Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three
years. Compared with other deep learning modalities, high-performance hardware …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

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 …

Graph processing is increasingly bottlenecked by main memory accesses. On-chip caches
are of little help because the irregular structure of graphs causes seemingly random memory …

Graph neural networks (GNNs) are gaining popularity as a promising approach to machine
learning on graphs. Unlike traditional graph workloads where each vertex/edge is …

Graph processing is typically memory bound due to low compute to memory access ratio
and irregular data access pattern. The emerging high-bandwidth memory (HBM) delivers …

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 …

Many applications perform frequent scatter update operations to large data structures. For
example, in push-style graph algorithms, processing each vertex requires updating the data …

Graph processing applications are notorious for exhibiting poor cache locality due to an
irregular memory access pattern. However, prior work on graph reordering has observed …

Due to the limited capacity of GPU memory, the majority of prior work on graph applications
on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware …