"What started as a requirement for my work spun out into a fully-fledged project of its own. Speed and Memory Efficiency - two things not commonly associated with the Levenshtein Distance algorithm. In case the name didn't give it away, these have been my two main goals with Quickenshtein. Primarily taking advantage of the Single Instruction Multiple Data (SIMD - aka. Hardware Intrinsics) features in .NET Core 3, Quickenshtein can calculate the "edit distance" between large strings extremely fast. Things go even faster when factoring in the ability to leverage multiple threads for the calculation, even the .NET Framework performs extremely fast. Current plans are to keep tweaking and optimizing it - there are still some low-hanging fruit when it comes to performance. In the future, other edit distance-related algorithms may be included too like Damerau–Levenshtein distance and Longest Common Subsequence (LCS)."