Year: 2024
Type: conference-paper
Abstract: Scaling Graph Neural Network (GNN) training on large-scale graph data poses a critical challenge for implementing GNN applications on real-world giant graphs. The size of real-world graphs often exceeds the memory capacity of accelerator devices, necessitating the use of multiple devices or host memory for training. While expanding memory space alleviates the out-of-memory problem, this approach introduces another bottleneck through heavy communication via low-bandwidth interconnection. Therefore, achieving efficient, scalable GNN training on large-scale graphs requires addressing both capacity and communication issues.
Authors: Yunki Han, Jaekang Shin, Gunhee Park, Lee-Sup Kim
Institution: Korea Advanced Institute of Science and Technology
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G6962113442
OpenAlex ID: W4409282574