  Graph neural networks (GNN) have shown great success in learning from
graph-structured data. They are widely used in various applications, such as
recommendation, fraud detection, and search. In these domains, the graphs are
typically large, containing hundreds of millions of nodes and several billions
of edges. To tackle this challenge, we develop DistDGL, a system for training
GNNs in a mini-batch fashion on a cluster of machines. DistDGL is based on the
Deep Graph Library (DGL), a popular GNN development framework. DistDGL
distributes the graph and its associated data (initial features and embeddings)
across the machines and uses this distribution to derive a computational
decomposition by following an owner-compute rule. DistDGL follows a synchronous
training approach and allows ego-networks forming the mini-batches to include
non-local nodes. To minimize the overheads associated with distributed
computations, DistDGL uses a high-quality and light-weight min-cut graph
partitioning algorithm along with multiple balancing constraints. This allows
it to reduce communication overheads and statically balance the computations.
It further reduces the communication by replicating halo nodes and by using
sparse embedding updates. The combination of these design choices allows
DistDGL to train high-quality models while achieving high parallel efficiency
and memory scalability. We demonstrate our optimizations on both inductive and
transductive GNN models. Our results show that DistDGL achieves linear speedup
without compromising model accuracy and requires only 13 seconds to complete a
training epoch for a graph with 100 million nodes and 3 billion edges on a
cluster with 16 machines. DistDGL is now publicly available as part of
DGL:https://github.com/dmlc/dgl/tree/master/python/dgl/distributed.
