Eyal Rozenberg

This short paper present a collection of GPU lightweight decompression algorithms implementations within a FOSS library, Giddy – the first to be published to offer such function-ality. As the use of compression is important in ameliorating PCIe data transfer bottlenecks, we believe this library and its constituent implementations can serve as useful building blocks in GPU-accelerated DBMSes — as well as other data-intensive systems. The paper also includes an initial exploration of GPU-oriented patched compression schemes. Patching makes compression ratio robust against outliers, and is important with real-life data, which (in contrast to many synthetic benchmark datasets) exhibits non-uniform data distributions and noise. An experimental evaluation of both the unpatched and the patched schemes in Giddy is included.

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.