Minisymposium Presentation

This talk will focus on distributed-memory parallel algorithms for graph neural network (GNN) training. We will first focus on utilizing sparse matrix primitives to parallelize mini-batch training based on node-wise and layer-wise sampling. Then, we will illustrate techniques that are based on sparsity-aware sparse matrix times dense matrix multiplication algorithms to accelerate both full-graph and mini-batch sampling based training.