  Distributed training of GNNs enables learning on massive graphs (e.g., social
and e-commerce networks) that exceed the storage and computational capacity of
a single machine. To reach performance comparable to centralized training,
distributed frameworks focus on maximally recovering cross-instance node
dependencies with either communication across instances or periodic fallback to
centralized training, which create overhead and limit the framework
scalability. In this work, we present a simplified framework for distributed
GNN training that does not rely on the aforementioned costly operations, and
has improved scalability, convergence speed and performance over the
state-of-the-art approaches. Specifically, our framework (1) assembles
independent trainers, each of which asynchronously learns a local model on
locally-available parts of the training graph, and (2) only conducts periodic
(time-based) model aggregation to synchronize the local models. Backed by our
theoretical analysis, instead of maximizing the recovery of cross-instance node
dependencies -- which has been considered the key behind closing the
performance gap between model aggregation and centralized training -- , our
framework leverages randomized assignment of nodes or super-nodes (i.e.,
collections of original nodes) to partition the training graph such that it
improves data uniformity and minimizes the discrepancy of gradient and loss
function across instances. In our experiments on social and e-commerce networks
with up to 1.3 billion edges, our proposed RandomTMA and SuperTMA approaches --
despite using less training data -- achieve state-of-the-art performance and
2.31x speedup compared to the fastest baseline, and show better robustness to
trainer failures.
