Survey on Key Technologies of Graph Processing Systems Based on Multi-core CPU and GPU Platforms

Zhang Yuan; Cao Huawei; Zhang Jie; Shen Yue; Sun Yiming; Dun Ming; An Xuejun; Ye Xiaochun

doi:10.7544/issn1000-1239.202440073

Journal of Computer Research and Development > 2024 > 61(6): 1401-1428. > DOI: 10.7544/issn1000-1239.202440073

Zhang Yuan, Cao Huawei, Zhang Jie, Shen Yue, Sun Yiming, Dun Ming, An Xuejun, Ye Xiaochun. Survey on Key Technologies of Graph Processing Systems Based on Multi-core CPU and GPU Platforms[J]. Journal of Computer Research and Development, 2024, 61(6): 1401-1428. DOI: 10.7544/issn1000-1239.202440073

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Survey on Key Technologies of Graph Processing Systems Based on Multi-core CPU and GPU Platforms

Zhang Yuan^{1, 2,},
Cao Huawei^{1, 3, ,},
Zhang Jie¹,
Shen Yue^{1, 3},
Sun Yiming^{1, 2},
Dun Ming¹,
An Xuejun^{1, 2},
Ye Xiaochun¹

1.
State Key Lab of Processors (Institute of Computing Technology, Chinese Academy of Sciences), Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
University of Chinese Academy of Sciences, Nanjing 211135

Funds: This work was supported by the National Key Research and Development Program of China (2023YFB4502305), the Beijing Natural Science Foundation (4232036), and the CAS Project for Youth Innovation Promotion Association.

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Author Bio:
Zhang Yuan: born in 1990. PhD candidate, engineer. Member of CCF. Her main research interests include high-performance computing and graph algorithm optimization

Cao Huawei: born in 1989. PhD, associate professor master supervisor. Member of CCF. His main research interests include parallel computing and high throughput computing architecture

Zhang Jie: born in 1999. Master, assistant engineer. Her main research interests include high-performance computing and graph algorithm optimization

Shen Yue: born in 1999. Master candidate. Her main research interests include parallel computing and dynamic graph processing

Sun Yiming: born in 1997. PhD candidate. His main research interests include parallel computing and graph algorithm optimization

Dun Ming: born in 1997. Master, assistant engineer. Her main research interests include high-performance computing and sparse algorithm optimization

An Xuejun: born in 1966. PhD, senior engineer, PhD supervisor. Member of CCF. His main research interests include computer system architecture and high-performance interconnection networks

Ye Xiaochun: born in 1981. PhD, professor, PhD supervisor. Member of CCF. His main research interests include algorithm paralleling and optimizing software simulation, and architecture for high throughput computer
Received Date: January 31, 2024
Revised Date: March 05, 2024
Available Online: April 14, 2024

Graphical Abstract

Abstract

Abstract

As a key technique for analyzing and mining relationships, graph computing has been widely used in smart healthcare, social network analysis, financial anti-fraud, road navigation, computational sciences, and others. In recent years, with the development of parallel structures, memory access methods, interconnection structures, and synchronization mechanisms of general-purpose CPU and GPU architecture, multi-core CPU and GPU have become common platforms for accelerating graph processing. However, there are significant challenges to graph processing in terms of improving performance and achieving high scalability, such as the large scale and irregular distribution of data, complex data dependence, high communication-to-computation ratio, and dynamic changes of vertices and edges. To address the above challenges, a large number of graph processing systems based on multi-core CPU and GPU platforms are proposed and achieve good results. To provide readers with a comprehensive understanding of graph processing optimizations on multi-core CPU and GPU platforms, we first present the basic concepts, including the graph data structures, the typical graph algorithms, and the characteristics of graph application. Then the challenges of graph processing are clarified. After that, we present the existing graph processing systems based on multi-core CPU and GPU platforms. From the perspective of accelerated graph processing design, we summarize the key technology optimizations in detail and systematically, including pre-processing, memory access optimization, computing acceleration, and overhead reduction of data communication. Finally, we analyze the performance and scalability of state-of-art graph processing and conclude the future development trend of graph processing based on different perspectives, which expects to bring certain inspiration to relevant researchers to explore high-performance graph processing system.

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References (179)

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