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Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations

Summary: Shows that minimizing tail latency for data-parallel queries requires placing items so each query's accesses are spread across many machines to maximize per-query parallelism. Proposes a linear computable parallelism metric and a scalable partitioning-based placement optimizer; 7–64% p99 improvements on Solr/MongoDB. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13516
Venue
VLDB
Year
2023
Pagerank
5.4889128e-05
Overall Rank
8,091 | 44.49%
DOI
10.14778/3574245.3574260

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{baruah_vldb23,
        title = {{Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations}},
        author = {Baruah, Nirvik and Kraft, Peter and Kazhamiaka, Fiodar and Bailis, Peter and Zaharia, Matei},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {4},
        pages = {760--771},
        doi = {10.14778/3574245.3574260},
        url = {https://doi.org/10.14778/3574245.3574260},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
8,861 Optimizing the cloud? Don't train models. Build oracles! 2024 CIDR 5.355716e-05
9,735 SkyPIE: A Fast & Accurate Oracle for Object Placement 2024 SIGMOD 5.227679e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

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