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.
Highlights
String matching algorithms are an important class of string algorithms that try to find a place where one or several strings are found within a larger string or text
The main question is “How to reduce execution time of the Quick Search string matching algorithm by using OpenMP parallel method?” the sub question of the main question is “How to prove the performance improvement of the parallel version of the Quick Search string matching algorithm compared with its performance of the sequential version of the Quick Search string matching algorithm?” the objective of this paper is to investigate the suitability of parallelizing the Quick Search algorithm on multi-core environment using OpenMP
In order to examine the performance of parallel algorithm, a standard benchmark data is used which is represented the common used of string matching algorithm, which are English text, Proteins sequence and DNA sequence
Summary
String matching algorithms are an important class of string algorithms that try to find a place where one or several strings ( called patterns) are found within a larger string or text. The fundamental string matching problem is defined as follows: given two strings a text and a pattern, determine whether the pattern appears in the text [1]. String matching algorithms are applied in many computer applications, such as data processing, image and voice recognition, information retrieval, computational biology. String matching algorithms have become a significant component of applications which are used to search nucleotide or amino acid sequence patterns in biological sequence databases in recent years [3]. The performance of the string matching algorithms plays a prominent role in the performance of these computer applications [4]. This research concentrates on the problems which are related to the performance of the Quick Search string matching algorithm. The main question is “How to reduce execution time of the Quick Search string matching algorithm by using OpenMP parallel method?” the sub question of the main question is “How to prove the performance improvement of the parallel version of the Quick Search string matching algorithm compared with its performance of the sequential version of the Quick Search string matching algorithm?” the objective of this paper is to investigate the suitability of parallelizing the Quick Search algorithm on multi-core environment using OpenMP
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
