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CGCGraph: Efficient CPU-GPU Co-execution for Concurrent Dynamic Graph Processing

文献类型：期刊论文

作者Sun, Yiming1,2,3; Zhang, Jie1; Cao, Huawei1,2; Zhang, Yuan1; An, Xuejun1,2; Huang, Junying1,4; Ye, Xiaochun1
刊名ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION
出版日期2025-09-01
卷号22期号:3页码:26
关键词CPU-GPU co-execution concurrent graph processing dynamic graph snapshot processing high throughput
ISSN号1544-3566
DOI10.1145/3744904
英文摘要With the continuous growth of user scale and application data, the demand for large-scale concurrent graph processing is increasing. Typically, large-scale concurrent graph processing jobs need to process corresponding snapshots of dynamically changing graph data to obtain information at different time points. To enhance the throughput of such applications, current solutions concurrently process multiple graph snapshots on the GPU. However, when dealing with rapidly changing graph data, transferring multiple snapshots of concurrent jobs to the GPU results in high data transfer overhead between CPU and GPU. Additionally, the execution mode of existing work suffers from underutilization of GPU computational resources. In this work, we introduce CGCGraph, which can be integrated into existing GPU graph processing systems like Subway, to enable efficient concurrent graph snapshot processing jobs and enhance overall system resource utilization. The key idea is to offload unshared graph data of multiple concurrent snapshots to the CPU, reducing CPU-GPU transfer overhead. By implementing CPU-GPU co-execution, there is potential for enhanced utilization of GPU computing resources. Specifically, CGCGraph leverages kernel fusion to process shared graph data concurrently on the GPU, while executing all snapshots in parallel on the CPU, with each snapshot assigned a dedicated thread. This approach enables efficient concurrent processing within a novel CPU-GPU co-execution model, incorporating three optimization strategies targeting storage, computation, and synchronization. We integrate CGCGraph with Subway, an existing system designed for out-of-GPUmemory static graph processing. Experimental results show that the integration of CGCGraph with current GPU-based systems obtains performance improvements ranging from 1.7 to 4.5 times.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001606025500009
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/41594]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Sun, Yiming
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
2.Zhongguancun Lab, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
4.Shanghai Innovat Ctr Processor Technol, Shanghai, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Yiming,Zhang, Jie,Cao, Huawei,et al. CGCGraph: Efficient CPU-GPU Co-execution for Concurrent Dynamic Graph Processing[J]. ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION,2025,22(3):26.
APA Sun, Yiming.,Zhang, Jie.,Cao, Huawei.,Zhang, Yuan.,An, Xuejun.,...&Ye, Xiaochun.(2025).CGCGraph: Efficient CPU-GPU Co-execution for Concurrent Dynamic Graph Processing.ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION,22(3),26.
MLA Sun, Yiming,et al."CGCGraph: Efficient CPU-GPU Co-execution for Concurrent Dynamic Graph Processing".ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 22.3(2025):26.

入库方式： OAI收割

来源：计算技术研究所

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