Toward Efficient Similarity Search under Edit Distance on Hybrid Architectures

Abstract

Edit distance is the most widely used method to quantify similarity between two strings. We investigate the problem of similarity search under edit distance. Given a collection of sequences, the goal of similarity search under edit distance is to find sequences in the collection that are similar to a given query sequence where the similarity score is computed using edit distance. The canonical method of computing edit distance between two strings uses a dynamic programming-based approach that runs in quadratic time and space, which may not provide results in a reasonable amount of time for large sequences. It advocates for parallel algorithms to reduce the time taken by edit distance computation. To this end, we present scalable parallel algorithms to support efficient similarity search under edit distance. The efficiency and scalability of the proposed algorithms is demonstrated through an extensive set of experiments on real datasets. Moreover, to address the problem of uneven workload across different processing units, which is mainly caused due to the significant variance in the size of the sequences, different data distribution schemes are discussed and empirically analyzed. Experimental results have shown that the speedup achieved by the hybrid approach over inter-task and intra-task parallelism is 18 and 13, respectively.