Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

Graphs are one of the key data structures for many real-world computing applications and
the importance of graph analytics is ever-growing. While existing software graph processing …

Sparse matrices are widely used in graph and data analytics, machine learning, engineering
and scientific applications. This paper describes and analyzes OuterSPACE, an accelerator …

Given the growing importance of large-scale graph analytics, there is a need to improve the
performance of graph analysis frameworks without compromising on productivity. GraphMat …

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 …

There are two types of high-performance graph processing engines: low-and high-level
engines. Low-level engines (Galois, PowerGraph, Snap) provide optimized data structures …

Memory-based data center applications require increasingly large memory capacities, but
face the challenges posed by the inherent difficulties in scaling DRAM and also the cost of …

Mathematical software and graph-theoretical algorithmic packages to efficiently model,
analyze, and query graphs are crucial in an era where large-scale spatial, societal, and …

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