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Network-Aware Grouping in Distributed Stream Processing Systems Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Accelerating Pattern Matching with CPU-GPU Collaborative Computing

Authors : Victoria Sanz, Adrián Pousa, Marcelo Naiouf, Armando De Giusti

Published in: Algorithms and Architectures for Parallel Processing

Publisher: Springer International Publishing

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Abstract

Pattern matching algorithms are used in several areas such as network security, bioinformatics and text mining. In order to support large data and pattern sets, these algorithms have to be adapted to take advantage of the computing power of emerging parallel architectures. In this paper, we present a parallel algorithm for pattern matching on CPU-GPU heterogeneous systems, which is based on the Parallel Failureless Aho-Corasick algorithm (PFAC) for GPU. We evaluate the performance of the proposed algorithm on a machine with 36 CPU cores and 1 GPU, using data and pattern sets of different size, and compare it with that of PFAC for GPU and the multithreaded version of PFAC for shared-memory machines. The results reveal that our proposal achieves higher performance than the other two approaches for data sets of considerable size, since it uses both CPU and GPU cores.

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Footnotes
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.
 
2
Load balance [14] can be defined as the ratio between the average time to finish all of the parallel tasks \(T_{avg}\) and the maximum time to finish any of the parallel tasks \(T_{max}\).
 
Literature
1.
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)
2.
3.
4.
Tumeo, A., et al.: Efficient pattern matching on GPUs for intrusion detection systems (2010)
5.
Aho, A.V., Corasick, M.J.: Efficient string matching: an aid to bibliographic search. Commun. ACM. 18(6), 333–340 (1975)MathSciNetCrossRef
6.
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)CrossRef
7.
Lin, C.H., et al.: Accelerating pattern matching using a novel parallel algorithm on GPUs. IEEE Trans. Comput. 62(10), 1906–1916 (2013)MathSciNetCrossRef
8.
Arudchutha S., et al.: String matching with multicore CPUs: Performing better with the Aho-Corasick algorithm. In: 2013 IEEE 8th International Conference on Industrial and Information Systems, pp. 231–236. IEEE Computer Society, Washington D. C. (2013)
9.
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)
10.
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)
11.
Mittal, S., Vetter, J.: A survey of CPU-GPU heterogeneous computing techniques. ACM Comput. Surv. 47(4), 69:1–69:35 (2015)CrossRef
12.
Wan, L., et al.: Efficient CPU-GPU cooperative computing for solving the subset-sum problem. Concurr. Comput.: Pract. Exp. 28(2), 185–186 (2016)CrossRef
13.
14.
Rahman, R.: Intel Xeon Phi Coprocessor Architecture and Tools: The Guide for Application Developers. Apress, Berkeley (2013)CrossRef
Metadata
Title
Accelerating Pattern Matching with CPU-GPU Collaborative Computing
Authors
Victoria Sanz
Adrián Pousa
Marcelo Naiouf
Armando De Giusti
Copyright Year
2018
Book
Algorithms and Architectures for Parallel Processing

Print ISBN: 978-3-030-05050-4

Electronic ISBN: 978-3-030-05051-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-05051-1_22

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