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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Vydané v:Frontiers of information technology & electronic engineering Ročník 26; číslo 6; s. 877 - 895
Hlavní autori: Ozsoy, Adnan, Nazli, Mengu, Cankur, Onur, Sahin, Cagri
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hangzhou Zhejiang University Press 01.06.2025
Springer Nature B.V
Predmet:
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
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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.
AbstractList 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.
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. 提出一种字符串匹配算法研究工具(SMART)库的并行版本,该版本在NVDIA的统一计算设备架构(CUDA)平台上实现,采用通用图形处理器(GPGPU)编程概念以提升性能及深入了解这些字符匹配算法的并行版本。利用CUDA应用程序编程接口(API)开发了CUDA增强的SMART(CUSMART)库,该库集成了64种字符串匹配算法的并行迭代。为确保全面且公正的比较,在各种场景下评估这些算法的性能,进而识别它们在特定应用场景中的优势和劣势。探索并建立了优化技术,以评估它们对算法性能的影响。该研究的结果通过算法的可扩展性突出了GPGPU计算在字符串匹配应用中的潜力,表明性能有显著提高。此外,确定了不同场景下表现最佳和最差的算法。
Author Nazli, Mengu
Sahin, Cagri
Cankur, Onur
Ozsoy, Adnan
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Issue 6
Keywords 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
Language English
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Springer Nature B.V
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  doi: 10.1109/TPDS.2016.2645222
– start-page: 1
  volume-title: Int Conf on ICT for Smart Society
  year: 2013
  ident: 91_CR42
  doi: 10.1109/ICTSS.2013.6588052
SSID ssj0001619798
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Snippet This study presents a parallel version of the string matching algorithms research tool (SMART) library, implemented on NVIDIA’s compute unified device...
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SubjectTerms 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
Title CUSMART: effective parallelization of string matching algorithms using GPGPU accelerators
URI https://link.springer.com/article/10.1631/FITEE.2400091
https://www.proquest.com/docview/3226033032
Volume 26
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