Parallelization and characterization of pattern matching using GPUs

Pattern matching is a highly computationally intensive operation used in a plethora of applications. Unfortunately, due to the ever increasing storage capacity and link speeds, the amount of data that needs to be matched against a given set of patterns is growing rapidly. In this paper, we explore how the highly parallel computational capabilities of commodity graphics processing units (GPUs) can be exploited for high-speed pattern matching. We present the design, implementation, and evaluation of a pattern matching library running on the GPU, which can be used transparently by a wide range of applications to increase their overall performance. The library supports both string searching and regular expression matching on the NVIDIA CUDA architecture. We have also explored the performance impact of different types of memory hierarchies, and present solutions to alleviate memory congestion problems. The results of our performance evaluation using off-the-self graphics processors demonstrate that GPU-based pattern matching can reach tens of gigabits per second on different workloads.