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

doi:10.1145/3744904

	CGCGraph: Efficient CPU-GPU Co-execution for Concurrent Dynamic Graph Processing
	Sun, Yiming 1,2,3; Zhang, Jie 1; Cao, Huawei 1,2; Zhang, Yuan 1; An, Xuejun 1,2; Huang, Junying 1,4; Ye, Xiaochun 1
	2025-09-01
发表期刊	ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION
ISSN	1544-3566
卷号	22 期号:3 页码:26
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.
关键词	CPU-GPU co-execution concurrent graph processing dynamic graph snapshot processing high throughput
DOI	10.1145/3744904
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods
WOS记录号	WOS:001606025500009
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41595
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.