Understanding High-Performance Subgraph Pattern Matching: A Systems Perspective

Akshit Sharma, Dinesh P. Mehta, Bo Wu

Abstract: Subgraph isomorphism is a crucial problem in graph-analytics with wide-ranging applications. This paper examines and compares two high-performance solutions to this problem: backtracking, represented by VF3, and compilation, represented by Dryadic. Despite both strategies being based on vertex-extension mapping, Dryadic significantly outperforms VF3 across all tests, with speed-ups ranging from a minimum of 4.95x to a maximum of 165x. To understand these disparities, the paper identifies and explores five key optimizations in Dryadic: candidate vertices generation, execution specificity, data graph storage, matching order, and redundancy elimination. With these optimizations removed, Dryadic's performance substantially degrades but it is still on average 10x faster than VF3 due to better spatial locality and search-space pruning. With the insights gained from these optimizations, we propose and implement two new techniques: lazy evaluation in Dryadic and connectivity checks in VF3, resulting in performance improvements of up to 1.23x and 1.46x, respectively.