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High-Performance Computing for Efficient Transaction Processing: A Comparative Study of Parallel and Sequential Execution using SQLite

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Udgivet i:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2024)
Hovedforfatter: CVRoshan Balaji
Andre forfattere: Shaman, K R, VPritham Raj, Kumar, NNithish, Kumaran, K
Udgivet:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Fag:
Parallel processing
Comparative studies
Computation
Finance
Databases
Fault tolerance
Efficiency
Optimization
Blockchain
Electronic commerce
Transaction processing
Multiprocessing
High performance computing
Software
Economic
Online adgang:Citation/Abstract
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Resumen:Conference Title: 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)Conference Start Date: 2024, Dec. 17 Conference End Date: 2024, Dec. 18 Conference Location: Bengaluru, IndiaHigh-performance computing (HPC) has become an essential tool for improving the efficiency and scalability of transaction processing systems, especially as data volumes continue to grow in fields like finance, e-commerce, and blockchain. This paper presents a comparative study of parallel and sequential transaction processing methods using an SQLite database. Specifically, the research investigates the impact of HPC techniques on transaction throughput, processing speed, and efficiency by simulating 1,000 user transactions. The study employs Python's multiprocessing module to simulate parallel execution, contrasting it with traditional sequential execution. Key performance indicators, including execution time, transaction success rate, and system efficiency, were analyzed to determine the advantages of parallel processing in a transaction-heavy environment. Our results reveal that parallel execution significantly reduces processing time, boosts throughput, and increases overall system efficiency compared to sequential processing. Additionally, the study discusses how parallel processing techniques can address common bottlenecks in transaction-heavy applications and provide solutions for improving the performance of large-scale, data-intensive systems. The findings demonstrate the potential for using HPC to optimize database operations, particularly in systems where high-volume transaction processing is a critical requirement. Future work will explore advanced parallelization strategies, fault tolerance mechanisms, and integrations with distributed databases and blockchain systems. This research contributes to the growing body of knowledge on optimizing transaction processing in high-performance computing environments, with potential applications across various sectors, including financial services, e-commerce, and blockchain technology.
DOI:10.1109/ICICNIS64247.2024.10823214
Fuente:Science Database