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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Vydané v:	Proceedings - International Conference on Parallel and Distributed Systems s. 570 - 575
Hlavní autori:	Lin, Cheng-Hung, Huang, Chun-Cheng
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.12.2015
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Cheng-Hung Lin Chun-Cheng Huang
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SubjectTerms	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
Title	High-Performance Parallel Location-Aware Algorithms for Approximate String Matching on GPUs
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