Summary of Mspipe: Efficient Temporal Gnn Training Via Staleness-aware Pipeline, by Guangming Sheng et al.
MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline
by Guangming Sheng, Junwei Su, Chao Huang, Chuan Wu
First submitted to arxiv on: 23 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 proposes MSPipe, a novel framework for Memory-based Temporal Graph Neural Networks (MTGNNs) that maximizes training throughput while maintaining model accuracy. MTGNNs utilize node memory modules to capture long-term temporal dependencies, but the iterative reading and updating process introduces significant overhead. The proposed framework addresses this challenge by integrating staleness into the memory module and introducing an online pipeline scheduling algorithm that strategically breaks temporal dependencies with minimal staleness. Additionally, a staleness mitigation mechanism is designed to enhance training convergence and model accuracy. Experimental results show that MSPipe achieves up to 2.45x speed-up without sacrificing accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MSPipe is a new way to make memory-based temporal graph neural networks (MTGNNs) faster and more efficient. These special kinds of neural networks are good at learning from data that has patterns over time, like how people interact with each other online. But making them work fast enough was a problem. The creators of MSPipe came up with a solution by adding something called staleness to the memory module, which helps break up the patterns and make it easier for the network to learn. They also added a scheduling algorithm that decides when to grab new information and when to use old information. This makes the whole process faster and more accurate. It’s like having a superpower for your neural networks! |
