Relational Joins on Graphics Processors
Summary: GPU-based relational joins use data-parallel primitives (split, sort) on GPUs, leveraging random writes and inter-processor communication. Implements indexed or non-indexed nested-loop, sort-merge, and hash joins; yields 2–7× CPU speedups on NVIDIA G80. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bingsheng He (Hong Kong University of Science and Technology)
- 2. Ke Yang (Zhejiang University)
- 3. Rui Fang (Highbridge Capital Management Limited Liability Company)
- 4. Mian Lu (Hong Kong University of Science and Technology)
- 5. Naga K. Govindaraju (Microsoft)
- 6. Qiong Luo (Hong Kong University of Science and Technology)
- 7. Pedro V. Sander (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{he_sigmod08,
title = {{Relational Joins on Graphics Processors}},
author = {He, Bingsheng and Yang, Ke and Fang, Rui and Lu, Mian and Govindaraju, Naga K. and Luo, Qiong and Sander, Pedro V.},
series = {{SIGMOD} '08},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1376616.1376670},
url = {https://dl.acm.org/doi/10.1145/1376616.1376670},
year = {2008}
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