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 gra...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.展开更多
String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that...String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.展开更多
基金Project supported by the Scientific and Technological Research Council of Türkiye(No.117E142)Open access funding provided by the Scientific and Technological Research Council of Türkiye(TÜBİTAK)。
文摘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.
文摘String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.
