[Semi-OT] I don't want to leave this language!

[Jon Degenhardt via Digitalmars-d-learn]

On Wednesday, 7 December 2016 at 16:33:03 UTC, bachmeier wrote:
> On Wednesday, 7 December 2016 at 12:12:56 UTC, Ilya Yaroshenko 
> wrote:
>
>> R, Matlab, Python, Mathematica, Gauss, and Julia use C libs. 
>> --Ilya
>
> You can call into those same C libs using D. Only if you want a 
> pure D solution do you need to be able to rewrite those 
> libraries and get the same performance. D is a fine solution 
> for the academic or the working statistician that is doing 
> day-to-day analysis. The GC and runtime are not going to be an 
> obstacle for most of them (and most won't even know anything 
> about them).

A cycle I think is common is for a researcher (industry or 
academic) to write functionality in native R code, then when 
trying to scale it, finds native R code is too slow, and switches 
to C/C++ to create a library used in R. C/C++ is chosen not 
because it the preferred choice, but because it is the common 
choice.

In such situations, the performance need is often to be quite a 
bit faster than native R code, not that it reach zero overhead. 
My personal opinion, but I do think D would be a very good choice 
here, run-time, phobos, gc, etc., included. The larger barrier to 
entry is more about ease of getting started, community (are 
others using this approach), etc., and less about having the 
absolutely most optimal performance. (There are obviously areas 
where the most optimal performance is critical, Mir seems to be 
targeting a number of them.)

For D to compete directly with R, Python, Julia, in these 
communities then some additional capabilities are probably 
needed, like a repl, standard scientific packages, etc.