Abstract: Subgraph matching is a basic graph algorithm that is widely used in various fields such as social networks and graph neural networks.As the scale of graph data grows,there is an increasing need for efficient subgraph matching algorithms.GENEVA is a GPU-based parallel subgraph matching algorithm.It uses the interval index graph storage structure and parallel matching optimization method to greatly reduce storage overhead and improve subgraph matching performance.However,due to the diffe-rence in the underlying hardware architecture and compilation environment of the platform,GENEVA cannot be directly applied to the domestic DCU platform.In order to solve this problem,this paper proposes GENEVA's transplantation and optimization scheme for domestic DCU.IO time consumption is the main performance bottleneck of GENEVA algorithm.This paper proposes three optimization strategies of page-locked memory,preloading,and scheduler to alleviate this bottleneck.Among them,page-locked memory technology avoids additional data transfer from pageable memory to temporary page-locked memory,and greatly reduces the time consumption of IO transfer on the DCU platform.The preloading technology overlaps IO data transmission with DCU kernel function computation to mask IO time consumption.The scheduler reduces redundant data transfer while satisfy preloading requirements.In this paper,Experiments are carried out on three real-world datasets of different sizes,and the results show that the algorithm performance is significantly improved after using the optimization strategies.On 92.6% of the test cases,the optimized GENEVA-HIP execution time on the Sugon DCU platform is less than that of the unported GENEVA on the GPU server.On a larger dataset,the execution time of the optimized Geneva-HIP algorithm on the DCU platform is reduced by 52.73% compared with the the pre-port GENEVA algorithm on the GPU server.

Key words: Subgraph matching, DCU, Heterogeneous platform, HIP, Transplantation and optimization