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GraphBench is a community-driven graph benchmark suite.

Graph and irregular computation is of increasing importance to industry, the government, and the sciences. The core computational component of many “big data”, machine-learning, and security applications is an irregular computation typically done on a graph data structure.

GraphBench is a collection of graph kernels and datasets to aid graph-processing framework creators in developing their systems. We've collected a number of existing datasets and reference implementations across a wide variety of graph analytics platforms.

This is a community effort! We need your help to build a representative and relevant benchmark suite. Please join our Google Group, clone our GitHub repo, and contribute your favorite implementations and data sources!

GraphBench has two components:

• Datasets and Synthetic data generators
• Reference implementations

Contributors

GraphBench is hosted at the University of Washington, Department of Computer Science and Engineering, and includes contributions from:

• Oracle

• Hassan Chafi

• Tim Harris

• University of Washington

• Cindy Xin Yi

• Jake Sanders

• Jacob Nelson

• Brandon Holt

• Brandon Myers

• Luis Ceze

• Mark Oskin

• Simon Kahan

This work is generously supported by Oracle Corporation and the National Science Foundation.