A Memory-Access-Efficient Implementation of the Approximate String Matching Algorithm on GPU

Abstract

The task of finding strings having a partial match to a given pattern is of interest to a number of practical applications, including DNA sequencing and text searching. Owing to its importance, alternatives to accelerate the Approximate String Matching (ASM) have been widely investigated in the literature. The main contribution of this work is to present a memory-access-efficient implementation for computing the ASM on a GPU. The key idea of our implementation relies on warp shuffle operations, which are used to reduce the communication overhead between threads. Experimental results, carried out on a GeForce GTX 960 GPU, show that the proposed implementation provides acceleration between 1.31 and 1.84 times when compared to another noteworthy alternative.