Memory Efficient Parallelization for Aho-Corasick Algorithm on a GPU

Pattern matching is a commonly used operation in many applications including image processing, computer and network security, bioinformatics, among many others. Aho-Corasick (AC) algorithm is one of the well-known pattern matching techniques and it is intensively used in computer and network security. In order to meet the real-time performance requirements imposed on these security applications, developing a high-speed parallelization technique is essential for the AC algorithm. In this paper, we present a new memory efficient parallelization technique which efficiently places and caches the input text data and the reference data in the on-chip shared memories and texture caches of the Graphic Processing Unit (GPU). Furthermore, the new approach efficiently schedules memory accesses in order to minimize the overhead in loading data to the on-chip shared memories. The approach cuts down the effective memory access latencies and leads to significant performance improvements. Experimental results on Nvidia GeForce 9500GT GPU shows up to 15-times speedup compared with a serial version on 2.2Ghz Core2Duo Intel processor, and 15Gbps throughput performance.