High performance parallel KMP algorithm on a heterogeneous architecture

Abstract

String matching algorithm is widely used in many application areas such as bio-informatics, network intrusion detection, computer virus scan, among many others. KMP (Knuth-Morris-Pratt) algorithm is commonly used for its fast execution time compared with many other string matching algorithms when applied to large input texts. However, the performance of the KMP algorithm is limited when the input text size increases significantly beyond a certain limit. In this paper, we propose a high performance parallel KMP algorithm on the Heterogeneous High Performance Computing (HPC) architecture based on the general-purpose multicore microprocessor and the Graphic Processing Unit (GPU) using OpenCL as the GPGPU (General Purpose computing using Graphic Processing Unit) platform. The proposed parallel KMP algorithm mainly focuses on optimizing the CPU-GPU memory hierarchy by overlapping the data transfer between the CPU memory and the GPU memory with the string matching operations on the GPU. It also optimizes the allocation of the work-groups and the work-items, and places the pattern data and the Failure Table to the on-chip shared-memory of the GPU. The experimental results show that the optimized parallel KMP algorithm leads up to ~8 times faster execution time.