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

[Bruce Carneal]

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++.

There *are* improvements to be made but, by my lights, dcompute 
is already better than CUDA in many ways.  If we improve 
usability, make dcompute accessible to "mere mortals", make it a 
"no big deal" choice instead of a "here be dragons" choice, we'd 
really have something.

By contrast, I just don't see the C++ crowd getting to 
sanity/simplicity any time soon... not unless ideas from the 
circle compiler or similar make their way to mainstream.