MetaStore: Analyzing Deep Learning Meta-Data at Scale
Summary: MetaStore stores compact backprop intermediates—prefix and suffix gradients—that exactly reconstruct full model gradients, drastically reducing gradient size. It runs gradient analytics directly on these compact structures, achieving 4–678× storage and 2–1000× runtime gains on VGG/BERT/ResNet. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Huayi Zhang (Worcester Polytechnic Institute)
- 2. Binwei Yan (Massachusetts Institute of Technology)
- 3. Lei Cao (Massachusetts Institute of Technology; University of Arizona)
- 4. Samuel Madden (Massachusetts Institute of Technology)
- 5. Elke Rundensteiner (Worcester Polytechnic Institute)
BibTeX Citation
@article{zhang_vldb24,
title = {{MetaStore: Analyzing Deep Learning Meta-Data at Scale}},
author = {Zhang, Huayi and Yan, Binwei and Cao, Lei and Madden, Samuel and Rundensteiner, Elke},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {6},
pages = {1446--1459},
doi = {10.14778/3648160.3648182},
url = {https://doi.org/10.14778/3648160.3648182},
year = {2024}
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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.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,692 | MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis | 2018 | SIGMOD | 9.8526476e-05 |
| 2,600 | Complaint-driven Training Data Debugging for Query 2.0 | 2020 | SIGMOD | 8.2346824e-05 |
| 4,689 | A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System | 2022 | VLDB | 6.4677473e-05 |
| 6,208 | FACET: Robust Counterfactual Explanation Analytics | 2023 | SIGMOD | 5.8479648e-05 |
| 7,102 | DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation | 2022 | VLDB | 5.5991152e-05 |
| 9,044 | LANCET: Labeling Complex Data at Scale | 2021 | VLDB | 5.2308178e-05 |
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