Accelerating Levenshtein and Damerau edit distance algorithms using GPU with unified memory

Abstract

String matching problems such as sequence alignment is one of the fundamental problems in many computer since fields such as natural language processing (NLP) and bioinformatics. Many algorithms have been proposed in the literature to address this problem. Some of these algorithms compute the edit distance between the two strings to perform the matching. However, these algorithms usually require long execution time. Many researches use high performance computing to reduce the execution time of many string matching algorithms. In this paper, we use the CUDA based Graphics Processing Unit (GPU) and the newly introduced Unified Memory(UM) to speed up the most common algorithms to compute the edit distance between two string. These algorithms are the Levenshtein and Damerau distance algorithms. Our results show that using GPU to implement the Levenshtein and Damerau distance algorithms improvements their execution times of about 11X and 12X respectively when compared to the sequential implementation. And an improvement of about 61X and 71X respectively can be achieved when GPU is used with unified memory.