High-Performance Parallel Location-Aware Algorithms for Approximate String Matching on GPUs

Abstract

Approximate string matching has been widely used in many applications, including deoxyribonucleic acid sequence searching, spell checking, text mining, and spam filters. The method is designed to find all locations of strings that approximately match a pattern in accordance with the number of insertion, deletion, and substitution operations. Among the proposed algorithms, the bit-parallel algorithms are considered to be the best and highly efficient algorithms. However, the traditional bit-parallel algorithms lacks the ability of identifying the start and end positions of a matched pattern. Furthermore, acceleration of the bit-parallel algorithms has become a crucial issue for processing big data nowadays. In this paper, we propose two kinds of parallel location-aware algorithms called data-segmented parallelism and high-degree parallelism as means to accelerate approximate string matching using graphic processing units. Experimental results show that the high-degree parallelism on GPUs achieves significant improvement in system and kernel throughputs compared to CPU counterparts. Compared to state-of-the-art approaches, the proposed high-degree parallelism achieves 11 to 105 times improvement.