Themis: A GPU-accelerated Relational Query Execution Engine
Summary: Themis is a GPU relational engine that minimizes intra- and inter-warp load imbalance by redistributing tuples among threads in a warp (so operators run only when all threads hold inputs) and migrating heavy-warps' tuples to idle warps. It uses cheap warp-workload approximations with adaptive thresholds and achieves up to 379× speedup on skewed JCC-H (TPC-H) workloads. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kijae Hong
- 2. Kyoungmin Kim
- 3. Young-Koo Lee
- 4. Yang-Sae Moon
- 5. Sourav S Bhowmick
- 6. Wook-Shin Han
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,916 | Terabyte-Scale Analytics in the Blink of an Eye | 2026 | VLDB | 4.6173899e-05 |
| 10,281 | GPU Acceleration of SQL Analytics on Compressed Data | 2026 | VLDB | 4.1945683e-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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