Research on Parallel Execution Techniques for Improving the Expandability of Database Systems
DOI:
https://doi.org/10.71222/bqj1hz96Keywords:
parallel execution, database system, scalability, multi core architectureAbstract
In today's era of widespread big data and cloud computing technology, database systems are under tremendous pressure to handle massive amounts of data and complex queries. When traditional independent database systems encounter high concurrency and large-scale data operations, their performance begins to feel inadequate. Parallel execution technology has been developed as a means to improve database performance and scalability. This technology significantly improves the system's processing capability and response speed by splitting database queries and transaction processing tasks into multiple small tasks for parallel execution. The purpose of this article is to analyze in depth the role of parallel execution technology in enhancing the scalability of database systems, evaluate the current status and challenges of this technology in database applications, and propose improvement strategies for multi-core and multi node architectures. Through in-depth research on the principles and applications of parallel execution technology, the aim is to provide theoretical support and practical suggestions for improving the scalability of database systems.
References
1. R. D. Alessio, A. Giordano, G. Mazzuca, et al., "Tailoring load balancing of cellular automata parallel execution to the case of a two-dimensional partitioned domain," J. Supercomput., vol. 79, no. 8, pp. 9273–9287, 2023, doi: 10.1007/s11227-023-05043-3.
2. W. Liu, L. Lin, J. Zhang, et al., "Multi-core parallel architecture design and experiment for deep learning model training," Mul-timedia Tools Appl., vol. 81, no. 8, pp. 11587–11604, 2022, doi: 10.1007/s11042-022-12292-6.
3. O. A. M. Khashan, N. M. Khafajah, W. Alomoush, M. Alshinwan, S. S. Atawneh, and M. K. Alsmadi, "Dynamic multimedia encryption using a parallel file system based on multi-core processors," Cryptography, vol. 7, no. 1, 2023, Art. no. 12. DOI: 10.3390/cryptography7010012, doi: 10.3390/cryptography7010012.
4. S. Dirim, O. O. Oezener, and H. Soezer, "Prioritization and parallel execution of test cases for certification testing of embedded systems," Softw. Qual. J., vol. 31, no. 2, 2023, doi: 10.1007/s11219-022-09594-1.
5. C. Xia, J. Zhao, and H. F. X. Cui, "HOPE: a heterogeneity-oriented parallel execution engine for inference on mobiles," High Technol. Lett., vol. 28, no. 4, pp. 363–372, 2022, doi: 10.3772/j.issn.1006-6748.2022.04.004.
6. T. Bagies, W. Le, and S. A. Jannesari, "Reducing branch divergence to speed up parallel execution of unit testing on GPUs," J. Supercomput., vol. 79, no. 16, pp. 18340–18374, 2023, doi: 10.1007/s11227-023-05375-0.
7. S. Baheti, P. S. Anjana, S. Peri, et al., "DiPETrans: A framework for distributed parallel execution of transactions of blocks in blockchains," Concurrency Comput. Pract. Exp., vol. 34, no. 10, 2022, doi: 10.1002/cpe.6804.
8. G. Dhanabalan, S. T. Selvi, and M. Mahdal, "Scan time reduction of PLCs by dedicated parallel-execution multiple PID con-trollers using an FPGA," Sensors, vol. 22, no. 12, 2022, doi: 10.3390/s22124584.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Zhongqi Zhu (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.