In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

In the big data era, much real-world data can be naturally represented as graphs.
Consequently, many application domains can be modeled as graph processing. Graph …

Graph Neural Networks (GNNs) have gained significant attention in the recent past, and
become one of the fastest growing subareas in deep learning. While several new GNN …

Graph neural networks (GNNs) have shown excellent performance in a wide range of
applications such as recommendation, risk control, and drug discovery. With the increase in …

For large-scale graph analytics on the GPU, the irregularity of data access/control flow and
the complexity of programming GPUs have been two significant challenges for develo** a …

Graph neural networks (GNNs) emerge as a powerful approach to process non-euclidean
data structures and have been proved powerful in various application domains such as …

As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel
for their capability to generate high-quality node feature vectors (embeddings). However, the …

Processing a one trillion-edge graph has recently been demonstrated by distributed graph
engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single …

Graph neural networks (GNNs) are gaining popularity as a promising approach to machine
learning on graphs. Unlike traditional graph workloads where each vertex/edge is …

Approximate nearest neighbor (ANN) searching is a fundamental problem in computer
science with numerous applications in (eg,) machine learning and data mining. Recent …