Space-efficient computation of parallel approximate string matching

Approximate string matching (ASM) has a number of applications in many disciplines, ranging from information retrieval to gene matching. Conventional solution to this problem is based on the dynamic programming-based strategy having quadratic space and time complexity. The complexity of the conventi...

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Published in:	The Journal of supercomputing Vol. 79; no. 8; pp. 9093 - 9126
Main Authors:	Sadiq, Muhammad Umair, Yousaf, Muhammad Murtaza
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2023 Springer Nature B.V
Subjects:	Algorithms Approximate string matching Compilers Complexity Computer Science Dynamic programming Gene sequencing Information retrieval Interpreters Processor Architectures Programming Languages Run time (computers) Performance evaluation Parallel algorithm Dynamic programming Approximate string matching OpenMP GPUs
ISSN:	0920-8542, 1573-0484
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Abstract	Approximate string matching (ASM) has a number of applications in many disciplines, ranging from information retrieval to gene matching. Conventional solution to this problem is based on the dynamic programming-based strategy having quadratic space and time complexity. The complexity of the conventional solution makes it impractical to search queries from the huge sequences having billions of characters. Therefore, many studies have been proposed that improves on the space and time requirement of the basic solution which includes heuristic, filtration, and index-based solutions. These existing solutions obtain the better performance by compromising on the completeness of the search. In this paper, we proposed the linear space algorithm for the approximate string matching problem while retaining the time complexity of conventional solution. The proposed method works in linear space without omitting any regions in the given text; hence, it finds all the possible matches. Conventional dynamic programming solution is modified in such a way that storage of complete trace back table is avoided by keeping only running count of each edit operation in the memory. A variety of laws and facts are discovered in classical dynamic programming table in that regard. We also presented the parallel approach to the proffered algorithm to improve the running time of the algorithm. The algorithm is evaluated on the CUDA-enabled GPUs. DNA sequences of sizes between 250 and 970 MBP are used for evaluation. Moreover, experiments are also performed by using natural language text to highlight the broader applicability of the proposed algorithm. Results show the substantial superiority of the algorithm in terms of performance and scalability compared to the state-of-the-art algorithms.
AbstractList	Approximate string matching (ASM) has a number of applications in many disciplines, ranging from information retrieval to gene matching. Conventional solution to this problem is based on the dynamic programming-based strategy having quadratic space and time complexity. The complexity of the conventional solution makes it impractical to search queries from the huge sequences having billions of characters. Therefore, many studies have been proposed that improves on the space and time requirement of the basic solution which includes heuristic, filtration, and index-based solutions. These existing solutions obtain the better performance by compromising on the completeness of the search. In this paper, we proposed the linear space algorithm for the approximate string matching problem while retaining the time complexity of conventional solution. The proposed method works in linear space without omitting any regions in the given text; hence, it finds all the possible matches. Conventional dynamic programming solution is modified in such a way that storage of complete trace back table is avoided by keeping only running count of each edit operation in the memory. A variety of laws and facts are discovered in classical dynamic programming table in that regard. We also presented the parallel approach to the proffered algorithm to improve the running time of the algorithm. The algorithm is evaluated on the CUDA-enabled GPUs. DNA sequences of sizes between 250 and 970 MBP are used for evaluation. Moreover, experiments are also performed by using natural language text to highlight the broader applicability of the proposed algorithm. Results show the substantial superiority of the algorithm in terms of performance and scalability compared to the state-of-the-art algorithms. Approximate string matching (ASM) has a number of applications in many disciplines, ranging from information retrieval to gene matching. Conventional solution to this problem is based on the dynamic programming-based strategy having quadratic space and time complexity. The complexity of the conventional solution makes it impractical to search queries from the huge sequences having billions of characters. Therefore, many studies have been proposed that improves on the space and time requirement of the basic solution which includes heuristic, filtration, and index-based solutions. These existing solutions obtain the better performance by compromising on the completeness of the search. In this paper, we proposed the linear space algorithm for the approximate string matching problem while retaining the time complexity of conventional solution. The proposed method works in linear space without omitting any regions in the given text; hence, it finds all the possible matches. Conventional dynamic programming solution is modified in such a way that storage of complete trace back table is avoided by keeping only running count of each edit operation in the memory. A variety of laws and facts are discovered in classical dynamic programming table in that regard. We also presented the parallel approach to the proffered algorithm to improve the running time of the algorithm. The algorithm is evaluated on the CUDA-enabled GPUs. DNA sequences of sizes between 250 and 970 MBP are used for evaluation. Moreover, experiments are also performed by using natural language text to highlight the broader applicability of the proposed algorithm. Results show the substantial superiority of the algorithm in terms of performance and scalability compared to the state-of-the-art algorithms.
Author	Sadiq, Muhammad Umair Yousaf, Muhammad Murtaza
Author_xml	– sequence: 1 givenname: Muhammad Umair surname: Sadiq fullname: Sadiq, Muhammad Umair email: m.umair@gcu.edu.pk organization: Department of Computer Science, Government College University, Faculty of Computing and Information Technology, University of the Punjab – sequence: 2 givenname: Muhammad Murtaza surname: Yousaf fullname: Yousaf, Muhammad Murtaza organization: Department of Software Engineering, Faculty of Computing and Information Technology, University of the Punjab
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Cites_doi	10.1093/bioinformatics/bts505 10.1371/journal.pone.0186251 10.1145/2893488 10.1007/s11227-017-2192-6 10.1016/j.jda.2013.08.004 10.1109/TCBB.2015.2465900 10.1371/journal.pone.0065632 10.1145/316542.316550 10.1109/TCBB.2018.2822687 10.1007/s10586-019-02962-w 10.1016/j.jda.2004.08.006 10.1371/journal.pone.0251047 10.1145/360825.360861 10.1093/bioinformatics/btp324 10.1038/nmeth.2221 10.1109/ICNC.2012.43 10.1109/IPDPSW.2012.298 10.1016/j.ic.2010.04.001 10.1093/bioinformatics/btw753 10.1186/s12859-015-0626-9 10.1109/TPDS.2016.2645222 10.1109/NAS.2013.40 10.1109/AVSS.2017.8078538 10.1186/gb-2009-10-3-r25 10.1016/0196-6774(80)90016-4 10.1093/comjnl/bxaa193 10.1007/BF01188584 10.1038/nmeth0810-576 10.1186/s12859-016-1320-2 10.1109/ICDE.2016.7498238 10.1145/266714.266721 10.1142/S0129183116500297 10.1145/135239.135244 10.1016/0196-6774(85)90023-9 10.1016/0022-0000(88)90045-1 10.1145/2640087.2644170 10.1109/TKDE.2007.250581 10.1109/FOCS.2009.11 10.1007/s11042-020-08913-7 10.1145/375360.375365 10.1137/0219067 10.1145/321796.321811
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publication-title: J ACM (JACM) doi: 10.1145/321796.321811
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Snippet	Approximate string matching (ASM) has a number of applications in many disciplines, ranging from information retrieval to gene matching. Conventional solution...
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SubjectTerms	Algorithms Approximate string matching Compilers Complexity Computer Science Dynamic programming Gene sequencing Information retrieval Interpreters Processor Architectures Programming Languages Run time (computers)
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Title	Space-efficient computation of parallel approximate string matching
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