Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses

arXiv:2306.16384v2 Announce Type: replace-cross
Abstract: Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Although GNNs have been shown to be effective on modest-sized graphs, training them on large-scale graphs remains a significant challenge due to lack of efficient data access and data movement methods. Existing frameworks for training GNNs use CPUs for graph sampling and feature aggregation, while the training and updating of model …

abstract aggregation application arxiv cs.ai cs.ar cs.dc cs.lg data domains frameworks gnn gnns gpu graph graph neural networks graphs inference networks neural networks operations sampling scale storage structured data tasks them tool training type