Summary of Slicing Input Features to Accelerate Deep Learning: a Case Study with Graph Neural Networks, by Zhengjia Xu et al.
Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks
by Zhengjia Xu, Dingyang Lyu, Jinghui Zhang
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 SliceGCN is a novel distributed graph learning method designed to enhance the scalability of Graph Neural Network (GNN) training for large-scale graphs. The existing sampling-based mini-batch and distributed graph learning methods suffer from performance degradation and heavy communication, which SliceGCN aims to overcome. It slices node features across multiple GPUs, allowing each GPU to handle a portion of the feature space. This approach avoids accuracy loss associated with mini-batch training and reduces inter-GPU communication during message passing. The paper proposes feature fusion and slice encoding techniques to mitigate potential accuracy reductions due to slicing. Experimental results on six node classification datasets show that SliceGCN improves efficiency on larger datasets, demonstrates better convergence, and exhibits potentially parameter-efficient nature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to train a computer program to understand complex data like social networks or traffic patterns. As the data gets bigger, it becomes harder for computers to process everything at once. SliceGCN is a new way to solve this problem. Instead of sending all the data to one computer, SliceGCN breaks the data into smaller pieces and processes them on multiple computers. This makes it possible to handle really big datasets without losing accuracy or speed. The researchers tested SliceGCN on six different datasets and found that it works better for larger datasets. It’s an important step towards making it easier for computers to learn from and understand complex data. |
Keywords
» Artificial intelligence » Classification » Gnn » Graph neural network » Parameter efficient
