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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Abstract

Graphs that are used to model real-world entities with vertices and relationships among entities with edges, have proven to be a powerful tool for describing real-world problems in applications. In most real-world scenarios, entities and their relationships are subject to constant changes. Graphs that record such changes are called dynamic graphs. In recent years, the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics results. As the scale of dynamic graphs becomes larger, higher performance requirements are demanded to dynamic graph processing systems. With the massive parallel processing power and high memory bandwidth, GPUs become mainstream vehicles to accelerate dynamic graph processing tasks. GPU-based dynamic graph processing systems mainly address two challenges: maintaining the graph data when updates occur (i.e., graph updating) and producing analytics results in time (i.e., graph computing). In this paper, we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph computing. To comprehensively discuss existing dynamic graph processing systems on GPUs, we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph computing. In addition, we discuss the challenges and future research directions of dynamic graph processing on GPUs.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61972444, 61825202, 62072195, and 61832006). This work was also supported by Zhejiang Lab (2022P10AC02).

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Authors and Affiliations

  1. National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Hongru Gao, Xiaofei Liao, Zhiyuan Shao, Kexin Li, Jiajie Chen & Hai Jin

  2. Zhejiang Lab, Hangzhou, 311121, China

    Hongru Gao, Zhiyuan Shao & Kexin Li

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  1. Hongru Gao
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  2. Xiaofei Liao
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Correspondence to Zhiyuan Shao.

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Hongru Gao received the BS degree in software engineering from Huazhong University of Science and Technology (HUST), China in 2020. He is currently working toward the PhD degree. His research interests include graph computing and graph neural networks.

Xiaofei Liao received the PhD degree in computer science and technology from Huazhong University of Science and Technology (HUST), China in 2005. He is now a professor and PhD supervisor at National Engineering Research Center for Big Data Technology and System, HUST, China. His research interests are in the areas of memory computing, runtime systems, and graph computing.

Zhiyuan Shao received the PhD degree in computer science and technology from Huazhong University of Science and Technology (HUST), China in 2005. He is now a professor at National Engineering Research Center for Big Data Technology and System, HUST, China. His research interests are in the areas of graph computing, big-data processing, and computing systems.

Kexin Li received the BS degree in computer science and technology from Huazhong University of Science and Technology (HUST), China in 2020. She is currently working toward the MS degree. Her research interests include graph computing and memory-access optimization.

Jiajie Chen received the BS degree in computer science and technology from Huazhong University of Science and Technology (HUST), China in 2020. He is currently working toward the MS degree. His research interests include graph computing and network processing.

Hai Jin is a Chair Professor of computer science and engineering at Huazhong University of Science and Technology (HUST), China. Jin received his PhD in computer engineering from HUST, China in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz, Germany. Jin worked at The University of Hong Kong, China between 1998 and 2000, and as a visiting scholar at the University of Southern California, USA between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. Jin is a Fellow of IEEE, Fellow of CCF, and a life member of the ACM. He has co-authored more than 20 books and published over 900 research papers. His research interests include computer architecture, parallel and distributed computing, big-data processing, data storage, and system security.

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Gao, H., Liao, X., Shao, Z. et al. A survey on dynamic graph processing on GPUs: concepts, terminologies and systems. Front. Comput. Sci. 18, 184106 (2024). https://doi.org/10.1007/s11704-023-2656-1

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