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Efficiently Processing Joins and Grouped Aggregations on GPUs
Summary: Revisits GPU join and group-by; GFTR reduces random accesses, up to 2.3x. Optimizes hash- and sort-based group-by (19.4x, 1.7x); adds partition-based group-by for high cardinalities, with cost models and heuristics to guide optimizers.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h83a138a75fbc2068
Venue
SIGMOD
Year
2025
Pagerank
5.8669913e-05
Overall Rank
6,153 | 58.65%
DOI
10.1145/3709689
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{wu_sigmod25,
title = {{Efficiently Processing Joins and Grouped Aggregations on GPUs}},
author = {Wu, Bowen and Koutsoukos, Dimitrios and Alonso, Gustavo},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709689},
url = {https://dl.acm.org/doi/10.1145/3709689},
year = {2025}
}
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