Parallel Quick Search Algorithm for the Exact String Matching Problem Using OpenMP

Abstract

String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.

Parallel implementation of maximum-shift algorithm using OpenMp

“…It entails the multicore implementation used on the Maximum-Shift algorithm and the performing of code by using the directives and the library functions of the OpenMP paradigm. Depending on the analysis of the Maximum-Shift algorithm in the previous part, the costly part in the string matching algorithm is in the checking of whether the character of the text window matches the character in the pattern [6]. To reduce the cost of the searching phase that includes the matching operation of the characters in the text window and the pattern, the parallelization is therefore utilized depending on the OpenMP model.…”

“…The parallelization algorithm has to select the parts of parallel that contains it, and it makes the appropriateness between the speed of 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].…”

Parallel processing of E-Atheer algorithm using pthread paradigm

“…Typically, String Matching is usually utilized in numerous computer applications such as signal and image processing, artificial intelligence (AI), web search engines, intrusion detection systems, operating systems, speech and pattern recognition [2], [3], information retrieval [4], and computational biology and chemistry. Furthermore, in recent years, the string matching algorithms are considered as the main component utilized in the application of DNA pattern matching, and the analysis of Protein sequences [5], [6]. The development and growth rate of the database is escalating at a swift rate; thus the need for enhancing the performance of exact string matching algorithms.…”

“…The parallel processing is defined as the practical way to reduce the computational time, which is dependent upon dealing with the cores or processors in computers to resolve the sequential computing problems [5], [11], [12]. Prevalently, in tandem with the increase in the enhanced development of computer technology, particularly in the architectures of the processors and multicores, there exist pertinent necessity for enhancing the performance of the exact string matching algorithms.…”

Fast Hybrid String Matching Algorithm based on the Quick-Skip and Tuned Boyer-Moore Algorithms

“…Basically, the string matching algorithm accepts two inputs, namely, a short string called a pattern and a long string called a text. The pattern string is usually compared with the text string to determine if the former is a substring of the latter [1], [2]. Although many algorithms and strategies have been developed to solve this problem, scientists still attempt to develop far more efficient methods.…”