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Aho-Corasick (AC) algorithm is a multiple patterns string matching algorithm commonly used in computer and network security and bioinformatics, among many others. In order to meet the highly demanding computational requirements imposed on these applications, achieving high performance for the AC algorithm is crucial. In this paper, we present a hig...
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... The parallelization of the shared memory is used in various applications of computers such as the classification of pictures [21], Restoration of the image [22], direct polynomial math [23], information mining [24], bioinformatics [25], Intrusion detection system (IDS) [26], [27], [28]. ...
The string matching problem is considered one of the substantial problems in the fields of computer science like speech and pattern recognition, signal and image processing, and artificial intelligence (AI). The increase in the speedup of performance is considered an important factor in meeting the growth rate of databases, Subsequently, one of the determinations to address this issue is the parallelization for exact string matching algorithms. In this study, the E-Abdulrazzaq string matching algorithm is chosen to be executed with the multi-core environment utilizing the OpenMP paradigm which can be utilized to decrease the execution time and increase the speedup of the algorithm. The parallelization algorithm got positive results within the parallel execution time, and excellent speeding-up capabilities, in comparison to the successive result. The Protein database showed optimal results in parallel execution time, and when utilizing short and long pattern lengths. The DNA database showed optimal speedup execution when utilizing short and long pattern lengths, while no specific database obtained the worst results.
... Parallel is characterized as the necessity in finding instructions, chain of instructions, or certain parts of the code that are executed with diverse cores or processors at the same time [1]. The parallelization of shared memory is utilized in various computer applications such as intrusion detection systems (IDS) [3], [4] linear algebra [5], string matching algorithms [6] bioinformatics [7], data mining [8], image classification [9], and Therapeutic picture preparing [10]. ...
String matching is considered as one of the center issues within the field of computer science, where there are numerous computer applications that supply the clients with string matching services. The increment within the number of databases which are created and protected in numerous computer gadgets had impacted researchers with the slant towards getting robust techniques in tending to this issue. In this study, the Maximum-Shift string matching algorithm is chosen to be executed with multi-core innovation through the utilization of OpenMP paradigm, in order to decrease the successive time, and increment the speedup and efficiency of the algorithm. The deoxyribonucleic acid (DNA), protein and the English text datasets are utilized to test the parallel execution that influences the Maximum-Shift algorithm execution when utilized with multi-core environment. The results demonstrated that the execution is affected by the performance between the parallel and consecutive execution of Maximum-Shift algorithm by data type. The English text appeared ideal comes about within the parallel execution time as compared to other datasets, whereas the DNA database set appeared the most elevated comes about when compared to other data types in terms of speedup and efficiency capabilities.
... Algorithms 2017, 10, 58 2 of 13 market [17]. In contrast, software-based algorithms utilize central processing units (CPUs) or graphical processing units (GPUs) characterized by high flexibility and programmability [18][19][20][21][22][23][24][25][26][27]. We therefore focused our efforts on designing a pattern-matching algorithm for software-based NIDSs. ...
As part of network security processes, network intrusion detection systems (NIDSs) determine whether incoming packets contain malicious patterns. Pattern matching, the key NIDS component, consumes large amounts of execution time. One of several trends involving general-purpose processors (GPPs) is their use in software-based NIDSs. In this paper, we describe our proposal for an efficient and flexible pattern-matching algorithm for inspecting packet payloads using a head-body finite automaton (HBFA). The proposed algorithm takes advantage of multi-core GPP parallelism and single-instruction multiple-data operations to achieve higher throughput compared to that resulting from traditional deterministic finite automata (DFA) using the Aho-Corasick algorithm. Whereas the head-body matching (HBM) algorithm is based on pre-defined DFA depth value, our HBFA algorithm is based on head size. Experimental results using Snort and ClamAV pattern sets indicate that the proposed algorithm achieves up to 58% higher throughput compared to its HBM counterpart.
Pattern matching algorithms are used in several areas such as network security, bioinformatics and text mining. In order to provide real-time response for large inputs, high-performance systems should be used. However, this requires adapting the algorithm to the underlying architecture. Intel Xeon Phi processors have attracted attention in recent years because they offer massive parallelism, good programmability and portability. In this paper, we present a pattern matching algorithm that exploits the full computational power of Intel Xeon Phi processors by using both SIMD and thread parallelism. We evaluate our algorithm on a Xeon Phi 7230 Knights Landing processor and measure its performance as the data size and the number of threads increase. The results reveal that both parallelism methods provide performance gains. Also they indicate that our algorithm is up to 63x faster than its serial counterpart and behaves well as the workload is increased.
