A bit-parallel algorithm for searching multiple patterns with various lengths

Kusudo, Ko; Ino, Fumihiko; Hagihara, Kenichi

doi:10.1016/j.jpdc.2014.11.003

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…In the first category, Knuth-Morris-Pratt [13] and Aho-Corasick (AC) [14] algorithms have been widely used for exact string matching. Meanwhile, the typical nondeterministic approaches namely the bit-parallel algorithms are used for both approximate and exact string matching [15]. The number of memory references is defined only by the text and pattern lengths in the bit-parallel algorithms.…”

Section: String Similarities In Large Datasetsmentioning

confidence: 99%

“…The Jaro-Winkler approach was chosen for string similarity evaluation considering the precise quantification of string similarities compared with approximate and exact string-matching algorithms and its comparable accuracy performances with ML algorithms [21]. As underlined in [15], GPU constraints regarding the data type and their fixed length have to be met, in spite of the variable string lengths for which the similarity evaluation needs to be performed on. Thus, the proposed implementation complies with the previous constraints, delivering increased throughput compared with state of the art approaches.…”

Section: String Similarities In Musical Industrymentioning

confidence: 99%

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GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

Baloi

Belean

Turcu

et al. 2022

Preprint

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The digital era brings up on one hand massive amounts of available data, and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, bio-medical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also evaluated in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of massmedia broadcast materials. Thus, the present paper proposes a GPU based approach for parallel implementation of the Jaro-Winkler string similarity metric computation. Further on, a thresholding-based algorithm is also implemented using GPU for matching records over large datasets. The global GPU RAM memory is used to store multiple string lines as raw data. In the case of a single string, its comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Moreover, based on the computed similarity metrics, an adaptive neural network approach guided by a novelty detection classifier together with a naive neural network implementation are proposed to increase the accuracy of the records matching procedure. Timing considerations and the computational complexity are detailed for the proposed approaches compared with state-of-the-art CPU and GPU approaches. A speed-up factor of 21.6 was obtained for the GPU based JaroWinkler implementation compared with the general purpose processor one, whereas improved accuracy for the records matching procedure was delivered using machine learning approaches.

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Section: String Similarities In Large Datasetsmentioning

confidence: 99%

Section: String Similarities In Musical Industrymentioning

confidence: 99%

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

Baloi

Belean

Turcu

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Pattern matching can be divided into two categories, i.e., single and multiple. The algorithms used for processing single patterns are as follows: the brute force [9,15], Rabin-Karp [25], Knuth-Morris-Pratt [26], Boyer-Moore [27], two-way string matching [28], and bit parallel algorithms [29][30][31][32]. On the other hand, the most widely used algorithm for processing multiple patterns is the AC algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Although the bit-parallel algorithm only matches a single pattern, Prasad et al [36] extended the bit-parallel algorithm to search for multiple patterns with a same length in text simultaneously. Kusudo et al [31] further extended the bit-parallel algorithm to match multiple In addition to the AC algorithm, there are other types of algorithms for multiple-patterns matching. Although the bit-parallel algorithm only matches a single pattern, Prasad et al [36] extended the bit-parallel algorithm to search for multiple patterns with a same length in text simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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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Enhancing Redundant Content Elimination Algorithm Using Processing Power of Multi-Core Architecture

Saxena

Jain

2020

Advances in Intelligent Systems and Computing

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A bit-parallel algorithm for searching multiple patterns with various lengths

Cited by 7 publications

References 34 publications

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

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

Enhancing Redundant Content Elimination Algorithm Using Processing Power of Multi-Core Architecture

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