A Survey on Graph Processing Accelerators: Challenges and Opportunities

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 arch...

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Published inJournal 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
LanguageEnglish
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
Online AccessGet full text
ISSN1000-9000
1860-4749
DOI10.1007/s11390-019-1914-z

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Abstract 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.
AbstractList 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.
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. Keywords graph processing accelerator, domain-specific architecture, performance, energy efficiency
Audience Academic
Author Liao, Xiao-Fei
Chen, Xin-Yu
Gui, Chuang-Yi
He, Bingsheng
Liu, Cheng
Jin, Hai
Zheng, Long
AuthorAffiliation 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
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ContentType Journal Article
Copyright Springer Science+Business Media, LLC & Science Press, China 2019
COPYRIGHT 2019 Springer
Springer Science+Business Media, LLC & Science Press, China 2019.
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Keywords domain-specific architecture
performance
graph processing accelerator
energy efficiency
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PublicationTitle Journal of computer science and technology
PublicationTitleAbbrev J. Comput. Sci. Technol
PublicationTitle_FL Journal of Computer Science ＆ Technology
PublicationYear 2019
Publisher Springer US
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
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– name: Services Computing Technology and System Laboratory, School of Computer Science and Technology Huazhong University of Science and Technology, Wuhan 430074, China
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Snippet 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...
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...
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SubjectTerms 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
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Title A Survey on Graph Processing Accelerators: Challenges and Opportunities
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