DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Hongkuan Zhou Da Zheng Xiang Song George Karypis Viktor Prasanna
Memory-based Temporal Graph Neural Networks are powerful tools in dynamic
graph representation learning and have demonstrated superior performance in
many real-world applications. However, their node memory favors smaller batch
sizes to capture more dependencies in graph events and needs to be maintained
synchronously across all trainers. As a result, existing frameworks suffer from
accuracy loss when scaling to multiple GPUs. Evenworse, the tremendous overhead
to synchronize the node memory make it impractical to be deployed to
distributed GPU clusters. In this work, we propose DistTGL -- an efficient and
scalable solution to train memory-based TGNNs on distributed GPU clusters.
DistTGL has three improvements over existing solutions: an enhanced TGNN model,
a novel training algorithm, and an optimized system. In experiments, DistTGL
achieves near-linear convergence speedup, outperforming state-of-the-art
single-machine method by 14.5% in accuracy and 10.17x in training throughput.