We present GRIP, a graph neural network accelerator architecture designed for low-latency
inference. Accelerating GNNs is challenging because they combine two distinct types of …

High-performance implementations of graph algorithms are challenging to implement on
new parallel hardware such as GPUs because of three challenges:(1) the difficulty of coming …

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 …

Graph analytics applications are ubiquitous in this era of a connected world. These
applications have very low compute to byte-transferred ratios and exhibit poor locality, which …

The purpose of the GraphBLAS Forum is to standardize linear-algebraic building blocks for
graph computations. An important part of this standardization effort is to translate the …

Graph neural networks (GNNs) are prevalent to deal with graph-structured datasets,
encoding graph data into low dimensional vectors. In this paper, we present a fast training …

We consider the problem of graph analytics on evolving graphs (ie, graphs that change over
time). In this scenario, a query typically needs to be applied to different snapshots of the …

With the development of computer architecture, even for embedded systems, GPU devices
can be integrated, providing outstanding performance and energy efficiency to meet the …

Placement is an important step in modern verylarge-scale integrated (VLSI) designs.
Detailed placement is a placement refining procedure intensively called throughout the …

In general, the performance of parallel graph processing is determined by three pairs of
critical parameters, namely synchronous or asynchronous execution mode (Sync or Async) …