  Graph Convolutional Networks (GCNs) are extensively utilized for deep
learning on graphs. The large data sizes of graphs and their vertex features
make scalable training algorithms and distributed memory systems necessary.
Since the convolution operation on graphs induces irregular memory access
patterns, designing a memory- and communication-efficient parallel algorithm
for GCN training poses unique challenges. We propose a highly parallel training
algorithm that scales to large processor counts. In our solution, the large
adjacency and vertex-feature matrices are partitioned among processors. We
exploit the vertex-partitioning of the graph to use non-blocking point-to-point
communication operations between processors for better scalability. To further
minimize the parallelization overheads, we introduce a sparse matrix
partitioning scheme based on a hypergraph partitioning model for full-batch
training. We also propose a novel stochastic hypergraph model to encode the
expected communication volume in mini-batch training. We show the merits of the
hypergraph model, previously unexplored for GCN training, over the standard
graph partitioning model which does not accurately encode the communication
costs. Experiments performed on real-world graph datasets demonstrate that the
proposed algorithms achieve considerable speedups over alternative solutions.
The optimizations achieved on communication costs become even more pronounced
at high scalability with many processors. The performance benefits are
preserved in deeper GCNs having more layers as well as on billion-scale graphs.
