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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