Parallel Text Matching Using GPGPU

Abstract

This paper studies implementation methods of parallel text matching using General Purpose computing on Graphics Processing Unit (GPGPU). It is necessary to accelerate text matching in applications of real-time processing, such as anomaly-detection and decision-making. GPGPU is a technology that can be used to accelerate a variety of applications with highly parallel processing elements in GPUs. Recently, the Parallel-Failure-less Aho-Corasick (PFAC) algorithm has been developed, and an open-source PFAC library is currently available. However, there are several different implementation methods in the host-side, and choosing a good combination of these methods is important to improve the performance. We implemented a prototype system, and evaluated the performance and power consumption varying the implementation methods and input data. The evaluation results show that the performance of the system using GPGPU is better than a system using 4-core CPU with smaller power consumption.