Themis: A GPU-accelerated Relational Query Execution Engine
Summary: Themis is a GPU relational engine addressing intra- and inter-warp imbalance from skewed, varying-cardinality pipelines. It redistributes tuples within and across warps using adaptive workload estimates, achieving up to 379× gains on skewed TPC-H. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kijae Hong (Pohang University of Science and Technology)
- 2. Kyoungmin Kim (EPFL)
- 3. Young-Koo Lee (Kyunghee University)
- 4. Yang-Sae Moon (Kangwon National University)
- 5. Sourav S Bhowmick (Nanyang Technological University)
- 6. Wook-Shin Han (Pohang University of Science and Technology)
BibTeX Citation
@article{hong_vldb25,
title = {{Themis: A GPU-accelerated Relational Query Execution Engine}},
author = {Hong, Kijae and Kim, Kyoungmin and Lee, Young-Koo and Moon, Yang-Sae and Bhowmick, Sourav S and Han, Wook-Shin},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {2},
pages = {426--438},
doi = {10.14778/3705829.3705856},
url = {https://doi.org/10.14778/3705829.3705856},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,959 | Terabyte-Scale Analytics in the Blink of an Eye | 2026 | VLDB | 5.5181056e-05 |
| 10,580 | GPU Acceleration of SQL Analytics on Compressed Data | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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