Methods › General › Asynchronous Data Parallel › Crossbow
Crossbow
Introduced by Alexandros Koliousis et al. in CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Crossbow is a single-server multi-GPU system for training deep learning models that enables users to freely choose their preferred batch size—however small—while scaling to multiple GPUs. Crossbow uses many parallel model replicas and avoids reduced statistical efficiency through a new synchronous training method. SMA, a synchronous variant of model averaging, is used in which replicas independently explore the solution space with gradient descent, but adjust their search synchronously based on the trajectory of a globally-consistent average model.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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A Low-Delay MAC for IoT Applications: Decentralized Optimal Scheduling of Queues without Explicit State Information Sharing 24 May 2021 · 0 repositories · arXiv:2105.11213
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CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers 8 Jan 2019 · 1 repository · arXiv:1901.02244
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Deep Learning | 1 |
| Fairness | 1 |
| GPU | 1 |
| Scheduling | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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