InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph
  Neural Network over Huge Graphs
Dalong Zhang Xianzheng Song Zhiyang Hu Yang Li Miao Tao Binbin Hu Lin Wang Zhiqiang Zhang Jun Zhou
GNN inference is a non-trivial task, especially in industrial scenarios with
giant graphs, given three main challenges, i.e., scalability tailored for
full-graph inference on huge graphs, inconsistency caused by stochastic
acceleration strategies (e.g., sampling), and the serious redundant computation
issue. To address the above challenges, we propose a scalable system named
InferTurbo to boost the GNN inference tasks in industrial scenarios. Inspired
by the philosophy of ``think-like-a-vertex", a GAS-like (Gather-Apply-Scatter)
schema is proposed to describe the computation paradigm and data flow of GNN
inference. The computation of GNNs is expressed in an iteration manner, in
which a vertex would gather messages via in-edges and update its state
information by forwarding an associated layer of GNNs with those messages and
then send the updated information to other vertexes via out-edges. Following
the schema, the proposed InferTurbo can be built with alternative backends
(e.g., batch processing system or graph computing system). Moreover, InferTurbo
introduces several strategies like shadow-nodes and partial-gather to handle
nodes with large degrees for better load balancing. With InferTurbo, GNN
inference can be hierarchically conducted over the full graph without sampling
and redundant computation. Experimental results demonstrate that our system is
robust and efficient for inference tasks over graphs containing some hub nodes
with many adjacent edges. Meanwhile, the system gains a remarkable performance
compared with the traditional inference pipeline, and it can finish a GNN
inference task over a graph with tens of billions of nodes and hundreds of
billions of edges within 2 hours.