### Overview

Graph theoretic problems are representative of fundamental kernels in traditional and emerging scientific applications such as complex network analysis, data mining and computational biology, as well as applications in national security. Graph abstractions are also extensively used to understand and solve challenging problems in scientific computing. Real-world systems such as the Internet, telephone networks, the world-wide web, social interactions and transportation networks are analyzed by modeling them as graphs. To efficiently solve large-scale graph problems, it is necessary to design high performance computing systems and novel parallel algorithms.

**GraphAnalysis.org** is a compendium of resources related to High Performance Computing applied for large-scale graph analysis.

- We maintain a parallel graph theory benchmark that solves multiple graph analysis kernels on small-world networks. An early version of the benchmark was part of the DARPA High Productivity Computing Systems (HPCS) Compact Application (SSCA) suite. The benchmark performance across current HPC systems can be compared using a single score called TrEPS (Traversed Edges Per Second).
- We also frequently update recent publications and web pages related to HPC Graph Analysis.

### Latest News

*August 3, 2018*: Added Graph Algorithms Building Blocks (GABB 2019), IPDPS 2019 workshop*October 15, 2017*: Added Graph Algorithms Building Blocks (GABB 2018), IPDPS 2018 workshop*May 16, 2017*: Added SIAM Computational Science and Engineering (CSE) 2017 workshop*October 3, 2016*: Added Graph Algorithms Building Blocks (GABB 2017), IPDPS 2017 workshop