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

Bruce Carneal bcarneal at
Thu Jan 13 02:16:33 UTC 2022

On Wednesday, 12 January 2022 at 22:50:38 UTC, Ola Fosheim 
Grøstad wrote:
> I found the CppCon 2021 presentation
> [C++ Standard 
> Parallelism]( by 
> Bryce Adelstein Lelbach very interesting, unusually clear and 
> filled with content. I like this man. No nonsense.
> It provides a view into what is coming for relatively high 
> level and hardware agnostic parallel programming in C++23 or 
> C++26. Basically a portable "high level" high performance 
> solution.
> He also mentions the Nvidia C++ compiler *nvc++* which will 
> make it possible to compile C++ to Nvidia GPUs in a somewhat 
> transparent manner. (Maybe it already does, I have never tried 
> to use it.)
> 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?

Given the emergence of ML in the commercial space and the 
prevalence of accelerator HW on SoCs and elsewhere, this is a 
timely topic Ola.

We have at least two options: 1) try to mimic or sit atop the, 
often byzantine, interfaces that creak out of the C++ community 
or 2) go direct to the evolving metal with D meta-programming 
shouldering most of the load.  I favor the second of course.

For reference, CUDA/C++ was my primary programming language for 
5+ years prior to taking up D and, even in its admittedly 
less-than-newbie-friendly state, I prefer dcompute to CUDA.

With some additional work dcompute could become a broadly 
accessible path to world beating performance/watt libraries and 
apps. Code that you can actually understand at a glance when you 
pick it up down the road.

Kudos to the dcompute contributors, especially Nicholas.

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