Improved Parallel Rabin-Karp Algorithm Using Compute Unified Device Architecture

TL;DR

This paper presents a modified parallel Rabin-Karp string matching algorithm optimized for GPU execution, demonstrating significant speed improvements over CPU implementations through extensive performance comparisons.

Contribution

It introduces a novel GPU-accelerated parallel Rabin-Karp algorithm, enhancing string matching efficiency by leveraging GPU's high parallel processing capabilities.

Findings

GPU implementation outperforms CPU in speed

Performance varies with number of threads and cores

Effective for large file and pattern sizes

Abstract

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.