We present new algorithms for the k mismatches version of approximate string matching. Our algorithms utilize the SIMD (Single Instruction Multiple Data) instruction set extensions, particularly AVX2 and AVX-512 instructions. Our approach is an extension of an earlier algorithm for exact string matching with SSE2 and AVX2. In addition, we modify this exact string matching algorithm to work with AVX-512. We demonstrate the competitiveness of our solutions by practical experiments. Our algorithms outperform earlier algorithms for both exact and approximate string matching on various benchmark data sets. • New algorithms for the k mismatches version of approximate string matching. • We modify the exact string-matching algorithm to work with AVX-512. • Demonstrate the competitiveness of our solutions by practical experiments.

Summary The order‐preserving pattern matching problem has gained attention in recent years. It consists in finding all substrings in the text, which have the same length and relative order as the input pattern. Typically, the text and the pattern consist of numbers. Since recent times, there has been a tendency to utilize the ability of the word RAM model to increase the efficiency of string matching algorithms. This model works on computer words, reading and processing blocks of characters at once, so that usual arithmetic and logic operations on words can be performed in one unit of time. In this paper, we present a fast order‐preserving pattern matching algorithm, which uses specialized word‐size packed string matching instructions, grounded on the single instruction multiple data instruction set architecture. We show with experimental results that the new proposed algorithm is more efficient than the previous solutions. ©2016 The Authors. Software: Practice and Experience Published by John Wiley & Sons Ltd.

The problem of order-preserving matching has gained attention lately. The text and the pattern consist of numbers. The task is to find all the substrings in the text which have the same length and relative order as the pattern. The problem has applications in analysis of time series. We present a new sublinear solution based on filtration. Any algorithm for exact string matching can be used as a filtering method. If the filtration algorithm is sublinear, the total method is sublinear on average. We show by practical experiments that the new solution is more efficient than earlier algorithms.