A Survey on Graph Processing Accelerators: Challenges and Opportunities

• Published in: Journal of computer science and technology Vol. 34; no. 2; pp. 339 - 371
• Main Authors: Gui, Chuang-Yi, Zheng, Long, He, Bingsheng, Liu, Cheng, Chen, Xin-Yu, Liao, Xiao-Fei, Jin, Hai
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2019; Springer; Springer Nature B.V; National Engineering Research Center for Big Data Technology and System, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; Services Computing Technology and System Laboratory, School of Computer Science and Technology Huazhong University of Science and Technology, Wuhan 430074, China; Cluster and Grid Computing Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China%School of Computing, National University of Singapore, Singapore 117418, Singapore%School of Computing, National University of Singapore, Singapore 117418, Singapore; Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
• Subjects: Accelerators; Artificial Intelligence; Computer Science; Computers; Data science; Data structures; Data Structures and Information Theory; Energy consumption; Energy efficiency; Field programmable gate arrays; Graph representations; Hardware; Information Systems Applications (incl.Internet); Machine learning; Personal computers; R&D; Research & development; Software; Software Engineering; Survey; Surveys; Theory of Computation; domain-specific architecture; performance; graph processing accelerator; energy efficiency
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-019-1914-z

Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures, dedicated hardware solutions, also referred to as graph processing accelerators, are essential and emerging to provide the benefits significantly beyond what those pure software solutions can offer. In this paper, we conduct a systematical survey regarding the design and implementation of graph processing accelerators. Specifically, we review the relevant techniques in three core components toward a graph processing accelerator: preprocessing, parallel graph computation, and runtime scheduling. We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator. Interestingly, we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and the complexity of hardware configurations. We finally present and discuss several challenges in details, and further explore the opportunities for the future research.