DaemonMode.jl VS julia-numpy-fortran-test

Thats for an entry point, you can search `Base.@main` to see a little summary of it. Later it will be able to be callable with `juliax` and `juliac` i.e. `~juliax test.jl` in shell.

DynamicalSystems looks like a heavy project. I don't think you can do much more on your own. There have been recent features in 1.10 that lets you just use the portion you need (just a weak dependency), and there is precompiletools.jl but these are on your side.

You can also look into https://github.com/dmolina/DaemonMode.jl for running a Julia process in the background and do your stuff in the shell without startup time until the standalone binaries are there.

> If I were to use e.g. Rust with polars, load time would be virtually none.

Because you're compiling...

And if you need to do the same in Julia, you should also pre-compile or some other method like https://github.com/dmolina/DaemonMode.jl (their demo shows loading a database, with subsequent loads after the first one taking roughly ~0.2% of the first)

Well, not nicely certainly, but:

https://github.com/dmolina/DaemonMode.jl

> portable

Neither is python - it just relies on universal availability. Over time…

You can get around a lot of these problems with DaemonMode.jl though.

You might want DaemonMode.jl

https://github.com/dmolina/DaemonMode.jl can possibly help in the future. Leaving it here so that people know this is planned.

If you strongly prefer to run scripts though, then you can use the package https://github.com/dmolina/DaemonMode.jl in order to re-use a Julia session between multiple scripts, saving you recompilation time.

No surprise, because NumPy is implemented with Fortran which is designed to be efficient and fast at mathematical operations. Rust is not. And Python is not either, which is why it uses Fortran under the covers.

I wouldn't be surprised to Rust numerical libraries created similar to NumPy which also use Fortran, for the same reasons.

If you want a real comparison, try NumPy vs Julia:

https://www.matecdev.com/posts/numpy-julia-fortran.html

Is the python code missing a square root?

https://github.com/mdmaas/julia-numpy-fortran-test/blob/main...

For fun I ran in Matlab with a 2.9 GHz i7-7820HQ and get about 1.83s for N=10,000 single threaded.

    A = exp((k*1i)*sqrt(a.^2 + (a.^2)'))