TY - GEN
T1 - Parallel Subsequence Generation of a String Using MPI and CUDA
AU - Kotian, Akash S.
AU - Niketh, Kumar B.
AU - Kini, N. Gopalakrishna
AU - Ashwath, Rao B.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Subsequence generation of a string is an important task in various disciplines of Computer Science and Bio-informatics. Speeding is desirable in subsequence generation from a string and is essential for several applications. The most effective way of extracting substring from a string is by parallel processing principles. This paper uses Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) techniques to investigate the parallelization of subsequence extraction from strings. In this study, for data distribution and coordination, MPI and CUDA are used in the best possible way. A novel parallel approach involving data segmentation, load balancing, and Graphical Processing Unit (GPU) acceleration as part of the design is addressed. The scalability and efficiency of the approach are demonstrated by the experimental findings on CPU-GPU system, which show significant speedup and better performance. Through quicker parallelized string data analysis, this work tackles the need for high-performance computing in subsequence analysis and offers potential benefits for data analytics, natural language processing, and genomics.
AB - Subsequence generation of a string is an important task in various disciplines of Computer Science and Bio-informatics. Speeding is desirable in subsequence generation from a string and is essential for several applications. The most effective way of extracting substring from a string is by parallel processing principles. This paper uses Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) techniques to investigate the parallelization of subsequence extraction from strings. In this study, for data distribution and coordination, MPI and CUDA are used in the best possible way. A novel parallel approach involving data segmentation, load balancing, and Graphical Processing Unit (GPU) acceleration as part of the design is addressed. The scalability and efficiency of the approach are demonstrated by the experimental findings on CPU-GPU system, which show significant speedup and better performance. Through quicker parallelized string data analysis, this work tackles the need for high-performance computing in subsequence analysis and offers potential benefits for data analytics, natural language processing, and genomics.
UR - https://www.scopus.com/pages/publications/105028330497
UR - https://www.scopus.com/pages/publications/105028330497#tab=citedBy
U2 - 10.1007/978-981-96-8799-2_9
DO - 10.1007/978-981-96-8799-2_9
M3 - Conference contribution
AN - SCOPUS:105028330497
SN - 9789819687985
T3 - Lecture Notes in Networks and Systems
SP - 113
EP - 120
BT - Machine Intelligence for Research and Innovations - Proceedings of MAiTRI 2024
A2 - Verma, Om Prakash
A2 - Wang, Lipo
A2 - Kumar, Rajesh
A2 - Yadav, Anupam
A2 - Rout, Ranjeet Kumar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2024 Summit
Y2 - 21 June 2024 through 23 June 2024
ER -