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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
11592
Venue
VLDB
Year
2017
Pagerank
6.2244283e-05
Overall Rank
4,395 | 69.43%
DOI
-

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