Abstract
We develop a method for performing convolutions efficiently in a word RAM model of computation, having a word size of w=Ω(logn) bits, where n is the input size. The basic idea is to pack several elements of the input vector into a single computer word, effectively enabling parallel computation of convolutions. The technique is applied to approximate string matching under Hamming distance. The obtained algorithms are the fastest known. In particular, we reduce the complexity of the Amir et al. (2000) algorithm for k-mismatches from O(nklogk) to O(n+nk/wlogk). Those algorithms impose some (not severe) limitation on the pattern length, m. We present another, less efficient however, technique based on word-level parallelism, which works without the pattern length limitation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
