COSMOS: Performance Portable Graph Pattern Matching with Domain-Specific Software Distributed Shared Memory

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.