GraphSnapShot

[NoakLiu]

🚀 Feature

GraphSnapShot: A framework for caching local structure to enable fast and efficient graph learning.

Motivation

Traditional graph learning methods waste significant resources and time by repeatedly resampling and refetching neighbors during each training iteration, which is computationally intensive and resource-consuming.

GraphSnapShot provides up to training acceleration and memory reduction without compromising graph machine learning performance.

GraphSnapShot is a framework designed for caching the local structure of graphs, enabling fast storage, retrieval, and computation for large-scale graph learning tasks. By "taking snapshots" of graph structures, it facilitates efficient updates and quick access to local topologies, optimizing the learning process.

Alternatives

GraphSnapShot serves as an alternative to traditional neighbor-sampling approaches, offering significant advantages in terms of speed and memory usage.

Paper: https://arxiv.org/abs/2406.17918
Code implementation: https://github.com/NoakLiu/GraphSnapShot
DGL acceleration module: https://github.com/NoakLiu/GraphSnapShot/tree/main/examples/dgl/

Pitch

1. GPU Memory Reduction: Significantly lower computational resource usage.
2. Training Acceleration: Faster model training through efficient graph caching and updates.

Additional context

GraphSnapShot offers practical solutions for handling large-scale graphs in machine learning, enabling both performance optimization and resource efficiency.