Pipelined Query Processing in Coprocessor Environments

Query processing on GPU-style coprocessors is severely limited by the movement of data. With teraflops of compute throughput in one device, even high-bandwidth memory cannot provision enough data for a reasonable utilization. Query compilation is a proven technique to improve memory efficiency. However, its inherent tuple-at-a-time processing style does not suit the massively parallel execution model of GPU-style coprocessors. This compromises the improvements in efficiency offered by query compilation. In this paper, we show how query compilation and GPU-style parallelism can be made to play in unison nevertheless. We describe a compiler strategy that merges multiple operations into a single GPU kernel, thereby significantly reducing bandwidth demand. Compared to operator-at-a-time, we show reductions of memory access volumes by factors of up to 7.5x resulting in shorter kernel execution times by factors of up to 9.5x. ACM Reference Format: Henning Funke, Sebastian Breß, Stefan Noll, Volker Markl, and Jens Teubner. 2018. Pipelined Query Processing in Coprocessor Environments. In SIGMOD/PODS ’18: 2018 International Conference on Management of Data, June 10–15, 2018, Houston, TX, USA. ACM, New York, NY, USA, 16 pages. https://doi.org/10.1145/3183713.3183734