Well, for example at the very least in Common Lisp you'll have much more joy with higher-order functions than with loops. The simple reason for that is the existence of compiler macros (http://clhs.lisp.se/Body/03_bba.htm) which can replace function compositions with arbitrary code. And it's much easier to figure out what the function composition does than to write a loop vectorizer.
Julia also has similar macro capabilities to common lisp.
> And it's much easier to figure out what the function composition does than to write a loop vectorizer
I probably agree but complicated loop vectorisation would probably expressed in Einstein tensor contraction notation such as Tullio.jl provides. I wonder a Fourier transform would look using function decomposition.
@tullio F[k] := S[x] * exp(-impi/8 (k-1) * x) (k ∈ axes(S,1))
Maybe a new iteration construct specifically supporting parallelization would help here. Some of these things might already work quite well, for example it should not be difficult to discern from (let me use ITERATE's example)
(iterate (for el in list)
(finding el minimizing (length el)))
that you're computing argmin using a pure function, which should be easy to do in parallel, whereas the way you'd typically write this in C would be probably difficult for the compiler to confirm as such (it would have to look for pattern such as "if (f(current)<f(best)) { best=current; }" or something like that. (BTW have you seen another loop construct supporting argmin/argmax in another language? I haven't so far.) Whether this could be done somewhat generally and extensibly is something I have to ponder on.No, I AM going to write procedural code, and it WILL be faster than your "high IQ" 1 line recursive solution. Also funny to see how little recursion gets used in CUDA/Pytorch/GPU programming - which is what we are seeing to be more and more important over time.
Julia code might also uses a lot of in place operations which would be hard for a compiler to infer as safe.