Parallel Implementation of Fuzzified Pattern Matching Algorithm on GPU

Author

Soroushnia, Shima ; Daneshtalab, Masoud ; Pahikkala, Tapio ; Plosila, Juha

Author_Institution

Dept. of Inf. Technol., Univ. of Turku, Turku, Finland

fYear

2015

fDate

4-6 March 2015

Firstpage

341

Lastpage

344

Abstract

Approximate pattern discovery is one of the fundamental and challenging problems in computer science. Fast and high performance algorithms are highly demanded in many applications in bioinformatics and computational molecular biology, which are the domains that are mostly and directly benefit from any enhancement of pattern matching theoretical knowledge and solutions. This paper proposed an efficient GPU implementation of fuzzified Aho-Corasick algorithm using Levenshtein method and N-gram technique as a solution for approximate pattern matching problem.

Keywords

approximation theory; bioinformatics; fuzzy set theory; graphics processing units; molecular biophysics; pattern matching; GPU; Levenshtein method; N-gram technique; approximate pattern discovery; approximate pattern matching problem; bioinformatics; computational molecular biology; computer science; fuzzified Aho-Corasick algorithm; fuzzified pattern matching algorithm; parallel implementation; Algorithm design and analysis; Approximation algorithms; Automata; Databases; Graphics processing units; Instruction sets; Pattern matching; Aho-Corasick; GPU; Pattern matching; fuzzy;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel, Distributed and Network-Based Processing (PDP), 2015 23rd Euromicro International Conference on

Conference_Location

Turku

ISSN

1066-6192

Type

conf

DOI

10.1109/PDP.2015.75

Filename

7092742