Distributed processing of large-scale graph data has many practical applications and has
been widely studied. In recent years, a lot of distributed graph processing frameworks and …

Graph mining, which finds specific patterns in the graph, is becoming increasingly important
in various domains. We point out that accelerating graph mining suffers from the following …

The latest hardware accelerators proposed for graph applications primarily focus on graph
neural networks (GNNs) and graph mining. High-level graph reasoning tasks, such as graph …

Graph Pattern Mining (GPM) algorithms mine structural patterns in graphs. The performance
of GPM workloads is bottlenecked by control flow and memory stalls. This is because of data …

Graph mining is an emerging application of high importance and also with high complexity,
thus requiring efficient hardware acceleration. Current accelerator designs only utilize …

Graph convolutional networks (GCNs) are becoming increasingly popular as they overcome
the limited applicability of prior neural networks. One recent trend in GCNs is the use of deep …

Accelerating matrix multiplication is crucial to achieve high performance in many application
domains, including neural networks, graph analytics, and scientific computing. These …

Graph Pattern Mining (GPM) is a class of algorithms that identifies given shapes within a
graph, eg, cliques of a certain size. Any area of a graph can contain a shape of interest, but …

The slowing and forecasted end of Moore's Law have forced designers to look beyond
simply adding transistors, encouraging them to employ other unused resources as a manner …

In this paper, we build a-, a high-performance graph processing system specific for a triangle
counting algorithm on graph data with up to tens of billions of edges, which significantly …