Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 2

Originally published at: https://developer.nvidia.com/blog/optimizing-memory-and-retrieval-for-graph-neural-networks-with-wholegraph-part-2/

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.  In my previous post, I introduced‚Ķ