matching algorithms have played critical role in many applications, such as DNA sequence comparison, network intrusion detection systems, and so forth. In this paper, we present a parallel multiple pattern matching method based on general purpose graphic processing units to realize fast string searching. In proposed method, we adopt a bit-parallel pattern comparison concept to accelerates string search to achieve efficient parallel search of multiple patterns of different lengths. In addition, we use CUDA framework to enhance the performance of searching string by leveraging GPU computing power. From the experimental results, the proposed method can achieve higher search throughput than other string matching methods. The proposed method is useful for genome sequence comparison and packet payload filtering.

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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!
3
Average
Average
Average
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