Graph pattern matching (GPM) aims to find subgraphs isomorphic to user-specified patterns within a large graph. Due to its ability to reveal potential relationships among entities in complex networks, it is widely applied in various fields, such as mining molecular structures in bioinformatics, detecting fraud in cloud-based e-commerce, and querying knowledge graphs in large language model. The explosion of data brought by the AI era has rendered traditional GPM systems inadequate for real-world needs. Due to the intricate data dependencies of GPM tasks, most SOTA GPM systems currently have limited scalability and performance, they perform well in small graph mining with single node but cannot scale to modern clusters with GPU acceleration. This paper introduces JUPITER, the first system capable of matching patterns on large graph across multi-node GPU clusters, which can handle graphs 10 times larger than SOTAs with the same memory resources. Its core principle is to delegate computation to the data-residing processing unit rather than pulling data to the computation location, which greatly improves communication efficiency. Experimental results show that JUPITER can reduce communication volume by two orders of magnitude compared to SOTA subgraph matching systems, achieving up to 120× speedup and an average of 21.5× speedup.

Graph pattern matching (GPM) algorithms search for specific topological patterns in large networks, revealing relationships between entities. They are applied in fields like social network analysis, cheminformatics, recommendation systems, classification systems, and anomaly detection. However, GPM is highly time-consuming, lacks polynomial-time algorithms, and is challenging to scale for distributed settings. These GPM algorithms require 2-hop neighbors and generate many intermediate results during execution. Traditional systems use a pull-based method, leading to significant memory and communication overhead, which complicates scaling to larger data sizes. This paper introduces a push-based method and further designs a push-pull hybrid algorithm. Based on the hybrid algorithms, we build a GPM system Pisces for efficiently matching patterns on partitioned graphs. With optimizations in the merge context, it achieves significant performance improvements over state-of-the-art GPM systems.

Graph pattern matching (GPM) is essential in fields like circuit logic synthesis, anomaly detection, social network analysis, cheminformatics, recommendation systems, and classification systems. Its NP-completeness and the irregular nature of graph data make scaling to distributed systems challenging, especially for complex supercomputers. Although utilizing architecture-specific optimization can improve the performance of Graph Pattern Matching on large-scale data, such ad-hoc solution lacks performance portability that not only causes vendor lock-in but also complicates the parallel evolution of GPM software with hardware architectures. This paper proposes Cosmos, a domain-specific software distributed shared memory model (DSM) that shields diversity of supercomputers from users and developers, achieving both performance portability and performance. This approach enables the same code scaling to thousands of nodes across different supercomputers while maintaining performance comparable to manually optimized versions.