Back to papers
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
- 12106
- Venue
- VLDB
- Year
- 2020
- Pagerank
- 0.00018195476
- Overall Rank
- 683 | 95.26%
- DOI
-
10.14778/3407790.3407816
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 26 of 26 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 1,504 |
Analyzing and Mitigating Data Stalls in DNN Training |
2021 |
VLDB |
0.00011642333 |
| 1,940 |
SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging |
2021 |
SIGMOD |
0.00010020173 |
| 2,839 |
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition |
2021 |
VLDB |
8.0378978e-05 |
| 4,557 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
6.087611e-05 |
| 4,957 |
Doing More with Less: Characterizing Dataset Downsampling for AutoML |
2021 |
VLDB |
5.8035715e-05 |
| 5,084 |
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle |
2022 |
SIGMOD |
5.7091191e-05 |
| 6,377 |
Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism |
2023 |
VLDB |
5.0911095e-05 |
| 6,884 |
Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines |
2023 |
VLDB |
4.8955332e-05 |
| 7,656 |
Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets |
2022 |
SIGMOD |
4.6871575e-05 |
| 8,092 |
Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications |
2023 |
SIGMOD |
4.587921e-05 |
| 8,182 |
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning |
2023 |
VLDB |
4.5659133e-05 |
| 8,514 |
UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads |
2022 |
VLDB |
4.4944285e-05 |
| 8,735 |
TensorSocket: Shared Data Loading for Deep Learning Training |
2026 |
SIGMOD |
4.456315e-05 |
| 8,864 |
Cerebro: A Layered Data Platform for Scalable Deep Learning |
2021 |
CIDR |
4.4326439e-05 |
| 9,192 |
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale |
2022 |
VLDB |
4.3765131e-05 |
| 9,222 |
Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning |
2021 |
VLDB |
4.3698672e-05 |
| 9,223 |
Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration |
2021 |
VLDB |
4.3698672e-05 |
| 9,265 |
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression |
2022 |
VLDB |
4.3667558e-05 |
| 9,806 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
4.2805224e-05 |
| 10,842 |
ML-Asset Management: Curation, Discovery, and Utilization |
2025 |
VLDB |
4.1945683e-05 |
| 10,998 |
Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems |
2024 |
VLDB |
4.1945683e-05 |
| 11,339 |
Redundancy Elimination in Distributed Matrix Computation |
2022 |
SIGMOD |
4.1945683e-05 |
| 11,431 |
Ease.ML: A Lifecycle Management System for MLDev and MLOps |
2021 |
CIDR |
4.1945683e-05 |
| 11,447 |
Grouped Learning: Group-By Model Selection Workloads |
2021 |
SIGMOD |
4.1945683e-05 |
| 13,171 |
Reimagining Deep Learning Systems Through the Lens of Data Systems |
2024 |
VLDB |
- |
| 13,271 |
Errata for “Cerebro: A Data System for Optimized Deep Learning Model Selection” |
2021 |
VLDB |
- |
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.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 140 |
The MADlib Analytics Library or MAD Skills, the SQL |
2012 |
VLDB |
0.00042270404 |
| 658 |
Towards a Unified Architecture for in-RDBMS Analytics |
2012 |
SIGMOD |
0.00018506577 |
| 1,402 |
Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML |
2014 |
VLDB |
0.00012180605 |
| 1,532 |
Data Management in Machine Learning: Challenges, Techniques, and Systems |
2017 |
SIGMOD |
0.00011472681 |
| 1,942 |
Heterogeneity-aware Distributed Parameter Servers |
2017 |
SIGMOD |
0.00010012691 |
| 2,440 |
FlexPS: Flexible Parallelism Control in Parameter Server Architecture |
2018 |
VLDB |
8.8119143e-05 |
| 2,863 |
Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations |
2019 |
SIGMOD |
7.9877991e-05 |
| 2,886 |
VISTA: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale |
2020 |
SIGMOD |
7.9612767e-05 |
| 3,363 |
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers |
2019 |
VLDB |
7.1731921e-05 |
| 3,948 |
A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics |
2018 |
VLDB |
6.5959084e-05 |
| 4,263 |
Mariana: Tencent Deep Learning Platform and its Applications |
2014 |
VLDB |
6.3106444e-05 |
| 6,538 |
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent |
2019 |
SIGMOD |
5.023239e-05 |
| 13,271 |
Errata for “Cerebro: A Data System for Optimized Deep Learning Model Selection” |
2021 |
VLDB |
- |
| 13,313 |
Demonstration of Krypton: Optimized CNN Inference for Occlusion-based Deep CNN Explanations |
2019 |
VLDB |
- |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 9,603 |
Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads |
2024 |
VLDB |
4.3177432e-05 |
| 9,170 |
MemFlow: Memory-Aware Distributed Deep Learning |
2020 |
SIGMOD |
4.3849075e-05 |
| 8,735 |
TensorSocket: Shared Data Loading for Deep Learning Training |
2026 |
SIGMOD |
4.456315e-05 |
| 13,271 |
Errata for “Cerebro: A Data System for Optimized Deep Learning Model Selection” |
2021 |
VLDB |
- |
| 9,222 |
Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning |
2021 |
VLDB |
4.3698672e-05 |
| 3,363 |
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers |
2019 |
VLDB |
7.1731921e-05 |
| 9,264 |
Model-Parallel Model Selection for Deep Learning Systems |
2021 |
SIGMOD |
4.3675421e-05 |
| 9,223 |
Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration |
2021 |
VLDB |
4.3698672e-05 |
| 4,557 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
6.087611e-05 |
| 8,864 |
Cerebro: A Layered Data Platform for Scalable Deep Learning |
2021 |
CIDR |
4.4326439e-05 |