Multiple String Matching on a GPU using CUDAs

Kouzinopoulos, C.; Michailidis, Panagiotis D.; Margaritis, Konstantinos G.

doi:10.12694/scpe.v16i2.1085

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…For the PRK, the parameters q = 65521, which is the largest prime number less than 2 16 , and d = 2 were used. In terms of the number τ of threads, step 2 of the PRK uses one CUDA thread for each pattern and compute the values of h(P k ).…”

Section: Resultsmentioning

confidence: 99%

“…The proposed CUDA-based implementation outperformed a quad-core processor providing speedup of up to 14 times. Kouzinopoulos et al [16] evaluated several multiple pattern matching algorithms on the GPU. They reported that even a basic implementation of these algorithms in the GPU were between 2.5 and 10.9 faster than a single core CPU implementation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Rabin-Karp Implementation for Handling Multiple Pattern-Matching on the GPU

Nunes

Bordim

Ito

et al. 2020

IEICE Trans. Inf. & Syst.

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The volume of digital information is growing at an extremely fast pace which, in turn, exacerbates the need of efficient mechanisms to find the presence of a pattern in an input text or a set of input strings. Combining the processing power of Graphics Processing Unit (GPU) with matching algorithms seems a natural alternative to speedup the string-matching process. This work proposes a Parallel Rabin-Karp implementation (PRK) that encompasses a fast-parallel prefix-sums algorithm to maximize parallelization and accelerate the matching verification. Given an input text T of length n and p patterns of length m, the proposed implementation finds all occurrences of p in T in O(m + q + n τ + nm q) time, where q is a sufficiently large prime number and τ is the available number of threads. Sequential and parallel versions of the PRK have been implemented. Experiments have been executed on p ≥ 1 patterns of length m comprising of m = 10, 20, 30 characters which are compared against a text string of length n = 2 27. The results show that the parallel implementation of the PRK algorithm on NVIDIA V100 GPU provides speedup surpassing 372 times when compared to the sequential implementation and speedup of 12.59 times against an OpenMP implementation running on a multi-core server with 128 threads. Compared to another prominent GPU implementation, the PRK implementation attained speedup surpassing 37 times.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Rabin-Karp Implementation for Handling Multiple Pattern-Matching on the GPU

Nunes

Bordim

Ito

et al. 2020

IEICE Trans. Inf. & Syst.

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show abstract

“…On the other hand, the most widely used algorithm for processing multiple patterns is the AC algorithm. In addition to this algorithm, the Commentz-Walter [33] and Wu-Manber (WM) algorithms [34,35] are used to process multiple patterns. The AC algorithm and parallel pattern matching algorithm are explained in the subsequent paragraphs.…”

Section: Related Workmentioning

confidence: 99%

Two-Phase PFAC Algorithm for Multiple Patterns Matching on CUDA GPUs

Lai

et al. 2019

Electronics

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The rapid advancement of high speed networks has resulted in a significantly increasing number of network packets per second nowadays, implying network intrusion detection systems (NIDSs) need to accelerate the inspection of packet content to protect the computer systems from attacks. On average, the pattern matching process in a NIDS consumes approximately 70% of the overall processing time. The conventional Aho–Corasick (AC) algorithm, adopting a finite state machine to identify attack patterns in NIDSs, is too slow to meet the requirement of high speed networks. In view of this, several studies have used the features of a graphics processing unit (GPU) to improve the core searching process of the AC algorithm. For instance, parallel failureless Aho-Corasick (PFAC) algorithm improves the process of pattern matching effectively by removing backward branches in the original finite state machine created using the AC algorithm. In this way, boundary detection can be avoided totally if we allocate an individual thread to each byte of an input stream to identify any pattern starting at the thread’s starting position. However, through analysis, we found that this algorithm experiences a serious load imbalance problem. Therefore, this paper proposes a two-phase PFAC algorithm to address the problem. A threshold is predefined to divide execution into two phases, and the failureless finite state machine is also decoupled into two parts accordingly. In the first phase, every thread identifies patterns by running the tiny part of the decoupled failureless finite state machine that are stored in fast shared memory. In the second phase, all the threads requiring further searching in a same block are regrouped into a few warps for less branch divergence. According to experimental results, the proposed algorithm shows a performance improvement of 50% compared to the PFAC algorithm.

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“…In 2015, Kouzinopoulos [23] did an experiment using a GTX 280 GPU and a Xeon 2.4GHz CPU. They tested the Aho-Corasick, Set Horspool, Set Backward Oracle Matching, Wu-Manber and SOG multiple pattern matching algorithms showing the basic implementation of these algorithm in GPU were between 2.5 and 10.9 faster than the CPU implementation.…”

Section: Improvements To String Matching In Gpu Using Cudamentioning

confidence: 99%