  Dynamic Graph Neural Network (DGNN) has shown a strong capability of learning
dynamic graphs by exploiting both spatial and temporal features. Although DGNN
has recently received considerable attention by AI community and various DGNN
models have been proposed, building a distributed system for efficient DGNN
training is still challenging. It has been well recognized that how to
partition the dynamic graph and assign workloads to multiple GPUs plays a
critical role in training acceleration. Existing works partition a dynamic
graph into snapshots or temporal sequences, which only work well when the graph
has uniform spatio-temporal structures. However, dynamic graphs in practice are
not uniformly structured, with some snapshots being very dense while others are
sparse. To address this issue, we propose DGC, a distributed DGNN training
system that achieves a 1.25x - 7.52x speedup over the state-of-the-art in our
testbed. DGC's success stems from a new graph partitioning method that
partitions dynamic graphs into chunks, which are essentially subgraphs with
modest training workloads and few inter connections. This partitioning
algorithm is based on graph coarsening, which can run very fast on large
graphs. In addition, DGC has a highly efficient run-time, powered by the
proposed chunk fusion and adaptive stale aggregation techniques. Extensive
experimental results on 3 typical DGNN models and 4 popular dynamic graph
datasets are presented to show the effectiveness of DGC.
