Is that correct/can you give a reference? Reading https://en.wikipedia.org/wiki/Multiple_dispatch#Use_in_pract..., julia uses it more in its standard library, but I don’t find indication that Julia is doing multiple dispatch better than, say, CLOS (https://en.wikipedia.org/wiki/Common_Lisp_Object_System) or Cecil (https://en.wikipedia.org/wiki/Cecil_(programming_language)).
I think one of the nice thing about Julia's "just ahead of time" monomorphization/devirtualization is that it allows a level of dynamism that also works on GPUs/TPUs. This post and linked paper help me understand a little bit of it: https://discourse.julialang.org/t/julia-inference-lattice-vs...
Is this level of dynamism required for conventional ML? Probably not. For physics-informed ML and probabilistic languages? Probably more likely.
Also, what types of dispatch does Jukai disallow that others allowed?
Just curious.
https://arxiv.org/pdf/1411.1607.pdf is a pretty good general resource for info about Julia's design decisions and has a section on multiple dispatch.
The reason that generating more code can be faster is that more code can actually result in fewer cache misses. Julia is so fast because it is really good at inlining, constant prop, and automatic vectorization. These optimizations all increase the amount of generated code, but remove conditionals that decrease code locality.
More code isn't randomly jumped around in, so there are not necessarily (or in practice) more cache misses. Hot paths in Julia compile down better than in many other languages (including C/C++/FORTRAN, where aliasing is much more of a problem), specifically since multiple dispatch gets resolved as much as possible (and often completely) at compile time.
If anything, by making per type versions of things, there are less cache misses since things that are similar stay together, needing less instructions on hot paths to look up types and do other stuff that other systems do hit.
The proof is in the pudding - write (or find) some good code doing similarly complex things, and test them. Julia has C/FORTRAN speeds (and better) for a lot of important tasks, with the development flexibility of Python.