Scientific computing and parallel computing C++23/C++26
bcarneal at gmail.com
Thu Jan 13 07:23:40 UTC 2022
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.
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