Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization
Intel
Seventh International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures (ADMS), 2016
@article{agbaria2016overtaking,
title={Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization},
author={Agbaria, Adnan and Minor, David and Peterfruend, Natan and Rozenberg, Eyal and Rosenberg, Ofer and Talyansky, Roman},
year={2016}
}
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 e!ort 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.
October 12, 2016 by hgpu
