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GPUTeraSort: High Performance Graphics Co-processor Sorting for Large Database Management
Summary: GPUTeraSort exploits GPU data- and task-parallelism to sort billion-record wide-key data, while the CPU handles I/O and resource management. A two-phase pipeline: read/build/sort/run/write and read/merge/write overlaps I/O with GPU sort, delivering near-peak I/O, higher bandwidth, and PennySort-level price-performance.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h307db4ec386f5b1b
Venue
SIGMOD
Year
2006
Pagerank
0.00021569633
Overall Rank
304 | 97.97%
DOI
10.1145/1142473.1142511
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{govindaraju_sigmod06,
title = {{GPUTeraSort: High Performance Graphics Co-processor Sorting for Large Database Management}},
author = {Govindaraju, Naga K. and Gray, Jim and Kumar, Ritesh and Manocha, Dinesh},
series = {{SIGMOD} '06},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1142473.1142511},
url = {https://dl.acm.org/doi/10.1145/1142473.1142511},
year = {2006}
}
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Efficient Implementation of Sorting on Multi-Core SIMD CPU Architecture 2008
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945
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VLDB
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2,485
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2,890
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3,117
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Lightning Fast and Space Efficient Inequality Joins 2015
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JouleSort: A Balanced Energy-Efficiency Benchmark 2007
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PARADIS: An Efficient Parallel Algorithm for In-place Radix Sort 2015
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Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms 2021
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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