Summary of Distributed Training Of Large Graph Neural Networks with Variable Communication Rates, by Juan Cervino et al.
Distributed Training of Large Graph Neural Networks with Variable Communication Rates
by Juan Cervino, Md Asadullah Turja, Hesham Mostafa, Nageen Himayat, Alejandro Ribeiro
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a variable compression scheme for reducing data communication between machines during distributed training of Graph Neural Networks (GNNs) on large graphs, without sacrificing model accuracy. The method is based on theoretical analysis and converges to the full communication case for all graph partitioning schemes. Experimental results show that it achieves comparable performance to full communication at a fixed compression ratio, outperforming full communication at any given compression ratio. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in training Graph Neural Networks (GNNs) on big graphs by sharing data between many computers. This makes the training slower because the computers have to share information. The solution is a way to compress this shared information so that it uses less space, but still keeps the same accuracy as before. It’s like taking a picture and reducing its size without losing important details. |
