Scientific computing and parallel computing C++23/C++26

[bachmeier]

On Thursday, 13 January 2022 at 07:23:40 UTC, Bruce Carneal wrote:
> On Thursday, 13 January 2022 at 03:56:00 UTC, bachmeier wrote:
>> On Wednesday, 12 January 2022 at 22:50:38 UTC, Ola Fosheim 
>> Grøstad wrote:
>>
>>> My gut feeling is that it will be very difficult for other 
>>> languages to stand up to C++, Python and Julia in parallel 
>>> computing. I get a feeling that the distance will only 
>>> increase as time goes on.
>>>
>>> What do you think?
>>
>> It doesn't matter all that much for D TBH. Without the basic 
>> infrastructure for scientific computing like you get out of 
>> the box with those three languages, the ability to target 
>> another platform isn't going to matter. There are lots of 
>> pieces here and there in our community, but it's going to take 
>> some effort to (a) make it easy to use the different parts 
>> together, (b) document everything, and (c) write the missing 
>> pieces.
>
> I disagree.  D/dcompute can be used as a better general purpose 
> GPU kernel language now (superior meta programming, sane nested 
> functions, ...).  If you are concerned about "infrastructure" 
> you embed in C++.

I was referring to libraries like numpy for Python or the 
numerical capabilities built into Julia. D just isn't in a state 
where a researcher is going to say "let's write a D program for 
that simulation". You can call some things in Mir and cobble 
together an interface to some C libraries or whatever. That's not 
the same as Julia, where you write the code you need for the task 
at hand. That's the starting point to make it into scientific 
computing.

On the embedding, yes, that is the strength of D. If you write 
code in Python, it's realistically only for the Python world. 
Probably the same for Julia.