Comparative Performance and Scalability Analysis of GPU-accelerated Database Operations

Carl Andersson, Jonathan Nilsson
Department of Computer Science and Engineering, Chalmers University of Technology, University of Gothenburg
Chalmers University of Technology, University of Gothenburg, 2023


   title={Comparative Performance and Scalability Analysis of GPUaccelerated Database Operations},

   author={Andersson, Carl and Nilsson, Jonathan},



Download Download (PDF)   View View   Source Source   



This Master’s thesis investigates the performance dynamics of database operations – V-Search, Fuzzy Search, and Join – implemented on both Central Processing Units (CPU) and Graphics Processing Units (GPU). With the ever-increasing demand for efficient data processing, it has become crucial to understand and optimize the use of different hardware platforms for executing diverse database tasks. As such, this research sheds light on the performance of each type of processing unit when running the said operations. The study first details the design and implementation of each database operation on both CPU and GPU, taking into account the different architectural characteristics and processing capabilities of each unit. The specific operations were chosen due to their wide use in the field of data management and their different processing requirements, which allows for a comprehensive performance analysis. Next, a series of benchmark tests is conducted to evaluate the relative performance of the CPU and GPU implementations. Factors such as data size, data type, and transfer time, among others are taken into account. The results show a detailed comparison of execution times between the two implementations, offering insights into the potential advantages and limitations of each. This work contributes to a better understanding of the trade-offs involved when choosing between CPU and GPU for database operations. We hope that our findings will inform future work on hardware-specific optimization for database systems, leading to more efficient and effective solutions for large-scale data processing tasks.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Contact us: