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Parallel Approaches to the String Matching Problem on the GPU

Authors:

Saman Ashkiani,

John D. OwensAuthors Info & Claims

SPAA '16: Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures

Pages 275 - 285

https://doi.org/10.1145/2935764.2935800

Published: 11 July 2016 Publication History

Abstract

We design a family of parallel algorithms and GPU implementations for the exact string matching problem, based on Rabin-Karp (RK) randomized string matching. We describe and analyze three primary parallel approaches to binary string matching: cooperative (CRK), divide-and-conquer (DRK), and a novel hybrid of both (HRK). The CRK is most effective for large patterns (>8K characters), while the DRK approach is superior for shorter patterns. We then generalize the DRK to support any alphabet size without loss of performance. Our DRK method achieves up to a 64 GB/s processing rate on 8-character patterns from an 8-bit alphabet on an NVIDIA Tesla K40c GPU. We next demonstrate a novel parallel two-stage matching method (DRK-2S), which first skims the text for a smaller subset of the pattern and then verifies all potential matches in parallel. Our DRK-2S method is superior for pattern sizes up to 64k compared to the fastest CPU-based string matching implementations. With an 8-bit alphabet and up to 1k-character patterns, we get a geometric mean speedup of 4.81x against the best CPU methods, and can achieve a processing rate of at least 53 GB/s.

References

[1]

M. C. Schatz and C. Trapnell, "Fast exact string matching on the GPU," University of Maryland, Tech. Rep., 2007.

[2]

C.-H. Lin, C.-H. Liu, and S.-C. Chang, "Accelerating regular expression matching using hierarchical parallel machines on GPU," in IEEE Global Telecommunications Conference, ser. GLOBECOMM 2011. IEEE, Dec. 2011, pp. 1--5.

[3]

R. M. Karp and M. O. Rabin, "Efficient randomized pattern-matching algorithms," IBM Journal of Research and Development, vol. 31, no. 2, pp. 249--260, Mar. 1987.

Digital Library

[4]

D. E. Knuth, J. H. Morris, Jr, and V. R. Pratt, "Fast pattern matching in strings," SIAM Journal on Computing, vol. 6, no. 2, pp. 323--350, 1977.

Digital Library

[5]

R. S. Boyer and J. S. Moore, "A fast string searching algorithm," Communications of the ACM, vol. 20, no. 10, pp. 762--772, Oct. 1977.

Digital Library

[6]

T. Lecroq, "Fast exact string matching algorithms," Information Processing Letters, vol. 102, no. 6, pp. 229--235, Jun. 2007.

Digital Library

[7]

S. Faro and T. Lecroq, "The exact online string matching problem: A review of the most recent results," ACM Computing Surveys (CSUR), vol. 45, no. 2, p. 13, Feb. 2013.

Digital Library

[8]

Z. Galil, "Optimal parallel algorithms for string matching," Information and Control, vol. 67, no. 1--3, pp. 144--157, Oct.--Dec. 1985.

Digital Library

[9]

U. Vishkin, "Optimal parallel pattern matching in strings," Information and Control, vol. 67, no. 1--3, pp. 91--113, Oct.--Dec. 1985.

Digital Library

[10]

X. Zha and S. Sahni, "GPU-to-GPU and host-to-host multipattern string matching on a GPU," IEEE Transactions on Computers, vol. 62, no. 6, pp. 1156--1169, Jun. 2013.

Digital Library

[11]

E. Seamans and T. Alexander, "Fast virus signature matching on the GPU," in GPU Gems 3, H. Nguyen, Ed. Addison-Wesley, Aug. 2007, ch. 35, pp. 771--783.

[12]

C. S. Kouzinopoulos and K. G. Margaritis, "String matching on a multicore GPU using CUDA," in ph13th Panhellenic Conference on Informatics, ser. PCI '09. IEEE, Sep. 2009, pp. 14--18.

Digital Library

[13]

J. Nickolls, I. Buck, M. Garland, and K. Skadron, "Scalable parallel programming with CUDA," ACM Queue, pp. 40--53, Mar. Apr. 2008.

