SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

Subgraph isomorphism is a fundamental graph problem with applications in diverse domains from biology to social network analysis. Of particular interest is molecular matching, which uses a subgraph isomorphism formulation for the drug discovery process. While subgraph isomorphism is known to be NP-complete and computationally expensive, in the molecular matching formulation a number of domain constraints allow for efficient implementations. This paper presents SIGMo, a high-throughput, portable subgraph isomorphism framework for GPUs, specifically designed for batch molecular matching. SIGMo takes advantage of the specific domain formulation to provide a more efficient filter-and-join strategy: the framework introduces a novel multi-level iterative filtering technique based on neighborhood signature encoding to efficiently prune candidates prior to a GPU-optimized join phase using a stack-based DFS traversal. The GPU implementation is written in SYCL, allowing portable execution on AMD, Intel, and NVIDIA GPUs. Our experimental evaluation on a large dataset from ZINC demonstrates up to 1470 × speedup over state-of-the-art subgraph isomorphism frameworks, and achieves a throughput of 7.7 billion matches per second on a cluster with 256 GPUs.