Recent deep learning models have moved beyond low dimensional regular grids such as
image, video, and speech, to high-dimensional graph-structured data, such as social …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

This paper introduces a new approach to building distributed-memory graph analytics
systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies …

Large-scale graph processing requires the high bandwidth of data access. However, as
graph computing continues to scale, it becomes increasingly challenging to achieve a high …

Graph analytics is an emerging application which extracts insights by processing large
volumes of highly connected data, namely graphs. The parallel processing of graphs has …

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 …

Graph neural network (GNN) is a promising emerging application for link prediction,
recommendation, etc. Existing hardware innovation is limited to single-machine GNN (SM …

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 …

Inspired by the successes of convolutional neural networks (CNN) in computer vision, the
convolutional operation has been moved beyond low-dimension grids (eg, images) to high …

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 …