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Cerebro: A Data System for Optimized Deep Learning Model Selection

Summary: Cerebro is a data system for optimized deep-learning model selection, boosting throughput and reproducibility at lower cost. Model hopper parallelism, a hybrid task/data-parallel SGD, yields 3–10x speedups and memory/network savings across varied resources. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hd8941b74afeb7d78
Venue
VLDB
Year
2020
Pagerank
0.00011762688
Overall Rank
1,154 | 92.27%
DOI
10.14778/3407790.3407816
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{nakandala_vldb20,
        title = {{Cerebro: A Data System for Optimized Deep Learning Model Selection}},
        author = {Nakandala, Supun and Zhang, Yuhao and Kumar, Arun},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {2159--2173},
        doi = {10.14778/3407790.3407816},
        url = {https://doi.org/10.14778/3407790.3407816},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 27 of 27 citing papers.

Rank Citing Paper Year Venue Pagerank
1,472 Analyzing and Mitigating Data Stalls in DNN Training 2021 VLDB 0.00010529036
2,341 SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging 2021 SIGMOD 8.5984124e-05
3,340 VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition 2021 VLDB 7.3886578e-05
4,117 Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches 2021 VLDB 6.784003e-05
5,011 Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism 2023 VLDB 6.3030066e-05
5,230 Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications 2023 SIGMOD 6.2059854e-05
5,916 Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines 2023 VLDB 5.9320442e-05
5,945 Doing More with Less: Characterizing Dataset Downsampling for AutoML 2021 VLDB 5.9197358e-05
6,474 In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle 2022 SIGMOD 5.7555547e-05
6,679 UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads 2022 VLDB 5.6956362e-05
7,833 Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets 2022 SIGMOD 5.4285082e-05
8,298 Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale 2022 VLDB 5.3437127e-05
8,435 SHiFT: An Efficient, Flexible Search Engine for Transfer Learning 2023 VLDB 5.3161281e-05
9,097 TensorSocket: Shared Data Loading for Deep Learning Training 2026 SIGMOD 5.208561e-05
9,175 Cerebro: A Layered Data Platform for Scalable Deep Learning 2021 CIDR 5.2004231e-05
9,495 COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression 2022 VLDB 5.1540266e-05
9,589 Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning 2021 VLDB 5.1377385e-05
9,590 Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration 2021 VLDB 5.1377385e-05
10,017 The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format 2024 SIGMOD 5.0825892e-05
10,869 NeurIDA: Dynamic Modeling for Effective In-Database Analytics 2026 VLDB 4.9605343e-05
11,485 ML-Asset Management: Curation, Discovery, and Utilization 2025 VLDB 4.9605343e-05
11,604 Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems 2024 VLDB 4.9605343e-05
11,896 Redundancy Elimination in Distributed Matrix Computation 2022 SIGMOD 4.9605343e-05
11,986 Ease.ML: A Lifecycle Management System for MLDev and MLOps 2021 CIDR 4.9605343e-05
12,002 Grouped Learning: Group-By Model Selection Workloads 2021 SIGMOD 4.9605343e-05
13,734 Reimagining Deep Learning Systems Through the Lens of Data Systems 2024 VLDB -
13,832 Errata for "Cerebro: A Data System for Optimized Deep Learning Model Selection" 2021 VLDB -
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

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