JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

CUSMART:effective parallelization of string-matching algorithms using GPGPU accelerators

Author(s): Adnan OZSOY, Mengu NAZLI, Onur CANKUR, Cagri SAHIN
Affiliation(s): Department of Computer Engineering, Hacettepe University, Ankara, Turkiye; more
Corresponding email(s): adnan.ozsoy@hacettepe.edu.tr, mengu@hacettepe.edu.tr, ocankur@umd.edu, cagrisahin@gazi.edu.tr
Key Words: String matching; Parallel programming; GPU programming; GPGPU; Nvidia; CUDA; SMART

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Adnan OZSOY, Mengu NAZLI, Onur CANKUR, Cagri SAHIN. CUSMART:effective parallelization of string-matching algorithms using GPGPU accelerators[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400091

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author="Adnan OZSOY, Mengu NAZLI, Onur CANKUR, Cagri SAHIN",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400091"
}

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%A Onur CANKUR
%A Cagri SAHIN
%J Frontiers of Information Technology & Electronic Engineering
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doi="https://doi.org/10.1631/FITEE.2400091"

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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: This study presents a parallel version of the String Matching Algorithms Research Tool (SMART) library, implemented on Nvidia’s Compute Unified Device Architecture (CUDA) platform, and utilizes general purpose graphics processing unit (GPGPU) programming concepts to enhance performance and gain insight on the parallel versions of these algorithms. We have developed the CUDA-enhanced SMART (CUSMART) library, which incorporates parallelized iterations of 64 string matching algorithms, leveraging the CUDA application programming interface (API). The performance of these algorithms has been assessed across various scenarios to ensure a comprehensive and impartial comparison, allowing for the identification of their strengths and weaknesses in specific application contexts. We have explored and established optimization techniques to gauge their influence on the performance of these algorithms. The results of this study show that the potential highlight of GPGPU computing in string matching applications is highlighted by the scalability of algorithms, suggesting significant performance improvements. Furthermore, we have identified the best and worst performing algorithms in various scenarios.

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