  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 weights are executed on
GPUs. However, our in-depth profiling shows the CPUs cannot achieve the
throughput required to saturate GNN model training throughput, causing gross
under-utilization of expensive GPU resources. Furthermore, when the graph and
its embeddings do not fit in the CPU memory, the overhead introduced by the
operating system, say for handling page-faults, comes in the critical path of
execution.
  To address these issues, we propose the GPU Initiated Direct Storage Access
(GIDS) dataloader, to enable GPU-oriented GNN training for large-scale graphs
while efficiently utilizing all hardware resources, such as CPU memory,
storage, and GPU memory with a hybrid data placement strategy. By enabling GPU
threads to fetch feature vectors directly from storage, GIDS dataloader solves
the memory capacity problem for GPU-oriented GNN training. Moreover, GIDS
dataloader leverages GPU parallelism to tolerate storage latency and eliminates
expensive page-fault overhead. Doing so enables us to design novel
optimizations for exploiting locality and increasing effective bandwidth for
GNN training. Our evaluation using a single GPU on terabyte-scale GNN datasets
shows that GIDS dataloader accelerates the overall DGL GNN training pipeline by
up to 392X when compared to the current, state-of-the-art DGL dataloader.
