Summary of Single-gpu Gnn Systems: Traps and Pitfalls, by Yidong Gong et al.
Single-GPU GNN Systems: Traps and Pitfalls
by Yidong Gong, Arnab Tarafder, Saima Afrin, Pradeep Kumar
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper investigates the current trend in graph neural networks (GNNs), where researchers often omit training accuracy results and rely on smaller datasets for evaluations. The authors analyze this phenomenon, revealing a chain of pitfalls in system design and evaluation that question the practicality of many proposed optimizations. They develop hypotheses, recommendations, and evaluation methodologies to address these issues, providing future directions. To establish a new line of optimizations, the authors introduce a reference system that efficiently solves system-design pitfalls. This work has the potential to advance the state-of-the-art in GNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how scientists are designing graph neural networks (GNNs). They noticed that some researchers don’t share their results and instead use smaller datasets to test their ideas. The authors studied this problem and found that it can lead to mistakes when designing new systems. They came up with solutions, suggestions for future research, and ways to evaluate GNNs better. Their work could help improve the state-of-the-art in GNNs. |
