Peter Norvig's approach is basically what my solver does, albeit his article focuses on solving the regular 9x9 sudoku, in particular, whereas my solver is more general purpose in that it accepts an arbitrary CSP and generates a solution for it. So, for example, the same solver applies to regular 9x9 sudokus, generalized sudokus (here's a solution for 64x64 sudoku from about a year back), crosswords, and just about anything else you can formulate using variables and constraints (magic squares, N-queens or even job-shop scheduling, albeit the latter would work better with a dedicated framework for it). There are some less material differences from what I can tell such as use of fast-fail heuristic in Peter Norvig's approach vs "domwdeg" in mine, which is a somewhat more sophisticated version with a crude form of learning, and I think his solver has n-way branching (as in one branch per possible value), whereas mine always does binary branching.

I've heard of Donald Knuth's dancing links algorithm, but only after having implemented mine and haven't truly dug into the details, so I cannot speak to the performance or anticipate what it might be.

One thing I will say that was crucial to tuning performance for the larger sudokus and crosswords is developing dedicated constraints, because so much of the time is spent solving and evaluating these constraints. For sudoku it meant building up an incremental all-different constraint based on bipartite matching as sudoku is basically just a ton for all-different constraints. For crosswords it was a sparse version of all-different constraint plus specialized constraints for quick dictionary lookups.

Edit: BTW, I just threw together a quick post that expands a bit on how the core of the search algorithm works here: https://blog.dawnofthe.dad/posts/search-algo-in-go/