Summary of Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study, by Nikolai Merkel et al.
Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study
by Nikolai Merkel, Pierre Toussing, Ruben Mayer, Hans-Arno Jacobsen
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB); Performance (cs.PF)
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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 abstract presents a research paper on graph neural networks (GNNs) that focuses on optimizing their training process. GNNs are designed to learn from graph-structured data, but the iterative aggregation of high-dimensional features from neighboring vertices can be computationally expensive. The authors investigate the impact of graph reordering, an optimization strategy commonly used in graph analytics workloads, on the performance of GNN training. They consider various aspects that affect GNN performance, including hyperparameters, neural network operations, intermediate vertex states, and GPU acceleration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how to improve the training process of graph neural networks (GNNs) by reordering the graph data. GNNs are a type of artificial intelligence model that can learn from data with connections between pieces of information. Training these models on big datasets is difficult because they have to combine lots of information together many times. The authors want to see if changing the order of this data helps make training faster. |
Keywords
» Artificial intelligence » Gnn » Neural network » Optimization
