NvPD: novel parallel edit distance algorithm, correctness, and performance evaluation

• Published in: Cluster computing Vol. 23; no. 2; pp. 879 - 894
• Main Authors: Sadiq, Muhammad Umair, Yousaf, Muhammad Murtaza, Aslam, Laeeq, Aleem, Muhammad, Sarwar, Shahzad, Jaffry, Syed Waqar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2020; Springer Nature B.V
• Subjects: Algorithms; Bioinformatics; Computer Communication Networks; Computer Science; Data compression; Data mining; Deoxyribonucleic acid; DNA; Dynamic programming; Graphics processing units; Image retrieval; Microprocessors; Operating Systems; Performance evaluation; Processor Architectures; Resource utilization; Speech recognition; Synchronism; Workloads; Performance evaluation; Edit distance; Dynamic programming; OpenMP; Parallel algorithms; GPUs
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-019-02962-w

Edit distance has applications in many domains such as bioinformatics, spell checking, plagiarism checking, query optimization, speech recognition, and data mining. Traditionally, edit distance is computed by dynamic programming based sequential solution which becomes infeasible for large problems. In this paper, we introduce NvPD, a novel algorithm for parallel edit distance computation by resolving dependencies in the conventional dynamic programming based solution. We also establish the correctness of modified dependencies. NvPD exhibits certain characteristics such as balanced workload among processors, less synchronization overhead, maximum utilization of resources and it can exploit spatial locality. It requires min ( m , n ) steps to complete as compared to diagonal based approach that completes in max ( m , n ) . Experimental evaluation using variety of random and real life data sets over shared memory multi-core systems and graphic processing units (GPUs) show that NvPD outperforms state-of-the-art parallel edit distance algorithms.