Concurrent Analytical Query Processing with GPUs

In current databases, GPUs are used as dedicated accel-erators 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 uti-lization 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 so-lution relies on GPU query scheduling and device memory swapping policies to address this challenge. We have im-plemented a prototype system and evaluated it intensively. The experiment results confirm the effectiveness and perfor-mance advantage of our approach. By executing multiple GPU queries concurrently, system throughput can be im-proved by up to 55 % compared with dedicated processing. 1