Enhancing database performance through SQL optimization, parallel processing and GPU integration
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| izdano v: | BIO Web of Conferences vol. 113 (2024), p. n/a |
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| Glavni avtor: | |
| Drugi avtorji: | , , , |
| Izdano: |
EDP Sciences
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| Teme: | |
| Online dostop: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1051/bioconf/202411304010 |2 doi | |
| 035 | |a 3069606031 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 268328 |2 nlm | ||
| 100 | 1 | |a Nuriev, Marat | |
| 245 | 1 | |a Enhancing database performance through SQL optimization, parallel processing and GPU integration | |
| 260 | |b EDP Sciences |c 2024 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a This article delves into the cutting-edge methodologies revolutionizing database management systems (DBMS) through the lens of SQL query optimization, parallel processing, and the integration of graphics processing units (GPUs). As the digital world grapples with ever-increasing volumes of data, the efficiency, speed, and scalability of database systems have never been more critical. The first section of the article focuses on SQL query optimization, highlighting strategies to refine query performance and reduce resource consumption, thus enhancing application responsiveness and efficiency. The discourse then transitions to parallel processing in databases, an approach that leverages multiple processors or distributed systems to significantly boost data processing capabilities. This segment explores the advantages of parallelism in managing large datasets and complex operations, addressing the challenges and the impact on system scalability and fault tolerance. Furthermore, the article examines the innovative application of GPUs in database management, a development that offers profound speedups for analytical and machine learning tasks within DBMS. Despite the complexities and the initial investment required, the utilization of GPUs is portrayed as a game-changer in processing largescale data, thanks to their highly parallel architecture and computational prowess. Together, these advancements signify a transformative shift in database technologies, promising to address the challenges of modern data management with unprecedented efficiency and scalability. This article not only elucidates these sophisticated technologies but also provides a glimpse into the future of database systems, where optimization, parallel processing, and GPU integration play pivotal roles in navigating the data-driven demands of the contemporary digital landscape. | |
| 653 | |a Databases | ||
| 653 | |a Data base management systems | ||
| 653 | |a Data management | ||
| 653 | |a Parallel processing | ||
| 653 | |a Data processing | ||
| 653 | |a Queries | ||
| 653 | |a Graphics processing units | ||
| 653 | |a Fault tolerance | ||
| 653 | |a Optimization | ||
| 653 | |a Task complexity | ||
| 653 | |a Efficiency | ||
| 653 | |a Resource consumption | ||
| 653 | |a Machine learning | ||
| 653 | |a Cognitive tasks | ||
| 653 | |a Query processing | ||
| 653 | |a Integration | ||
| 653 | |a Management systems | ||
| 653 | |a Query languages | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Zaripova, Rimma | |
| 700 | 1 | |a Sinicin, Alexey | |
| 700 | 1 | |a Chupaev, Andrey | |
| 700 | 1 | |a Shkinderov, Maksim | |
| 773 | 0 | |t BIO Web of Conferences |g vol. 113 (2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3069606031/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3069606031/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |