Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 2 – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-03-25T16:00:00Z http://www.open-lab.net/blog/feed/ Dongxu Yang <![CDATA[Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 2]]> http://www.open-lab.net/blog/?p=80232 2024-04-18T20:13:55Z 2024-04-03T22:24:10Z Large-scale graph neural network (GNN) training presents formidable challenges, particularly concerning the scale and complexity of graph data. These challenges...]]> Large-scale graph neural network (GNN) training presents formidable challenges, particularly concerning the scale and complexity of graph data. These challenges...Decorative image of graphs as light web.

Large-scale graph neural network (GNN) training presents formidable challenges, particularly concerning the scale and complexity of graph data. These challenges extend beyond the typical concerns of neural network forward and backward computations, encompassing issues such as bandwidth-intensive graph feature gathering and sampling, and the limitations of single GPU capacities.

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