Regular Expressions on Modern GPGPUs
Cheng Li, Clark Verbrugge · 2024
Using GPUs is an effective way to accelerate regular expression (RE) matching, offering orders of magnitude faster processing than pure CPU approaches. Prior GPU-based RE acceleration methods, however, were developed on older GPU models and primarily aimed at expediting network packet inspection problems. In this work we conduct an updated study aiming to improve performance and enhance generality. We first incorporate prefiltering, verifying whether simpler parts of the RE can match before testing more complex RE components. We also observed that naive implementation of current designs on a modern GPU results in low thread occupancy, limiting performance, and improving the selection of GPU parameters is also crucial to optimizing performance. In combination our optimized design allows us to achieve 40x performance improvement over iNFAnt [5] and up to 1900x faster than ASyncAP [11]. Such an updated approach allows for faster, more general RE matching on modern GPUs.