Multi-pattern matching is widely used in modern software for applications requiring high throughput such as protein search, network traffic inspection, virus or spam detection. Graphics Processor Units (GPUs) excel at executing massively parallel workloads. Regular expression (regex) matching is typically performed by simulating the execution of deterministic finite automata (DFAs) or nondeterministic finite automata (NFAs). The natural implementations of these automata simulation algorithms on GPUs are highly inefficient because they give rise to irregular memory access patterns. This paper presents HybridSA, a heterogeneous CPU-GPU parallel engine for multi-pattern matching. HybridSA uses bit parallelism to efficiently simulate NFAs on GPUs, thus reducing the number of memory accesses and increasing the throughput. Our bit-parallel algorithms extend the classical shift-and algorithm for string matching to a large class of regular expressions and reduce automata simulation to a small number of bitwise operations. We have developed a compiler to translate regular expressions into bit masks, perform optimizations, and choose the best algorithms to run on the GPU. The majority of the regular expressions are accelerated on the GPU, while the patterns that exhibit random memory accesses are executed on the CPU in parallel. We evaluate HybridSA against state-of-the-art CPU and GPU engines, as well as a hybrid combination of the two. HybridSA achieves between 4 and 60 times higher throughput than the state-of-the-art CPU engine and between 4 and 233 times better than the state-of-the-art GPU engine across a collection of real-world benchmarks.

@article{GlaunecKM24,
  title = {HybridSA: GPU Acceleration of Multi-pattern Regex Matching using Bit Parallelism},
  author = {Alexis Le Glaunec and Lingkun Kong and Konstantinos Mamouras},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {8},
  number = {OOPSLA2},
  year = {2024},
  doi = {10.1145/3689771}
}