GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and Retrieval

• URL: http://arxiv.org/abs/2406.17918v3
• Date: Tue, 19 Nov 2024 18:24:03 GMT
• Title: GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and Retrieval
• Authors: Dong Liu, Roger Waleffe, Meng Jiang, Shivaram Venkataraman,
• Abstract summary: GraphSnapShot is a framework for fast cache, storage, retrieval and computation for graph learning. In experiments, GraphSnapShot shows efficiency, it can achieve up to 30% training acceleration and 73% memory reduction.
• Score: 19.225957670728622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In our recent research, we have developed a framework called GraphSnapShot, which has been proven an useful tool for graph learning acceleration. GraphSnapShot is a framework for fast cache, storage, retrieval and computation for graph learning. It can quickly store and update the local topology of graph structure and allows us to track patterns in the structure of graph networks, just like take snapshots of the graphs. In experiments, GraphSnapShot shows efficiency, it can achieve up to 30% training acceleration and 73% memory reduction for lossless graph ML training compared to current baselines such as dgl.This technique is particular useful for large dynamic graph learning tasks such as social media analysis and recommendation systems to process complex relationships between entities. The code for GraphSnapShot is publicly available at https://github.com/NoakLiu/GraphSnapShot.

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