Digital Library

[14]

G. E. Blelloch, "Prefix sums and their applications," School of Computer Science, Carnegie Mellon University, Tech. Rep. Carnegie Mellon University-CS-90--190, Nov. 1990.

[15]

N. Bell and J. Hoberock, "Thrust: A productivity-oriented library for CUDA," in GPU Computing Gems, W. W. Hwu, Ed. Morgan Kaufmann, Oct. 2011, vol. 2, ch. 26, pp. 359--371.

[16]

NVIDIA Corporation, "NVIDIA CUDA C programming guide," Sep. 2015, pG-02829-001_v7.5.

[17]

D. Merrill, "CUB," 2011. {Online}. Available: nvlabs.github.io/cub/

[18]

S. Faro and T. Lecroq, "Smart: a string matching algorithm research tool," 2011. {Online}. Available: http://www.dmi.unict.it/faro/smart/

Cited By

NUNES LBORDIM JITO YNAKANO K(2020)A Rabin-Karp Implementation for Handling Multiple Pattern-Matching on the GPUIEICE Transactions on Information and Systems10.1587/transinf.2020PAP0002E103.D:12(2412-2420)Online publication date: 1-Dec-2020
https://doi.org/10.1587/transinf.2020PAP0002
Baig MSivakumar SNayak S(2020)Optimizing Performance of Text Searching Using CPU and GPUsProgress in Computing, Analytics and Networking10.1007/978-981-15-2414-1_15(141-150)Online publication date: 27-Mar-2020
https://doi.org/10.1007/978-981-15-2414-1_15
Lai WWu CLai LSie M(2019)Two-Phase PFAC Algorithm for Multiple Patterns Matching on CUDA GPUsElectronics10.3390/electronics80302708:3(270)Online publication date: 1-Mar-2019
https://doi.org/10.3390/electronics8030270
Show More Cited By

Index Terms

Parallel Approaches to the String Matching Problem on the GPU
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Divide and conquer
    2. Parallel algorithms

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cover image ACM Conferences

SPAA '16: Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures

July 2016

492 pages

ISBN:9781450342100

DOI:10.1145/2935764

General Chair:
Christian Scheideler
University of Paderborn
,
Program Chair:
Seth Gilbert
National University of Singapore

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 11 July 2016

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Sandia LDRD
UC Lab Fees Research Program Award
National Science Foundation

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SPAA '16

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SPAA '16: 28th ACM Symposium on Parallelism in Algorithms and Architectures

July 11 - 13, 2016

California, Pacific Grove, USA

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Cited By

NUNES LBORDIM JITO YNAKANO K(2020)A Rabin-Karp Implementation for Handling Multiple Pattern-Matching on the GPUIEICE Transactions on Information and Systems10.1587/transinf.2020PAP0002E103.D:12(2412-2420)Online publication date: 1-Dec-2020
https://doi.org/10.1587/transinf.2020PAP0002
Baig MSivakumar SNayak S(2020)Optimizing Performance of Text Searching Using CPU and GPUsProgress in Computing, Analytics and Networking10.1007/978-981-15-2414-1_15(141-150)Online publication date: 27-Mar-2020
https://doi.org/10.1007/978-981-15-2414-1_15
Lai WWu CLai LSie M(2019)Two-Phase PFAC Algorithm for Multiple Patterns Matching on CUDA GPUsElectronics10.3390/electronics80302708:3(270)Online publication date: 1-Mar-2019
https://doi.org/10.3390/electronics8030270
Velea RDrăgan ŞGurzău F(2018)CPU/GPU Hybrid Detection for Malware Signatures for Battery-Powered Devices Using OpenCLRecent Trends in Computer Applications10.1007/978-3-319-89914-5_9(139-152)Online publication date: 20-Nov-2018
https://doi.org/10.1007/978-3-319-89914-5_9
Velea RDragan S(2017)CPU/GPU Hybrid Detection for Malware Signatures2017 International Conference on Computer and Applications (ICCA)10.1109/COMAPP.2017.8079736(85-89)Online publication date: Sep-2017
https://doi.org/10.1109/COMAPP.2017.8079736

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