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FPGA-based Data Partitioning
Summary: FPGA-accelerated data partitioning in a CPU-FPGA coherent hybrid architecture to cut partitioning cost and data-transfer overhead in parallel joins. Shows faster, more robust partitioning than CPU-only schemes, enabling partition-on-FPGA while build/probe runs on CPU.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h657036aef22bc11f
Venue
SIGMOD
Year
2017
Pagerank
6.3254925e-05
Overall Rank
4,958 | 66.77%
DOI
10.1145/3035918.3035946
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{kara_sigmod17,
title = {{FPGA-based Data Partitioning}},
author = {Kara, Kaan and Giceva, Jana and Alonso, Gustavo},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3035946},
url = {https://dl.acm.org/doi/10.1145/3035918.3035946},
year = {2017}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
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Rethinking SIMD Vectorization for In-Memory Databases
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Fast Sort on CPUs and GPUs: A Case for Bandwidth Oblivious SIMD Sort
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