Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects
Summary: Triton Join scales large joins on GPUs by using fast interconnects (NVLink 2.0) to spill state to main memory. Delivers >100x GPU hash-join gains over non-partitioned approaches, and up to 2.5x vs CPU radix-join, enabling GPU DBMSs to exceed GPU memory. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Clemens Lutz (Technical University of Berlin)
- 2. Sebastian Breß (Snowflake)
- 3. Steffen Zeuch (German National Research Center for Information Technology)
- 4. Tilmann Rabl (Hasso Plattner Institute)
- 5. Volker Markl (German National Research Center for Information Technology; Technical University of Berlin)
BibTeX Citation
@inproceedings{lutz_sigmod22,
title = {{Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects}},
author = {Lutz, Clemens and Breß, Sebastian and Zeuch, Steffen and Rabl, Tilmann and Markl, Volker},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517911},
url = {https://dl.acm.org/doi/10.1145/3514221.3517911},
year = {2022}
}
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