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The Power of Nested Parallelism in Big Data Processing – Hitting Three Flies with One Slap –

Summary: Matryoshka enables nested parallelism in dataflow engines with a two-phase flattening that turns programs into flat ones, even with inner control flow. It adds nesting primitives and runtime data-aware optimizations, validated on PageRank and K-means. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6240
Venue
SIGMOD
Year
2021
Pagerank
5.3449654e-05
Overall Rank
8,958 | 38.55%
DOI
10.1145/3448016.3457287

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gevay_sigmod21,
        title = {{The Power of Nested Parallelism in Big Data Processing – Hitting Three Flies with One Slap –}},
        author = {Gévay, Gábor E. and Quiané-Ruiz, Jorge-Arnulfo and Markl, Volker},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457287},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457287},
        year = {2021}
}

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