GPU Acceleration of SQL Analytics on Compressed Data
Summary: Direct GPU execution over RLE, index-, bit-width-, and dictionary-encoded columns, avoiding decompression and fitting larger-than-HBM datasets. Novel multi-RLE and heterogeneous-encoding operators, implemented with portable PyTorch tensors, deliver order-of-magnitude gains over CPU systems. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Zezhou Huang (Microsoft)
- 2. Krystian Sakowski (Microsoft)
- 3. Hans Lehnert (Microsoft)
- 4. Wei Cui (Microsoft)
- 5. Carlo Curino (Microsoft)
- 6. Matteo Interlandi (Microsoft)
- 7. Marius Dumitru (Microsoft)
- 8. Rathijit Sen (Microsoft)
BibTeX Citation
@article{huang_vldb26,
title = {{GPU Acceleration of SQL Analytics on Compressed Data}},
author = {Huang, Zezhou and Sakowski, Krystian and Lehnert, Hans and Cui, Wei and Curino, Carlo and Interlandi, Matteo and Dumitru, Marius and Sen, Rathijit},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {3},
pages = {320--333},
doi = {10.14778/3778092.3778095},
url = {https://doi.org/10.14778/3778092.3778095},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,959 | Terabyte-Scale Analytics in the Blink of an Eye | 2026 | VLDB | 5.5181056e-05 |
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