Graph neural networks (GNNs) have gained increasing popularity in many areas such as e-
commerce, social networks and bio-informatics. Distributed GNN training is essential for …

Because of the importance of graph workloads and the limitations of CPUs/GPUs, many
graph processing accelerators have been proposed. The basic approach of prior …

In many graph-based applications, the graphs tend to grow, imposing a great challenge for
GPU-based graph processing. When the graph size exceeds the device memory capacity …

Modern analytics and recommendation systems are increasingly based on graph data that
capture the relations between entities being analyzed. Practical graphs come in huge sizes …

This paper demonstrates G3, a framework for Graph Neural Network (GNN) training, tailored
from Graph processing systems on Graphics processing units (GPUs). G3 aims at improving …

Today's GPU graph processing frameworks face scalability and efficiency issues as the
graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe …

Many graph processing systems have been recently developed for many-core processors.
However, for iterative graph processing, due to the dependencies between vertices' states …

The recent scale-up of GPU hardware through the integration of multiple GPUs into a single
machine and the introduction of higher bandwidth interconnects like NVLink 2.0 has …

In concurrent graph processing, different queries are evaluated on the same graph
simultaneously, sharing the graph accesses via the memory hierarchy. However, different …

Graph analytics, which mainly includes graph processing, graph mining, and graph learning,
has become increasingly important in several domains, including social network analysis …