NInickysielicki·Apr 18, 2025
In Less Slow C++NVIDIA
stdexec
C++2023–2025
NVIDIA / stdexec
stdexec
`std::execution`, the standard C++ framework for asynchronous and parallel programming.
4Distinct people
4Mentions
0Explicit recommenders
2.5KGitHub stars
What to know before exploring
What it is
`std::execution`, the standard C++ framework for asynchronous and parallel programming.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 11:56:56 AM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month4 dated mentions
Observed mentions: 2023-10-27 23:47:25 UTC to 2025-04-18 15:26:35 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Oct 2023 | 2023-10-01 00:00:00 UTC | 2023-11-01 00:00:00 UTC | 1 | 0 | 1 |
| Nov 2023 | 2023-11-01 00:00:00 UTC | 2023-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2023 | 2023-12-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2024 | 2024-01-01 00:00:00 UTC | 2024-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2024 | 2024-02-01 00:00:00 UTC | 2024-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2024 | 2024-03-01 00:00:00 UTC | 2024-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2024 | 2024-04-01 00:00:00 UTC | 2024-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2024 | 2024-05-01 00:00:00 UTC | 2024-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2024 | 2024-06-01 00:00:00 UTC | 2024-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2024 | 2024-07-01 00:00:00 UTC | 2024-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2024 | 2024-08-01 00:00:00 UTC | 2024-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2024 | 2024-09-01 00:00:00 UTC | 2024-10-01 00:00:00 UTC | 2 | 0 | 2 |
| Oct 2024 | 2024-10-01 00:00:00 UTC | 2024-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2024 | 2024-11-01 00:00:00 UTC | 2024-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2024 | 2024-12-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2025 | 2025-01-01 00:00:00 UTC | 2025-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2025 | 2025-02-01 00:00:00 UTC | 2025-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2025 | 2025-03-01 00:00:00 UTC | 2025-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2025 | 2025-04-01 00:00:00 UTC | 2025-05-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
4 sourcesI haven't compared performance with the P2300 proposal yet. It seems like it's trying to unify asynchronous and parallel execution for C++, which is much broader in scope than my library.
It's true that coroutines can avoid heap allocation, but I haven't tested when or if that happens in my implementation. From the papers, it's clear that certain conditions must be met for the compiler to optimize this. If you know of any good sources on this, please let me know.
I think it's definitely worth looking into this optimization and possibly using custom allocators for specific cases. I'll also compare performance with the proposal's implementation[1] to see the difference.
Thank you for your feedback.
[1] https://github.com/NVIDIA/stdexec
In Show HN: Coros – A Modern C++ Library for Task ParallelismHave you compared perf with the reference implementation of the P2300 “Senders” proposal? https://github.com/NVIDIA/stdexec
In Show HN: Coros – A Modern C++ Library for Task ParallelismIt's not totally clear what you're looking for.
As you noted, polyhedral compilers work on a pretty restricted subset of programs, but are fairly impressive in what they do. There has been research on distributed code generation [1] as well as GPUs [2]. While there has been work on generalizing the model [3], I think the amount of parallelization that a compiler can do is still very limited by its ability to analyze the code (which is to say, highly restricted).
Then you've got a large class of data-parallel-ish constructs like Rayon [4] as well as executors which may work their way into the C++ standard at some point [5]. How much safety these provide depends greatly on the underlying language. Generally speaking, the constructs here are usually pretty restricted (think parallel map), but often you can write more-or-less arbitrary code i
Read the rest
nside, which is often not the case in the polyhedral compilers.
If you don't care so much about safety and just want access to every parallel programming construct under the sun, Chapel [6] may be interesting to you. There is no attempt here, as best I can tell, to offer any sort of safety guarantees, but maybe that's fine.
On the other end of the spectrum you have languages like Pony [7] that do very much care about safety, but (I assume, haven't looked deeply) this comes with tradeoffs in expressiveness.
(I work in this area too [8].)
Overall, there are some very stringent tradeoffs involved in parallelizing code and while it certainly has been and continues to be a very active area of research, there's only so much you can do to tackle fundamentally intractable analysis problems that pop up in the area of program parallelization.
[1]: https://www.csa.iisc.ac.in/~udayb/publications/uday-sc13.pdf
[2]: https://arxiv.org/pdf/1804.10694.pdf
[3]: https://inria.hal.science/file/index/docid/551087/filename/B...
[4]: https://docs.rs/rayon/latest/rayon/
[5]: https://github.com/NVIDIA/stdexec (disclaimer: I did a quick Google search on this, not 100% sure this is the best link)
[6]: https://chapel-lang.org/
[7]: https://www.ponylang.io/
[8]: https://regent-lang.org/