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CLC number: TP391.4
On-line Access: 2025-07-02
Received: 2024-02-06
Revision Accepted: 2025-07-02
Crosschecked: 2024-07-23
Cited: 0
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CUSMART: effective parallelization of string matching algorithms using GPGPU accelerators
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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, 2025, 26(6): 877-895.
@article{title="CUSMART: effective parallelization of string matching algorithms using GPGPU accelerators",
author="Adnan OZSOY, Mengu NAZLI, Onur CANKUR, Cagri SAHIN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="26",
number="6",
pages="877-895",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400091"
}
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%T CUSMART: effective parallelization of string matching algorithms using GPGPU accelerators
%A Adnan OZSOY
%A Mengu NAZLI
%A Onur CANKUR
%A Cagri SAHIN
%J Frontiers of Information Technology & Electronic Engineering
%V 26
%N 6
%P 877-895
%@ 2095-9184
%D 2025
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400091
TY - JOUR
T1 - CUSMART: effective parallelization of string matching algorithms using GPGPU accelerators
A1 - Adnan OZSOY
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A1 - Onur CANKUR
A1 - Cagri SAHIN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 26
IS - 6
SP - 877
EP - 895
%@ 2095-9184
Y1 - 2025
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2400091
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 uses general-purpose computing on graphics processing unit (GPGPU) programming concepts to enhance performance and gain insight into 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. 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 highlight the potential of GPGPU computing in string matching applications through 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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