CUSMART: effective parallelization of string matching algorithms using GPGPU accelerators

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...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering Vol. 26; no. 6; pp. 877 - 895
Main Authors: Ozsoy, Adnan, Nazli, Mengu, Cankur, Onur, Sahin, Cagri
Format: Journal Article
Language:English
Published: Hangzhou Zhejiang University Press 01.06.2025
Springer Nature B.V
Subjects:
Algorithms
Application programming interface
Communications Engineering
Computation
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Electrical Engineering
Electronics and Microelectronics
Graphics processing units
Instrumentation
Networks
Optimization techniques
Parallel processing
Research Article
String matching
NVIDIA
统一计算设备架构(CUDA)
String matching
图形处理器(GPU)编程
String matching algorithms research tool (SMART)
并行编程
TP391.4
通用图形处理器(GPGPU)
英伟达(NVDIA)
Parallel programming
Compute unified device architecture (CUDA)
字符串匹配算法研究工具(SMART)
Graphics processing unit (GPU) programming
General-purpose computing on GPU (GPGPU)
字符串匹配
ISSN:2095-9184, 2095-9230
Online Access:Get full text
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Summary: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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ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.2400091