Interleaved Bitstream Execution for Multi-Pattern Regex Matching on GPUs

Pattern matching is a key operation in unstructured data analytics, commonly supported by regular expression (regex) engines. Bitparallel regex engines compile regexes into bitstream programs, which expose fine-grained parallelism and are well-suited for GPU execution. A straightforward strategy executes each bitstream instruction sequentially, processing all data blocks in a loop. However, this execution suffers from poor data reuse and high memory consumption, limiting throughput. Our key insight is to adopt an interleaved execution model, where all bitstream instructions are fused into a single loop and executed block-wise. While interleaved execution could improve data reuse, enabling it on GPUs is non-trivial due to cross-block data dependencies. To address this, we introduce 1) Dependency-Aware Thread-Data Mapping, which resolves cross-block dependencies via selective recomputation. We further improve interleaved execution performance with two additional optimizations: 2) Shift Rebalancing, which balances dependency chains to reduce synchronization barriers; and 3) Zero Block Skipping, which exploits bitstream sparsity to skip computation on zero blocks. Together, these techniques make interleaved execution practical and efficient. Experiments on real-world regex benchmarks demonstrate a 19.5× geometric mean speedup over the state-of-theart GPU regex engine.