A Distributed Framework for Subgraph Isomorphism Leveraging CPU and GPU Heterogeneous Computing

Chen Chen, Li Shen, Yingwen Chen

Abstract: Subgraph isomorphism enumerates all embeddings in a data graph that are identical to a query graph. It is a well-known NP-hard problem widely used in various domains, such as bioinformatics, chem-informatics, and social network analysis. Recent works are focused on using GPUs for subgraph isomorphism. Due to the massive scale of intermediate results, current GPU implementations face challenges in scaling across multiple nodes due to high communication costs. The computational power of CPUs is not fully utilized in this process. We present a distributed framework for subgraph isomorphism that leverages CPU and GPU heterogeneous computing. It eliminates the intermediate results on GPU and significantly reduces communication overhead during the load-balancing process. The experiments indicate that our algorithm can be extended to multiple nodes with an almost linear efficiency improvement. Furthermore, our method also significantly outperforms other existing works on GPUs. It can reach an improvement of up to 21 × compared to the state-of-the-art implementation CuTS in the distributed environment.