The rapid growth of medical data volumes and the increasing complexity of analytical tasks require database management systems (DBMS) with high computational efficiency, scalability, and reliability. Modern information technologies, particularly graphics processing units (GPUs), provide powerful tools for accelerating data-intensive operations through massive parallelism. This paper presents an integrated overview of GPU architectures and their application in modeling and optimizing medical database management systems. We analyze key GPU architectural solutions (G80, GT200, and Fermi), the CUDA parallel programming model, and memory hierarchies, and demonstrate their relevance for medical data storage, processing, and signal analysis. The proposed integrated approach enables efficient processing of medical signals and images, supports large-scale databases, and reduces computational and economic costs in internet-based medical applications.
| Mualliflar | Saidovich, Rakhimov Bakhtiyar, Kabulovna, Sobirova Sabokhat, Bakhtiyarovna , Rakhimova Feroza, Akabarovna, Allayarova Asal, Bakhtiyarovich, Saidov Atabek |
|---|---|
| Jurnal | Innovative Multidisciplinary Journal of Applied Technology |
| Nashr sanasi | 2026-05-31 |
| Jild | 4 |
| Son | 4 |
| Betlar | 174-178 |
| Til | Ingliz |
| DOI | 10.51699/j329t768 |
DOI: 10.51699/j329t768 · Maqolaning asl sahifasi
Medical Databases, GPU Computing, CUDA, Parallel Processing, Information Technology, Medical Data Modeling
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