Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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

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 Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning
model. However, it can be notoriously challenging to inference GCNs over large graph …

Abstract Graph Neural Networks (GNNs) have been widely used in various domains, and
GNNs with sophisticated computational graph lead to higher latency and larger memory …

Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph
data across various domains, yet their acceleration on mainstream GPUs is challenged by …

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 …

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 …

The widespread use of Sparse Matrix Dense Matrix Multiplication (SpMM) and Sampled
Dense Matrix Dense Matrix Multiplication (SDDMM) kernels makes them candidates for …

Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique
capability to extend Machine Learning (ML) approaches to applications broadly-defined as …