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Distributed GPU Joins on Fast RDMA-capable Networks

Summary: Pipelined distributed GPU joins on fast RDMA networks overlap shuffling with build/probe to hide GPU idle time. RDMA/GPUDirect-based algorithms scale to arbitrarily large tables and show up to 6x faster full queries versus CPU-only joins. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6594
Venue
SIGMOD
Year
2023
Pagerank
6.1596139e-05
Overall Rank
5,587 | 61.67%
DOI
10.1145/3588709

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{thostrup_sigmod23,
        title = {{Distributed GPU Joins on Fast RDMA-capable Networks}},
        author = {Thostrup, Lasse and Doci, Gloria and Boeschen, Nils and Luthra, Manisha and Binnig, Carsten},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588709},
        url = {https://dl.acm.org/doi/10.1145/3588709},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
252 Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited 2014 VLDB 0.00023242719
631 Relational Joins on Graphics Processors 2008 SIGMOD 0.00015591241
678 Fast Sort on CPUs and GPUs: A Case for Bandwidth Oblivious SIMD Sort 2010 SIGMOD 0.00015061068
823 The Yin and Yang of Processing Data Warehousing Queries on GPU Devices 2013 VLDB 0.00013792901
892 Rack-Scale In-Memory Join Processing using RDMA 2015 SIGMOD 0.00013376761
976 The End of Slow Networks: It's Time for a Redesign 2016 VLDB 0.00012864533
1,342 Designing Distributed Tree-based Index Structures for Fast RDMA-capable Networks 2019 SIGMOD 0.00011098147
1,345 The End of a Myth: Distributed Transactions Can Scale 2017 VLDB 0.00011090483
1,466 A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics 2020 SIGMOD 0.0001068941
1,574 Pipelined Query Processing in Coprocessor Environments 2018 SIGMOD 0.00010321274
2,140 Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture 2013 VLDB 9.0991487e-05
2,202 Quantifying TPC-H Choke Points and Their Optimizations 2020 VLDB 8.9639459e-05
2,566 Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects 2020 SIGMOD 8.4116562e-05
2,713 Robust Query Processing in Co-Processor-accelerated Databases 2016 SIGMOD 8.2122726e-05
2,926 Distributed Join Algorithms on Thousands of Cores 2017 VLDB 7.9549783e-05
3,134 Efficient Join Algorithms For Large Database Tables in a Multi-GPU Environment 2021 VLDB 7.7231028e-05
3,178 Why it is time for a HyPE: A Hybrid Query Processing Engine for Efficient GPU Coprocessing in DBMS 2013 VLDB 7.6630901e-05
3,227 MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures 2021 SIGMOD 7.6217889e-05
3,868 DFI: The Data Flow Interface for High-Speed Networks 2021 SIGMOD 7.0614899e-05
4,535 Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects 2022 SIGMOD 6.6419266e-05
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