  Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale
graph-based recommender systems. Training GCN requires the minibatch generator
traversing graphs and sampling the sparsely located neighboring nodes to obtain
their features. Since real-world graphs often exceed the capacity of GPU
memory, current GCN training systems keep the feature table in host memory and
rely on the CPU to collect sparse features before sending them to the GPUs.
This approach, however, puts tremendous pressure on host memory bandwidth and
the CPU. This is because the CPU needs to (1) read sparse features from memory,
(2) write features into memory as a dense format, and (3) transfer the features
from memory to the GPUs. In this work, we propose a novel GPU-oriented data
communication approach for GCN training, where GPU threads directly access
sparse features in host memory through zero-copy accesses without much CPU
help. By removing the CPU gathering stage, our method significantly reduces the
consumption of the host resources and data access latency. We further present
two important techniques to achieve high host memory access efficiency by the
GPU: (1) automatic data access address alignment to maximize PCIe packet
efficiency, and (2) asynchronous zero-copy access and kernel execution to fully
overlap data transfer with training. We incorporate our method into PyTorch and
evaluate its effectiveness using several graphs with sizes up to 111 million
nodes and 1.6 billion edges. In a multi-GPU training setup, our method is
65-92% faster than the conventional data transfer method, and can even match
the performance of all-in-GPU-memory training for some graphs that fit in GPU
memory.
