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GraphBench is a community-driven graph benchmark suite.

Graph and irregular computation are 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. Our approach is to provde specifications of important kernels, along with curation of existing datasets and reference implementations across a wide variety of graph analytics platforms. We also provide preconfigured Docker images for many of these platforms to make it easier to get started running the kernels.

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

GraphBench has four components:

• Kernel specifications
• Datasets and Synthetic data generators
• Reference implementations
• Instructions on using Frameworks and Docker images

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.