Title: Optimising group-by and aggregation on the coupled CPU-GPU architecture

Authors: Hua Luan; Yan Fu

Addresses: School of Artificial Intelligence, Beijing Normal University, Beijing, China ' School of Artificial Intelligence, Beijing Normal University, Beijing, China

Abstract: The coupled CPU-GPU architecture as an emerging heterogeneous environment has attracted much attention from researchers. On this kind of architecture, the GPU is built on the same chip as the CPU. Different from the discrete GPU, there is no data transfer via the PCIe bus between the CPU and the integrated GPU, and the two processors share the same memory. Grouping and aggregation is an important and time-consuming operator in a DBMS. Whether the coupled GPU could be used to increase its performance is an interesting problem. In this paper, we study how to optimise grouping and aggregation based on chained hashing on the coupled CPU-GPU architecture. Two flexible co-processing strategies are proposed to take advantage of the hybrid computing resources effectively. A thorough set of experiments are conducted and the results show that the coupled GPU could help obtain better performance for group-by and aggregation.

Keywords: hash grouping; coupled CPU-GPU architecture; co-processing.

DOI: 10.1504/IJCSE.2024.137290

International Journal of Computational Science and Engineering, 2024 Vol.27 No.2, pp.219 - 229

Received: 19 Mar 2022
Received in revised form: 17 Jan 2023
Accepted: 03 Feb 2023
Published online: 11 Mar 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article