Parallel Approaches to the String Matching Problem on the GPU

Abstract

We design a family of parallel algorithms and GPU implementations for the exact string matching problem, based on Rabin-Karp (RK) randomized string matching. We describe and analyze three primary parallel approaches to binary string matching: cooperative (CRK), divide-and-conquer (DRK), and a novel hybrid of both (HRK). The CRK is most effective for large patterns (>8K characters), while the DRK approach is superior for shorter patterns. We then generalize the DRK to support any alphabet size without loss of performance. Our DRK method achieves up to a 64 GB/s processing rate on 8-character patterns from an 8-bit alphabet on an NVIDIA Tesla K40c GPU. We next demonstrate a novel parallel two-stage matching method (DRK-2S), which first skims the text for a smaller subset of the pattern and then verifies all potential matches in parallel. Our DRK-2S method is superior for pattern sizes up to 64k compared to the fastest CPU-based string matching implementations. With an 8-bit alphabet and up to 1k-character patterns, we get a geometric mean speedup of 4.81x against the best CPU methods, and can achieve a processing rate of at least 53 GB/s.