Scalable Asynchronous Gradient Descent Optimization for Out-of-Core Models
Summary: Scales asynchronous SGD for out-of-core models via vertical offline partitioning of the model and online updates. A preemptive push-based sharing mechanism minimizes disk I/O, delivering improved convergence over HOGWILD! and enabling scalability to massive models. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chengjie Qin (GraphSQL)
- 2. Martin Torres (University of California Merced)
- 3. Florin Rusu (University of California Merced)
BibTeX Citation
@article{qin_vldb17,
title = {{Scalable Asynchronous Gradient Descent Optimization for Out-of-Core Models}},
author = {Qin, Chengjie and Torres, Martin and Rusu, Florin},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {10},
pages = {986--997},
doi = {10.14778/3115404.3115405},
url = {https://doi.org/10.14778/3115404.3115405},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,693 | FlexPS: Flexible Parallelism Control in Parameter Server Architecture | 2018 | VLDB | 8.247004e-05 |
| 3,782 | CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers | 2019 | VLDB | 7.1298979e-05 |
| 6,485 | Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent | 2019 | SIGMOD | 5.8657457e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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