[tgv]

Idk, but just a few weeks ago I started looking at Julia, partly because of the performance claims. I wanted to write a program a bit heavier than your average starter program, so I wrote a back-tracker (automatic layout for stripboards, to be precise). It was

* interesting (not fun) to find out how Julia works

* annoying AF to discover that much of the teaching material was hidden behind some 3rd party website, presumably in videos (I didn't bother to register, but started browsing the manual instead). What's wrong with text?

* unnecessarily complex because the documentation for the basic functions is nearly inaccessible to beginners.

But, I managed to get a simple layout system up and running, and it wasn't fast. I rewrote it in Go (the language in which I'm currently working most), and it was literally >100x faster. And that should not be due to the startup costs, because a backtracker shouldn't have that much overhead JIT-ing.

I think I can now say that I can't see the use case for Julia. "Faster than Python" is simply not good enough, and for the rest there are no redeeming features. Perhaps the fabled partial differential equation module is worth it, but that can get ported to other languages, I guess.

[oivey]

Your relative skill and time invested in Julia vs Go makes that a not very fair comparison, I think. A 100x difference in performance is probably a sign of something that could be fixed in your code (common one: type instability). In general, Julia is being used to implement things like competitive versions of BLAS. Your Julia code can almost certainly be made much faster.

Coming from a Python and C++ background, I found it sufficient to just read the docs and do some Advent of Code problems to get productive in Julia. What videos are you talking about? https://docs.julialang.org/en/v1/manual/performance-tips/ I found to be a pretty good document on why and when Julia can be slow.

[DNF2]

I simply do not understand how some people are able to form so strong opinions in such a short time, and spew out disdain and negativity on the most flimsy basis. It's a matter of temperament, I guess.

Julia performance should be on par with Go, if it's slower, read the performance tips in the manual. As for teaching material on 3rd party websites, I don't know what you mean. The Julia manual is available from the julialang.org website.

As for re-writing DifferentialEquations, that is extremely strongly tied to the multiple dispatch paradigm, re-writing it would be hard. What you can get is wrappers like diffeqpy and diffeqr, which call out to Julia.

[tgv]

You can verify that the teaching materials are not really up to scratch. Even nim and zig, which have less resources behind them, I think, do a better job there. The manual is a reference manual, and it was difficult to find all the operations on arrays. E.g., the difference between Array{Int} and Array{Int,1} is not clarified from the start.

And as I said: I wrote a straight-forward backtracker. It just recursive function calls: check a possible state for the current item, and when successful, update the overall state and move on to the next item; on return, try another state for the current item, until the search space is exhausted. There's not a lot to optimize, nor is there a lot of work for a JIT compiler.

> on the most flimsy basis

I've got more gripes. Forward type declaration to name one. But I'm not spewing disdain: I just don't see Julia take a larger role in general software development.

[DNF2]

I have no particular opinion on the teaching materials, I just use the manual and the discussion fora, so I don't know. But if a third party offers teaching materials, it's not so strange if it resides on their third party website.

As for performance, I'm not really talking about 'optimization'. Your implementation may simply have used some pattern that should be avoided, such as global variables, type instabilities, abstract types in structs, or some inappropriate data structures. If it's a microbencmhark, then there are some things to keep in mind.

These are not really optimizations, but basic performance principles. I cannot know that you are unaware of them, but your statement that 'there's not a lot to optimize' make me suspect that this could be the case. The unusual thing about Julia is that it's both dynamic and compiled, so that code that would simply not compile in static languages instead ends up slow.

[oivey]

If I had to guess, your problem is type stability. Are you using NamedTuples to store your state and items you’re iterating over? If the keys and are not all the same and value types don’t stay the same (e.g. something initialized as zero(Int) and then accumulated into with Float64s) then performance will suffer. Another possibility is that you have a data type not is not concrete in an inner loop. For example, Array{Real} will be slower than Array{Float64} because an array of Reals has to support arrays mixing Float32 and Float64. If you had this in a function definition the likely correct thing to do is Array{<:Real}, which means the element type of the array must be a subtype of Real. Maybe even better, just drop the type annotations. They very, very rarely improve performance because Julia relies on compile time type inference.

Failed or bad type inference is almost always the cause of performance issues in Julia. Getting a feel for when the compiler can infer things or not takes practice, but it’s a lot easier than the semantics of generic programming systems IMO.

The REPL is really great for learning. If you type “Array{Int} == Array{Int, 1}” the result is false. If you type “?Array” it prints the docstring which gives some guidance on how to use one versus the other.