Comparative assessment of GPU-Accelerated VS. CPU-based databases: Architecture, performance, and cost implications

The current research paper aims to outline the capabilities of GP-GPU accelerated parallel computing for database operations and compare it with Central Processing Units (CPU)-based methodologies. The study evaluates the performance, scalability, and cost-effectiveness of both processing architectures through the execution of three database operations: None external critical success factors were identified for this system. We found out that GPUs take situational advantages by virtue of their parallelism nature for different computations and also show limitations arising from data transfer overhead, memory availability. The results are further elaborated in detail and recommendations for future work are also provided in order to add to the extant knowledge on GPU-accelerated databases.