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A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms

Summary: Large-scale KNL/Skylake experiments show Cartesian Vertex-Cut outscales Edge-Cut despite higher replication and communication, because communication topology matters beyond volume. Advocates multi-policy systems and a computation/cluster-driven selection tree. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12156
Venue
VLDB
Year
2019
Pagerank
5.6338252e-05
Overall Rank
7,362 | 49.50%
DOI
10.14778/3297753.3297754

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BibTeX Citation

@article{gill_vldb19,
        title = {{A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms}},
        author = {Gill, Gurbinder and Dathathri, Roshan and Hoang, Loc and Pingali, Keshav},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {4},
        pages = {321--334},
        doi = {10.14778/3297753.3297754},
        url = {https://doi.org/10.14778/3297753.3297754},
        year = {2019}
}

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