Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization
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- Agbaria A., Minor D., Peterfreund N., Rozenberg E., Rosenberg O. (2017) Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization. In: Blanas S., Bordawekar R., Lahiri T., Levandoski J., Pavlo A. (eds) Data Management on New Hardware. IMDM 2016, ADMS 2016. Lecture Notes in Computer Science, vol 10195. Springer, Cham
Existing work on accelerating analytic DB query processing with (discrete) GPUs fails to fully realize their potential for speedup through parallelism: Published results do not achieve significant speedup over more performant CPU-only DBMSes when processing complete queries.
This paper presents a successful effort to better meet this challenge, in the form of a proof-of-concept query processing framework. The framework constitutes a graft onto an existing DBMS, altering some parts of it and replacing its execution engine entirely. It intensively refactors query execution plans, making them better-parallelizable, before executing them on either a CPU or on GPU. This results in a significant speedup even on a CPU, and a further speedup when using a GPU, over the chosen host DBMS (MonetDB) — which itself already bests most published results utilizing a GPU for query processing.
Finally, we outline some concrete future improvements on our results which can cut processing time by half and possibly much more.