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Subgraph isomorphism is a pattern-matching algorithm widely used in many domains such as chem-informatics, bioinformatics, databases, and social network analysis. It is computationally expensive and is a proven NP-hard problem. The massive parallelism in GPUs is well suited for solving subgraph isomorphism. However, current GPU implementations are far from the achievable performance. Moreover, the enormous memory requirement of current approaches limits the problem size that can be handled. This work analyzes the fundamental challenges associated with processing subgraph isomorphism on GPUs and develops an efficient GPU implementation. We also develop a GPU-friendly trie-based data structure to drastically reduce the intermediate storage space requirement, enabling large benchmarks to be processed. We also develop the first distributed sub-graph isomorphism algorithm for GPUs. Our experimental evaluation demonstrates the efficacy of our approach by comparing the execution time and number of cases that can be handled against the state-of-the-art GPU implementations.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.