Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

In this study, to substantially improve the runtimes of exact and approximate string matching algorithms, we propose a tribrid parallel method for bit-parallel algorithms such as the Shift-Or and Wu-Manber algorithms. Our underlying idea is to interpret bit-parallel algorithms as inclusive-scan oper...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 28; no. 7; pp. 1989 - 2002
Main Authors: Mitani, Yasuaki, Ino, Fumihiko, Hagihara, Kenichi
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximate string matching
Approximation algorithms
Automata
bit-parallel algorithm
GPU
Graphics processing units
inclusive scan
Instruction sets
Operators
Parallel processing
Pattern matching
Reproduction (copying)
Segmentation
shift-or algorithm
String matching
Synchronism
Throughput
Wu-Manber algorithm
ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary:In this study, to substantially improve the runtimes of exact and approximate string matching algorithms, we propose a tribrid parallel method for bit-parallel algorithms such as the Shift-Or and Wu-Manber algorithms. Our underlying idea is to interpret bit-parallel algorithms as inclusive-scan operations, which allow these bit-parallel algorithms to run efficiently on a graphics processing unit (GPU); we achieve this speed-up here because inclusive-scan operations not only eliminate duplicate searches between threads but also realize a GPU-friendly memory access pattern that maximizes memory read/write throughput. To realize our ideas, we first define two binary operators and then present a proof regarding the associativity of these operators, which is necessary for the parallelization of the inclusive-scan operations. Finally, we integrate the inclusive-scan scheme into a previous segmentation-based scheme to maximize search throughput, identifying the best tradeoff point between synchronization cost and duplicate work. Through our experiments, we compared our proposed method with previous segmentation-based methods and indexing-based sequence aligners. For online string matching, our proposed method performed 6.7-16.7 times faster than previous methods, achieving a search throughput of up to 1.88 terabits per second (Tbps) on a GeForce GTX TITAN X GPU. We therefore conclude that our proposed method is quite effective for decreasing the runtimes of online string matching of short patterns.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2016.2645222