[DNF2]

> I'd prefer if the language only had a single number type

> But if you talk about the general problem of naming functions differently according to their types, I feel that it's perfectly OK.

You and I have nothing in common in this entire world.

Why on earth would you even want to touch Julia with 12 lightyear stick? The whole point of Julia is diametrically opposite to your preferences.

Being opposed to multiple dispatch, polymorphism and generic programming, while 'liking' Julia, is similar to loving bycicles, but abhorring wheels.

[enriquto]

But I really love Julia! I was seduced by the idea of "like octave, but with fast loops", which is exactly what I need and the interpreter pretty much lives to that ambition. Also I love the examples with single-letter greek variables, the "f.(x)" notation, everything. And the fact that it feels like a well thought out language for numerical computing, cleaner and more beautiful than matlab/octave, and not an ugly kludge like numpy.

The multiple dispatch and oo features are some unfortunate warts, but I can live with those. Following your analogy, I love bikes and Julia is an electrically assisted bike. Sure, I would prefer if it was lighter and without the stupid motor, but it's still a bike. Not like numpy which is a horse carriage.

> You and I have nothing in common in this entire world.

for one, I 100% agree with your views on variable naming elsewhere on this thread ;)

[DNF2]

> "like octave, but with fast loops"

But the performance relies on the aggressive specialization which depends on multiple dispatch. And the adaptation to numerical computing, the cleanness and beauty is all about multiple dispatch.

To stay with the bike analogy, multiple dispatch is definitely the wheels, not the motor.

> for one, I 100% agree with your views on variable naming elsewhere on this thread ;)

Waaah! (pulls hair.) And yet, so far apart on the function naming ;)

But seriously, though. Without the incredible polymorphism and genericity, there's really nothing at all left of Julia. Multiple dispatch isn't a feature bolted onto Julia. It is the core philosophy, and the central organizing principle.

[enriquto]

> But the performance relies on the aggressive specialization which depends on multiple dispatch.

This is not a necessity, but an implementation choice. "Specialization" is an unnecessary step when everything is explicit from the beginning. I'm not talking on philosophical grounds, but thinking on concrete examples of jit systems which are really fast but have nothing to do with multiple dispatch (for example luajit).

> Without the incredible polymorphism and genericity, there's really nothing at all left of Julia.

If "matlab with fast loops" is "nothing" to you, sure. As a user of Julia, this is the killer feature for me. The rest I see as unnecessary complexity and mumbo-jumbo. But there's nothing wrong that each user has different favorite parts of the language!

[DNF2]

Actually, come to think of it, Matlab is now fully jit-compiled. As long as you stick with double precision floats, there is already a "Matlab with fast loops": it's called Matlab!

Just stay away from classes and other complex data structures, which you don't like anyway, and performance is very good indeed.

[DNF2]

Cannot reply to deeply nested posts, so I put the answer up here:

> This is not a necessity, but an implementation choice.

The performance of Julia relies on algorithmic specialization, and since you do not know what input types your code will receive, you cannot take advantage of clever specializations. I understand that you reject the concept of allowing user types that can be used by generic functions, but then you throw all the performance out the window. I want to do fast linear algebra, but your library says "no special static arrays!", "no special handling of diagonal matrices", and in general no exploitation of special structure in user types.

I want to calculate fast derivatives, but your library doesn't accept dual number types.

Your code will never be fast if it's only fast Float64, because it's when my special type elides all computation that the real speedups arrive.

> "Specialization" is an unnecessary step when everything is explicit from the beginning.

But you cannot know what it should be explicit about. I give your `sum` function a special Range vector, or a BitVector, or a OneHot vector. What should it do? It has to fall back to the same thing it does with all arrays: add-add-add-add-add. What is the norm of my UnitVector type? Your library has to ploddingly square, sum and sqrt. In the meantime, multiple dispatch just replaces the call with the number 1.0.

> If "matlab with fast loops" is "nothing" to you, sure.

As a daily user of Matlab for 25 years, several tens of thousands of hours of grudging use, I assure you it would be less than nothing. Matlab is a tragic, horrible mess of a language, and it's not even that slow! I rue every hour I've spent on it. If Julia were slow, and Matlab faster, I would still prefer Julia.

Also, Matlab has OOP and sigular dispatch.

> But there's nothing wrong that each user has different favorite parts of the language!

Well, as I am trying to say, multiple dispatch isn't a part of the language. It is the language. Everything revolves around that concept.