The random dot product graph (RDPG) is an independent-edge random graph that is
analytically tractable and, simultaneously, either encompasses or can successfully …

The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

Traditionally distributed graph processing systems have largely focused on scalability
through the optimizations of inter-node communication and load balance. However, they …

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 …

In this paper, we present GridGraph, a system for processing large-scale graphs on a single
machine. Grid-Graph breaks graphs into 1D-partitioned vertex chunks and 2D-partitioned …

The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

Remote access to NVMe Flash enables flexible scaling and high utilization of Flash capacity
and IOPS within a datacenter. However, existing systems for remote Flash access either …

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 …