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Hyper Dimension Shuffle: Efficient Data Repartition at Petabyte Scale in SCOPE

Summary: Hyper Dimension Shuffle introduces a recursive, divide-and-conquer shuffle for petabyte-scale data in SCOPE. Recursive partitioning with intermediate aggregations yields quasilinear shuffling complexity and tight fan-out/fan-in guarantees, avoiding prior quadratic blowups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11810
Venue
VLDB
Year
2019
Pagerank
6.3247927e-05
Overall Rank
4,248 | 70.45%
DOI
10.14778/3339490.3339495

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