Methods › General › Distributed Methods › Dorylus
Dorylus
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Dorylus is a distributed system for training graph neural networks which uses cheap CPU servers and Lambda threads. It scales to large billion-edge graphs with low-cost cloud resources.
Papers archive 2025-07-28
1 shown of 1, 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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Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads 24 May 2021 · 1 repository · arXiv:2105.11118
Tasks archive 2025-07-28
3 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 |
|---|---|
| CPU | 1 |
| GPU | 1 |
| Graph Neural Network | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections