Scalable GPU Acceleration of Scalar Functions in Analytical Databases: Compilation, Benchmarking, and Optimization
Summary: GPU acceleration for scalar functions in analytical DBs, not just relational ops. LLVM/MLIR toolchain auto-translates production CPU scalar-function code to efficient GPU kernels; new scalar-heavy TPC-H variant shows 7.6x/6.4x gains and hand-tuned parity. (summarized by gpt-5.4-mini on May 27 2026)
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Authors
- 1. Kaushik Rajan (Microsoft)
- 2. Sampath Rajendra (Microsoft)
- 3. Momin Al-Ghosien (Microsoft)
- 4. Nicolas Bruno (Microsoft)
- 5. Carlo Curino (Microsoft)
- 6. Matteo Interlandi (Microsoft)
- 7. Yinan Li (Microsoft)
- 8. Lukas M. Maas (Microsoft)
- 9. Craig Peeper (Microsoft)
- 10. Surajit Chaudhuri (Microsoft)
- 11. Johannes Gehrke (Microsoft)
BibTeX Citation
@article{rajan_vldb26,
title = {{Scalable GPU Acceleration of Scalar Functions in Analytical Databases: Compilation, Benchmarking, and Optimization}},
author = {Rajan, Kaushik and Rajendra, Sampath and Al-Ghosien, Momin and Bruno, Nicolas and Curino, Carlo and Interlandi, Matteo and Li, Yinan and Maas, Lukas M. and Peeper, Craig and Chaudhuri, Surajit and Gehrke, Johannes},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {7},
pages = {1441--1454},
doi = {10.14778/3801059.3801061},
url = {https://doi.org/10.14778/3801059.3801061},
year = {2026}
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