17924

Graph Processing on GPUs: A Survey

Xuanhua Shi, Zhigao Zheng, Yongluan Zhou, Hai Jin, Ligang He, Bo Liu, Qiang-Sheng Hua
School of Computer Science and Technology, Huazhong University of Science and Technology, China
ACM Computing Surveys, 2018

@article{shi2018graph,

   title={Graph processing on GPUs: A survey},

   author={Shi, Xuanhua and Zheng, Zhigao and Zhou, Yongluan and Jin, Hai and He, Ligang and Liu, Bo and Hua, Qiang-Sheng},

   journal={ACM Computing Surveys (CSUR)},

   volume={50},

   number={6},

   pages={81},

   year={2018},

   publisher={ACM}

}

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In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This paper surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping and specific GPU programming. In this paper, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in details, and explore the research opportunities in future.
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