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: 13748
Venue: VLDB
Year: 2024
Pagerank: 4.3177432e-05
Overall Rank: 9,603 | 33.20%
DOI: 10.14778/3636218.3636227

Incoming Non-self Citations Over Time

Authors

1. Kabir Nagrecha
2. Arun Kumar

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,884	Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines	2023	VLDB	4.8955332e-05
10,192	Performant Synchronization in Geo-Distributed Databases	2026	SIGMOD	4.1945683e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank
183	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036721403
411	PyTorch Distributed: Experiences on Accelerating Data Parallel Training	2020	VLDB	0.00023906921
1,071	Starfish: A Self-tuning System for Big Data Analytics	2011	CIDR	0.00014312777
5,988	NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access	2022	SIGMOD	5.2430981e-05
8,864	Cerebro: A Layered Data Platform for Scalable Deep Learning	2021	CIDR	4.4326439e-05
9,264	Model-Parallel Model Selection for Deep Learning Systems	2021	SIGMOD	4.3675421e-05

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