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 …

Software-intensive systems in most domains, from autonomous vehicles to health, are
becoming predominantly parallel to efficiently manage large amount of data in short (even …

While there has been significant work on parallel graph processing, there has been very
surprisingly little work on high-performance hypergraph processing. This paper presents a …

The use of FPGAs for efficient graph processing has attracted significant interest. Recent
memory subsystem upgrades including the introduction of HBM in FPGAs promise to further …

Non-volatile main memory (NVRAM) technologies provide an attractive set of features for
large-scale graph analytics, including byte-addressability, low idle power, and improved …

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

Attracted by the enormous potentials of Graphics Processing Units (GPUs), an array of
efforts has surged to deploy Breadth-First Search (BFS) on GPUs, which, however, often …

Graph processing, especially high-performance graph traversal, plays a more and more
important role in data analytics. The successor of Sunway TaihuLight, New Sunway, is …

Many systems are recently proposed for large-scale iterative graph analytics on a single
machine with GPU accelerators. Despite of many research efforts, for iterative directed graph …

Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs.
The large data sizes of graphs and their vertex features make scalable training algorithms …