Graph Computing

Graph Neural Network (GNN) has emerged as an important workload for learning on graphs. With the size of graph data and the complexity of GNN model architectures increasing, developing an efficient GNN system grows more important.

GraphSet is a pattern-aware graph mining system supporting both CPU and GPU. GraphSet achieves high performance by proposing a set-based equivalent transformation approach to optimize pattern-aware graph mining applications, which can leverage classic set properties to eliminate most control flows and reduce computation overhead exponentially.

Graph Neural Network (GNN) has recently drawn a rapid increase of interest in many domains for its effectiveness in learning over graphs. Maximizing its performance is essential for many tasks, but remains preliminarily understood.

Graph pattern matching, which aims to discover structural patterns in graphs, is considered one of the most fundamental graph mining problems in many real applications. Despite previous efforts, existing systems face two main challenges.