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Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads

Summary: Formalizes SPASE: jointly select parallelism, allocate GPUs, and schedule multi-model DL experiments via a template plus an empirical profiler. Solves SPASE as an MILP (beats heuristics) with introspective scheduling in Saturn, reducing model-selection runtime by 39–49%. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13936
Venue
VLDB
Year
2024
Pagerank
5.227679e-05
Overall Rank
9,737 | 33.20%
DOI
10.14778/3636218.3636227

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{nagrecha_vldb24,
        title = {{Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads}},
        author = {Nagrecha, Kabir and Kumar, Arun},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {4},
        pages = {712--725},
        doi = {10.14778/3636218.3636227},
        url = {https://doi.org/10.14778/3636218.3636227},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,588 Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines 2023 VLDB 5.833338e-05
10,481 Performant Synchronization in Geo-Distributed Databases 2026 SIGMOD 5.093636e-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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