GPU-Augmented OLAP Execution Engine: GPU Offloading
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915758557102080 |
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| author | Chang, Ilsun |
| author_facet | Chang, Ilsun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19911 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Augmented OLAP Execution Engine: GPU Offloading Chang, Ilsun Hardware Architecture Databases Distributed, Parallel, and Cluster Computing H.2.4 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. |
| title | GPU-Augmented OLAP Execution Engine: GPU Offloading |
| topic | Hardware Architecture Databases Distributed, Parallel, and Cluster Computing H.2.4 |
| url | https://arxiv.org/abs/2601.19911 |