Abstract
Pattern matching algorithms are used in several areas such as network security, bioinformatics and text mining, where the volume of data is growing rapidly. In order to provide real-time response for large inputs, high-performance computing should be considered. In this paper, we present a novel hybrid pattern matching algorithm that efficiently exploits the computing power of a heterogeneous system composed of multicore processors and multiple graphics processing units (GPUs). We evaluate the performance of our algorithm on a machine with 36 CPU cores and 2 GPUs and study its behaviour as the data size and the number of processing resources increase. Finally, we compare the performance of our proposal with that of two other algorithms that use only the CPU cores and only the GPUs of the system respectively. The results reveal that our proposal outperforms the other approaches for data sets of considerable size.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Speedup is defined as \(\frac{T_{s}}{T_{p}}\), where \(T_{s}\) is the execution time of the sequential algorithm and \(T_{p}\) is the execution time of the parallel algorithm.
References
Tumeo, A., Villa, O.: Accelerating DNA analysis applications on GPU clusters. In: IEEE 8th Symposium on Application Specific Processors (SASP), pp. 71–76. IEEE Computer Society, Washington D.C. (2010)
Clamav. http://www.clamav.net
Norton, M.: Optimizing pattern matching for intrusion detection. Sourcefire Inc., White Paper. https://www.snort.org/documents/optimization-of-pattern-matches-for-ids
Tumeo, A., et al.: Efficient pattern matching on GPUs for intrusion detection systems. In: Proceedings of the 7th ACM International Conference on Computing Frontiers, pp. 87–88. ACM, New York (2010)
Aho, A.V., Corasick, M.J.: Efficient string matching: an aid to bibliographic search. Commun. ACM 18(6), 333–340 (1975)
Tumeo, A., et al.: Aho-Corasick string matching on shared and distributed-memory parallel architectures. IEEE Trans. Parallel Distrib. Syst. 23(3), 436–443 (2012)
Lin, C.H., et al.: Accelerating pattern matching using a novel parallel algorithm on GPUs. IEEE Trans. Comput. 62(10), 1906–1916 (2013)
Arudchutha, S., et al.: String matching with multicore CPUs: performing better with the Aho-Corasick algorithm. In: Proceedings of the IEEE 8th International Conference on Industrial and Information Systems, pp. 231–236. IEEE Computer Society, Washington D.C. (2013)
Herath, D., et al.: Accelerating string matching for bio-computing applications on multi-core CPUs. In: Proceedings of the IEEE 7th International Conference on Industrial and Information Systems (ICIIS), pp. 1–6. IEEE Computer Society, Washington D.C. (2012)
Lin, C.H., et al.: A novel hierarchical parallelism for accelerating NIDS using GPUs. In: Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 578–581. IEEE (2018)
Soroushnia, S., et al.: Heterogeneous parallelization of Aho-Corasick algorithm. In: Proceedings of the IEEE 7th International Conference on Industrial and Information Systems (ICIIS), pp. 1–6. IEEE Computer Society, Washington D.C. (2012)
Lee, C.L., et al.: A hybrid CPU/GPU pattern-matching algorithm for deep packet inspection. PLoS One 10(10), 1–22 (2015)
Sanz, V., Pousa, A., Naiouf, M., De Giusti, A.: Accelerating pattern matching with CPU-GPU collaborative computing. In: Vaidya, J., Li, J. (eds.) ICA3PP 2018. LNCS, vol. 11334, pp. 310–322. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05051-1_22
Wan, L., et al.: Efficient CPU-GPU cooperative computing for solving the subset-sum problem. Concurr. Comput. Pract. Exp. 28(2), 185–186 (2016)
The British National Corpus, version 3 (BNC XML Edition). Distributed by Bodleian Libraries, University of Oxford, on behalf of the BNC Consortium (2007). http://www.natcorp.ox.ac.uk/
Rahman, R.: Intel Xeon Phi Coprocessor Architecture and Tools: The Guide for Application Developers. Apress, Berkeley (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sanz, V., Pousa, A., Naiouf, M., De Giusti, A. (2020). Efficient Pattern Matching on CPU-GPU Heterogeneous Systems. In: Wen, S., Zomaya, A., Yang, L. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11944. Springer, Cham. https://doi.org/10.1007/978-3-030-38991-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-38991-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38990-1
Online ISBN: 978-3-030-38991-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)