GPU-Augmented OLAP Execution Engine: GPU Offloading

TL;DR

This paper introduces a hybrid GPU-accelerated OLAP execution engine that selectively offloads high-impact primitives using risk-aware gating, reducing data movement and improving tail latency in large-scale analytical queries.

Contribution

It proposes a novel risk-aware gating mechanism for selective GPU offloading in OLAP execution, extending previous optimizer-stage gating to execution primitives.

Findings

Improved tail latency (P95/P99) with gated offloading

Reduced data transfer via key-only transfer and late materialization

Effective offloading of high-impact primitives improves performance

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.