I made the caveat that it's sufficiently optimized. I'm not talking about full loops on native Python arrays or something silly like that. If two people who know what they're doing try to optimize some scientific algorithm in a language without extra penalties (C, C++, Fortran, Julia, Go, Rust, etc.) the runtimes end up quite similar, at least within a few x. Of course someone can make it worse (there exist many bad coders in science...), but I'm not talking about that. I'm just saying that spending 2 years to play with some assembler code is not likely to give you some amazing speedup, while specializing algorithms to your problem is a clear and tested way to get something that is much more efficient.