"Concurrent analytical query processing with GPUs"  

Kaibo Wang, Kai Zhang, Yuan Yuan, Siyuan Ma, Rubao Lee, Xiaoning Ding, and Xiaodong Zhang 

Proceedings of 40th International Conference on Very Large Data bases (VLDB  2014), 
Hangzhou, China, September 1-5, 2014.


In current database, GPUs are used as dedicated accerators to process each individual 
query. Sharing GPUs among concurrent queries is not supported, causing serious resource 
underutilization. Based on the profiling of an open source GPU query engine running 
commonly used single query data warehousing workloads, we observe that the utilization of 
main GPU resources is only up to 25%. The underutilization leads to low system throughput. 

To address the problem, this paper proposes concurrent query execution as an effection 
solution. To efficiently share GPUs among concurrent queries for high throughput, the 
major challenge is to provide software support to control and resolve resource contention 
incurred by the sharing. Our solution relies on GPU query scheduling and device memory 
swapping policies to address this challenge. We have implemented a prototype system and 
evaluated it intensively. The experiment results confirm the effectiveness and performance 
advantage of our approach. By executing multiple GPU queries concurrently, system throughput 
can be improved by up to 55% compared with dedicated processing.