High-Performance Parallel Location-Aware Algorithms for Approximate String Matching on GPUs

Approximate string matching has been widely used in many applications, including deoxyribonucleic acid sequence searching, spell checking, text mining, and spam filters. The method is designed to find all locations of strings that approximately match a pattern in accordance with the number of insert...

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Bibliographic Details
Published in:Proceedings - International Conference on Parallel and Distributed Systems pp. 570 - 575
Main Authors: Lin, Cheng-Hung, Huang, Chun-Cheng
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.12.2015
Subjects:
Acceleration
Algorithm design and analysis
Algorithms
Approximate string matching
Approximation algorithms
bit-parallel algorithm
Business process re-engineering
Computer networks
Data mining
Digital signal processing
graphic processing units
Insertion
Levenshtein distance
nondeterministic finite automaton
parallel algorithm
Parallel processing
Pattern matching
Position (location)
Registers
Searching
ISSN:1521-9097
Online Access:Get full text
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Summary:Approximate string matching has been widely used in many applications, including deoxyribonucleic acid sequence searching, spell checking, text mining, and spam filters. The method is designed to find all locations of strings that approximately match a pattern in accordance with the number of insertion, deletion, and substitution operations. Among the proposed algorithms, the bit-parallel algorithms are considered to be the best and highly efficient algorithms. However, the traditional bit-parallel algorithms lacks the ability of identifying the start and end positions of a matched pattern. Furthermore, acceleration of the bit-parallel algorithms has become a crucial issue for processing big data nowadays. In this paper, we propose two kinds of parallel location-aware algorithms called data-segmented parallelism and high-degree parallelism as means to accelerate approximate string matching using graphic processing units. Experimental results show that the high-degree parallelism on GPUs achieves significant improvement in system and kernel throughputs compared to CPU counterparts. Compared to state-of-the-art approaches, the proposed high-degree parallelism achieves 11 to 105 times improvement.
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ISSN:1521-9097
DOI:10.1109/ICPADS.2015.77