Using GPUs to speed-up Levenshtein edit distance computation

Abstract

Sequence comparison problems such as sequence alignment and approximate string matching are part of the fundamental problems in many fields such as natural language processing, data mining and bioinformatics. However, the algorithms proposed to address these problems suffer from high computational complexities prohibiting them from being widely used in practical large-scale settings. Many researchers used parallel programming to reduce the execution time of these algorithms. In this paper, we follow this approach and use the parallelism capabilities of the Graphics Processing Unit (GPU) to accelerate one of the most common algorithms to compute the edit distance between two strings, which is known as the Levenshtein distance. To take full advantage of the large number of cores in a GPU, we employ a diagonal-based tracing technique which results in even greater improvements in terms of the running time. In fact, our CUDA implementation of the Levenshtein algorithm is about 11X faster than the sequential implementation. This is achieved without affecting the accuracy.