Improved Parallel Rabin-Karp Algorithm Using Compute Unified Device Architecture

String matching algorithms are among one of the most widely used algorithms in computer science. Traditional string matching algorithms efficiency of underlaying string matching algorithm will greatly increase the efficiency of any application. In recent years, Graphics processing units are emerged as highly parallel processor. They out perform best of the central processing units in scientific computation power. By combining recent advancement in graphics processing units with string matching algorithms will allows to speed up process of string matching. In this paper we proposed modified parallel version of Rabin-Karp algorithm using graphics processing unit. Based on that, result of CPU as well as parallel GPU implementations are compared for evaluating effect of varying number of threads, cores, file size as well as pattern size.Comment: Information and Communication Technology for Intelligent Systems (ICTIS 2017

A Frame Work for Parallel String Matching- A Computational Approach with Omega Model

Now a day2019;s parallel string matching problem is attracted by so many researchers because of the importance in information retrieval systems. While it is very easily stated and many of the simple algorithms perform very well in practice, numerous works have been published on the subject and research is still very active. In this paper we propose a omega parallel computing model for parallel string matching. Experimental results show that, on a multi-processor system, the omega model implementation of the proposed parallel string matching algorithm can reduce string matching time by more than 40%

String Matching Problems with Parallel Approaches An Evaluation for the Most Recent Studies

In recent years string matching plays a functional role in many application like information retrieval, gene analysis, pattern recognition, linguistics, bioinformatics etc. For understanding the functional requirements of string matching algorithms, we surveyed the real time parallel string matching patterns to handle the current trends. Primarily, in this paper, we focus on present developments of parallel string matching, and the central ideas of the algorithms and their complexities. We present the performance of the different algorithms and their effectiveness. Finally this analysis helps the researchers to develop the better techniques

A Parallel Computational Approach for String Matching- A Novel Structure with Omega Model

In r e cent day2019;s parallel string matching problem catch the attention of so many researchers because of the importance in different applications like IRS, Genome sequence, data cleaning etc.,. While it is very easily stated and many of the simple algorithms perform very well in practice, numerous works have been published on the subject and research is still very active. In this paper we propose a omega parallel computing model for parallel string matching. The algorithm is designed to work on omega model pa rallel architecture where text is divided for parallel processing and special searching at division point is required for consistent and complete searching. This algorithm reduces the number of comparisons and parallelization improves the time efficiency. Experimental results show that, on a multi - processor system, the omega model implementation of the proposed parallel string matching algorithm can reduce string matching time

A New Multi-threaded and Interleaving Approach to Enhance String Matching for Intrusion Detection Systems

String matching algorithms are computationally intensive operations in computer science. The algorithms find the occurrences of one or more strings patterns in a larger string or text. String matching algorithms are important for network security, biomedical applications, Web search, and social networks. Nowadays, the high network speeds and large storage capacity put a high requirement on string matching methods to perform the task in a short time. Traditionally, Aho-Corasick algorithm, which is used to find the string matches, is executed sequentially. In this paper, a new multi-threaded and interleaving approach of Aho-Corasick using graphics processing units (GPUs) is designed and implemented to achieve high-speed string matching. Compute Unified Device Architecture (CUDA) programming language is used to implement the proposed parallel version. Experimental results show that our approach achieves more than 5X speedup over the sequential and other parallel implementations. Hence, a wide range of applications can benefit from our solution to perform string matching faster than ever before

Data structures and algorithms for approximate string matching Zvi Galil, Raffaele Giancarlo

This paper surveys techniques for designing efficient sequential and parallel approximate string matching algorithms. Special attention is given to the methods for the construction of data structures that efficiently support primitive operations needed in approximate string matching

Towards optimal packed string matching

a r t i c l e i n f o a b s t r a c t Dedicated to Professor Gad M. Landau, on the occasion of his 60th birthday Keywords: String matching Word-RAM Packed strings In the packed string matching problem, it is assumed that each machine word can accommodate up to α characters, thus an n-character string occupies n/α memory words. The main word-size string-matching instruction wssm is available in contemporary commodity processors. The other word-size maximum-suffix instruction wslm is only required during the pattern pre-processing. Benchmarks show that our solution can be efficiently implemented, unlike some prior theoretical packed string matching work. (b) We also consider the complexity of the packed string matching problem in the classical word-RAM model in the absence of the specialized micro-level instructions wssm and wslm. We propose micro-level algorithms for the theoretically efficient emulation using parallel algorithms techniques to emulate wssm and using the Four-Russians technique to emulate wslm. Surprisingly, our bit-parallel emulation of wssm also leads to a new simplified parallel random access machine string-matching algorithm. As a byproduct to facilitate our results we develop a new algorithm for finding the leftmost (most significant) 1 bits in consecutive non-overlapping blocks of uniform size inside a word. This latter problem is not known to be reducible to finding the rightmost 1, which can be easily solved, since we do not know how to reverse the bits of a word in O (1) time

Parallel String Matching with Multi Core Processors-A Comparative Study for Gene Sequences

The increase in huge amount of data is seen clearly in present days because of requirement for storing more information. To extract certain data from this large database is a very difficult task, including text processing, information retrieval, text mining, pattern recognition and DNA sequencing. So we need concurrent events and high performance computing models for extracting the data. This will create a challenge to the researchers. One of the solutions is parallel algorithms for string matching on computing models. In this we implemented parallel string matching with JAVA Multi threading with multi core processing, and performed a comparative study on Knuth Morris Pratt, Boyer Moore and Brute force string matching algorithms. For testing our system we take a gene sequence which consists of lacks of records. From the test results it is shown that the multicore processing is better compared to lower versions. Finally this proposed parallel string matching with multicore processing is better compared to other sequential approaches

Parallel string matching with k mismatches

AbstractTwo improved algorithms for string matching with k mismatches are presented. One algorithm is based on fast integer multiplication algorithms whereas the other follows more closely classic string-matching techniques