  As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have
received increasing attention in both academia and industry. Nevertheless, it
is notoriously difficult to deploy GNNs on industrial scale graphs, due to
their huge data size and complex topological structures. In this paper, we
propose GLISP, a sampling based GNN learning system for industrial scale
graphs. By exploiting the inherent structural properties of graphs, such as
power law distribution and data locality, GLISP addresses the scalability and
performance issues that arise at different stages of the graph learning
process. GLISP consists of three core components: graph partitioner, graph
sampling service and graph inference engine. The graph partitioner adopts the
proposed vertex-cut graph partitioning algorithm AdaDNE to produce balanced
partitioning for power law graphs, which is essential for sampling based GNN
systems. The graph sampling service employs a load balancing design that allows
the one hop sampling request of high degree vertices to be handled by multiple
servers. In conjunction with the memory efficient data structure, the
efficiency and scalability are effectively improved. The graph inference engine
splits the $K$-layer GNN into $K$ slices and caches the vertex embeddings
produced by each slice in the data locality aware hybrid caching system for
reuse, thus completely eliminating redundant computation caused by the data
dependency of graph. Extensive experiments show that GLISP achieves up to
$6.53\times$ and $70.77\times$ speedups over existing GNN systems for training
and inference tasks, respectively, and can scale to the graph with over 10
billion vertices and 40 billion edges with limited resources.
