GPU-Augmented OLAP Execution Engine: GPU Offloading
Abstract
Modern OLAP systems have mitigated I/O bottlenecks via storage-compute separation and columnar layouts, but CPU costs in the execution layer (especially Top-K selection and join probe) are emerging as new bottlenecks at scale. This paper proposes a hybrid architecture that augments existing vectorized execution by selectively offloading only high-impact primitives to the GPU. To reduce data movement, we use key-only transfer (keys and pointers) with late materialization. We further introduce a Risky Gate (risk-aware gating) that triggers offloading only in gain/risk intervals based on input size, transfer, kernel and post-processing costs, and candidate-set complexity (K, M). Using PostgreSQL microbenchmarks and GPU proxy measurements, we observe improved tail latency (P95/P99) under gated offloading compared to always-on GPU offloading. This work extends the risk-aware gating principle used for optimizer-stage GPU-assisted measurement (arXiv:2512.19750) to execution-layer OLAP primitives.
Cite
@article{arxiv.2601.19911,
title = {GPU-Augmented OLAP Execution Engine: GPU Offloading},
author = {Ilsun Chang},
journal= {arXiv preprint arXiv:2601.19911},
year = {2026}
}
Comments
4 pages, figures included. PostgreSQL microbenchmarks and GPU proxy measurements (RTX 4060 Laptop GPU). Extends arXiv:2512.19750 to execution-layer OLAP primitives