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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)

Paper ID
11780
Venue
VLDB
Year
2017
Pagerank
6.5024714e-05
Overall Rank
4,799 | 67.08%
DOI
10.14778/3115404.3115405

Incoming Non-self Citations Over Time

Authors

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}
}

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