Summary of Glisp: a Scalable Gnn Learning System by Exploiting Inherent Structural Properties Of Graphs, By Zhongshu Zhu et al.
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
by Zhongshu Zhu, Bin Jing, Xiaopei Wan, Zhizhen Liu, Lei Liang, Jun zhou
First submitted to arxiv on: 6 Jan 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 The paper proposes GLISP, a system that addresses the scalability issues of Graph Neural Networks (GNNs) on industrial-scale graphs. GNNs are powerful tools for modeling graph data, but they struggle with large datasets and complex topological structures. GLISP consists of three components: a graph partitioner, a graph sampling service, and a graph inference engine. The system exploits structural properties of graphs to improve performance and scalability. The results show that GLISP achieves significant speedups over existing GNN systems, up to 6.53x for training and 70.77x for inference tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GLISP is a new way to use Graph Neural Networks (GNNs) with big data. GNNs are great at learning from graphs, but they can get stuck when the graph is too big or complicated. GLISP helps by breaking down the problem into smaller parts and using special tricks to make it faster. It’s like having a superpower that lets you work with huge amounts of data! |
Keywords
* Artificial intelligence * Gnn * Inference
