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 …

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

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 …

Graph Neural Network (GNN) has recently drawn a rapid increase of interest in many
domains for its effectiveness in learning over graphs. Maximizing its performance is …

There has been a growing interest in the graph-streaming setting where a continuous
stream of graph updates is mixed with graph queries. In principle, purely-functional trees are …

The ubiquity of accelerators in high-performance computing has driven programming
complexity beyond the skill-set of the average domain scientist. To maintain performance …

There is growing interest in graph pattern mining (GPM) problems such as motif counting.
GPM systems have been developed to provide unified interfaces for programming …

Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable
memory with higher density and lower cost than DRAM. This enables the design of …

The value of graph-based big data can be unlocked by exploring the topology and metrics of
the networks they represent, and the computational approaches to this exploration take on …

Various applications model problems as streaming graphs, which need to quickly apply a
stream of updates and run algorithms on the updated graph. Furthermore, many dynamic …