In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

In the big data era, much real-world data can be naturally represented as graphs.
Consequently, many application domains can be modeled as graph processing. Graph …

The explosion of digital data and the ever-growing need for fast data analysis have made in-
memory big-data processing in computer systems increasingly important. In particular, large …

Graph convolutional neural networks (GCNs) have emerged as an effective approach to
extend deep learning for graph data analytics. Given that graphs are usually irregular, as …

Breadth-first search (BFS) is a core primitive for graph traversal and a basis for many higher-
level graph analysis algorithms. It is also representative of a class of parallel computations …

GPUs have been used to accelerate many regular applications and, more recently, irregular
applications in which the control flow and memory access patterns are data-dependent and …

Vertex-centric graph processing is employed by many popular algorithms (eg, PageRank)
due to its simplicity and efficient use of asynchronous parallelism. The high compute power …

The increasing importance of graph-data based applications is fueling the need for highly
efficient and parallel implementations of graph analysis software. In this paper we describe …

Specialized hardware accelerators can significantly improve the performance and power
efficiency of compute systems. In this paper, we focus on hardware accelerators for graph …

Graphs are common data structures for many applications, and efficient graph processing is
a must for application performance. Recently, the graphics processing unit (GPU) has been …