Into the CUDA Multiverse of Runtime Compilation: Exploring Kernel Fusion Strategies for GPU Database Systems
Summary: CUDA Code-General interactively compares runtime/static compilation and three CUDA kernel-fusion strategies for TPC-H or arbitrary SQL. It exposes pipeline-driven compilation costs, cooperative-group synchronization, and device-side dynamic launches via generated-code inspection. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Maha Alwahibi (University of Bamberg)
- 2. Bachana Jangebashvili (University of Bamberg)
- 3. Maximilian E. Schüle (University of Bamberg)
BibTeX Citation
@article{alwahibi_vldb26,
title = {{Into the CUDA Multiverse of Runtime Compilation: Exploring Kernel Fusion Strategies for GPU Database Systems}},
author = {Alwahibi, Maha and Jangebashvili, Bachana and Schüle, Maximilian E.},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4658--4661},
doi = {10.14778/3827998.3828090},
url = {https://doi.org/10.14778/3827998.3828090},
year = {2026}
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Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 21 | Efficiently Compiling Efficient Query Plans for Modern Hardware | 2011 | VLDB | 0.00056855599 |
| 1,747 | HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines | 2019 | VLDB | 9.7335416e-05 |
| 1,952 | Quantifying TPC-H Choke Points and Their Optimizations | 2020 | VLDB | 9.3189525e-05 |
| 3,961 | Data-Parallel Query Processing on Non-Uniform Data | 2020 | VLDB | 6.8948667e-05 |
| 4,645 | Improving Execution Efficiency of Just-in-time Compilation based Query Processing on GPUs | 2021 | VLDB | 6.4881339e-05 |
| 4,752 | Optimal Join Algorithms Meet Top-k | 2020 | SIGMOD | 6.434561e-05 |
| 8,894 | QPJVis Demo: Quality-boost Progressive Join Query Processing System | 2024 | VLDB | 5.2559789e-05 |
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