Concurrent analytical query processing with GPUs

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

Research output: Contribution to journalConference articlepeer-review

63 Scopus citations


In current databases, GPUs are used as dedicated accelerators to process each individual query. Sharing GPUs among concurrent queries is not supported, causing serious resource underutilization. Based on the profiling of an opensource GPU query engine running commonly used singlequery 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 effective 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.

Original languageEnglish (US)
Pages (from-to)1011-1022
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number11
StatePublished - 2014
EventProceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China
Duration: Sep 1 2014Sep 5 2014

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science


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