Julia vs. D?

Brian Rogoff via Digitalmars-d digitalmars-d at puremagic.com
Tue May 6 07:35:31 PDT 2014


On Tuesday, 6 May 2014 at 11:20:33 UTC, Paulo Pinto wrote:
> On Tuesday, 6 May 2014 at 09:11:30 UTC, Chris wrote:
>> I recently came across this article 
>> http://www.wired.com/2014/02/julia/. On the Julia homepage 
>> there are some benchmarks times relative to C. I know that 
>> bearophile has mentioned Julia several times on this forum. 
>> Has anyone compared D's vs Julia's performance as well as 
>> design features?
>
> I can only comment on design features.
>
> You can think of Julia as a dynamic language similar to Python, 
> with optional typing and for such a young language, a quite 
> good JIT compiler backed by the LLVM backend.
>
> It is a multi-paradigm language, with an OO approach based on 
> multi-methods and direct support for scientific programming.
>
> The target audience are the scientifc community that makes use 
> of R, Python with NumPy and so on, which are currently 
> disappointed with the performance of said systems. Their goal 
> is to keep the programming flexibility of R and Python, while 
> improving the performance without having to be forced to write 
> C code.

Excellent summary, one quibble, you omit MATLAB and Octave users 
from your target audience, when they may be the most important 
one. Julia reads much more like MATLAB than like R or Python, and 
numerical linear algebra is one of the things Julia is being 
aimed at.

It has a very rich language of types, and a macro system. 
Disappointingly, whilst you can annotate function arguments and 
variables with types, you can't annotate the function itself with 
a return type.

TL;DR MATLAB reimagined by Lisp hackers. I like it!

Not really competing in the same space as D. Yes, I know, I'm 
pigeonholing D, which is supposed to be a wide spectrum language, 
etc. etc.


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