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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](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
- Reference implementations
- Instructions on using Frameworks and Docker images
GraphBench is a project of the University of Washington, Department of Computer Science and Engineering. Contributors include:
- 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.