Presentation
FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs
DescriptionTraining Graph Neural Networks(GNNs) on a large monolithic graph presents unique challenges as the graph cannot fit within a single machine and it cannot be decomposed into smaller disconnected components. Distributed sampling-based training distributes the graph across multiple machines and trains the GNN on small parts of the graph that are randomly sampled every training iteration. We show that in a distributed environment, the sampling overhead is a significant component of the training time for large-scale graphs. We propose FastSample which is composed of two synergistic techniques that greatly reduce the distributed sampling time: 1)~a new graph partitioning method that eliminates most of the communication rounds in distributed sampling , 2)~a novel highly optimized sampling kernel that reduces memory movement during sampling. We test FastSample on large-scale graph benchmarks and show that FastSample speeds up distributed sampling-based GNN training by up to 2x with no loss in accuracy.
Event Type
Work-in-Progress Poster
TimeTuesday, June 256:00pm - 7:00pm PDT
LocationLevel 2 Lobby
AI
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