rNdN: Fast Query Compilation for NVIDIA GPUs

Abstract

GPU database systems are an effective solution to query optimization, particularly with compilation and data caching. They fall short, however, in end-to-end workloads as existing compiler toolchains are too expensive for use with short-running queries. In this work, we define and evaluate a runtime-suitable query compilation pipeline for NVIDIA GPUs that extracts high performance with only minimal optimization. In particular, our balanced approach successfully trades minor slowdowns in execution for major speedups in compilation, even as data sizes increase. We demonstrate performance benefits compared to both CPU and GPU database systems using interpreters and compilers, extending query compilation for GPUs beyond cached use cases.

A Comprehensive Analysis of Nvidia's Technological Innovations, Market Strategies, and Future Prospects

“…Addressing the limitation of short queries in Nvidia GPU databases, Krolik et al (2023) presented the revolutionary compilation pipeline, rNdN. This innovative solution strikes a balance between minor execution slowdowns and significant compilation speedups, unlocking doors for a wider range of applications in GPU databases.…”