Prism: Practical In-Memory Acceleration for Subgraph Matching at Scale

Subgraph matching is a core primitive in graph analytics, yet it remains difficult to scale due to its combinatorial complexity and highly irregular memory access patterns. Despite decades of algorithmic innovation, existing CPU and GPU-based systems struggle to fully utilize available compute resources, as the memory-bound nature of subgraph workloads severely limits sustained throughput. Our empirical characterization reveals that even state-of-the-art GPU solutions, including recent GraphChallenge champions, achieve only a small fraction of their theoretical peak performance on real workloads.This paper presents Prism, the first solution to address the GraphChallenge contest using practical emerging processing-in-memory (PIM) hardware. Prism targets UPMEM, a commercially available platform that integrates thousands of in-memory compute units known as DPUs. To harness UPMEM’s massively parallel, memory-centric architecture and overcome its unique architectural constraints, Prism introduces a cross-layer design that integrates skew-aware task decomposition, asynchronous memory-compute decoupling, and topology-adaptive intersection kernels. We evaluate Prism across diverse subgraph workloads and datasets. It delivers up to 10.73×, 26.53×, 11.13×, and 10.11× speedup over GraphChallenge champions MERCURY and SMOG, and recent SOTA systems including TRUST and PimPam, while scaling near-linearly to 40,960 DPUs. The source codes are available at https://github.com/CGCL-codes/Prism.