13387

Gunrock: A High-Performance Graph Processing Library on the GPU

Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, John D. Owens
University of California, Davis
arXiv:1501.05387 [cs.DC], (22 Jan 2015)

@article{wang2015gunrock,

   title={Gunrock: A High-Performance Graph Processing Library on the GPU},

   author={Wang, Yangzihao and Davidson, Andrew and Pan, Yuechao and Wu, Yuduo and Riffel, Andy and Owens, John D.},

   year={2015},

   month={jan},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

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For large-scale graph analytics on the GPU, the irregularity of data access and control flow and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our graph-processing system, uses a high-level bulk-synchronous abstraction with traversal and computation steps, designed specifically for the GPU. Gunrock couples high performance with a high-level programming model that allows programmers to quickly develop new graph primitives with only a few hundred lines of code. We evaluate Gunrock on five key graph primitives and show that Gunrock has at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.
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