NEnew_user55·Jul 14, 2026
You can write the cuda kernels and directly differentiate them using Enzyme. Last time I checked KOKKOS was still WIP: https://github.com/EnzymeAD/Enzyme/issues?q=kokkos
In Differentiable Fortran with LFortran and EnzymeEnzymeAD
Enzyme
LLVM2020–2026
EnzymeAD / Enzyme
Enzyme
High-performance automatic differentiation of LLVM and MLIR.
8Distinct people
9Mentions
0Explicit recommenders
1.7KGitHub stars
What to know before exploring
What it is
High-performance automatic differentiation of LLVM and MLIR.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM 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 year9 dated mentions
Observed mentions: 2020-12-08 18:12:02 UTC to 2026-07-14 19:16:37 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
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 |
|---|---|---|---|---|---|
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 3 | 1 | 2 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
9 sourcesIt makes use of https://github.com/EnzymeAD/enzyme which is an LLVM plugin and since Rust also uses LLVM in its backend, we can enable this plugin in our Rust toolchain when autodiff is enabled. So, it is a bit of compiler black magic rather than a direct implementation in the standard library.
In CUDA-oxide: Nvidia's official Rust to CUDA compilerlattner left google and was the primary reason they chose swift, so they lost interest.
if you're asking from an ML perspective, i believe the original motivation was to incorporate automatic differentiation in the swift compiler. enzyme would be the spiritual successor to that.
https://github.com/EnzymeAD/Enzyme
In Ask HN: What Happened to TensorFlow Swift?Enzyme dev here, so take everything I say as being a bit biased:
While, by design Enzyme is able to run very fast by operating within the compiler (see https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b... for details) -- it aggressively prioritizes correctness. Of course that doesn't mean that there aren't bugs (we're only human and its a large codebase [https://github.com/EnzymeAD/Enzyme], especially if you're trying out newly-added features).
Notably, this is where the current rough edges for Julia users are -- Enzyme will throw an error saying it couldn't prove correctness, rather than running (there is a flag for "making a best guess, but that's off by default"). The exception to this is garbage collection, for which you can either run a static analysis, or stick to the "officially supported" subset of Julia that Enzyme spe
Read the rest
cifies.
Incidentally, this is also where being a cross-language tool is really nice -- namely we can see edge cases/bug reports from any LLVM-based language (C/C++, Fortran, Swift, Rust, Python, Julia, etc). So far the biggest code we've handled (and verified correctness for) was O(1million) lines of LLVM from some C++ template hell.
I will also add that while I absolutely love (and will do everything I can to support) Enzyme being used throughout arbitrary Julia code: in addition to exposing a nice user-facing interface for custom rules in the Enzyme Julia bindings like Chris mentioned, some Julia-specific features (such as full garbage collection support) also need handling in Enzyme.jl, before Enzyme can be considered an "all Julia AD" framework. We are of course working on all of these things (and the more the merrier), but there's only a finite amount of time in the day. [^]
[^] Incidentally, this is in contrast to say C++/Fortran/Swift/etc, where Enzyme has much closer to whole-language coverage than Julia -- this isn't anything against GC/Julia/etc, but we just have things on our todo list.
[ps sorry if this ended up as a dup, I meant to reply deeper in the tree, so I deleted the older comment and moved it here].
Idk, Enzyme is pretty next gen, all the way down to LLVM code.
https://github.com/EnzymeAD/Enzyme
In Jax vs. Julia (Vs PyTorch)Wsmoses/Enzyme: High-performance automatic differentiation of LLVM
5 points · Open discussionHi all, another author here and happy to answer any questions!
Some more relevant links for the curious
Github: https://github.com/wsmoses/Enzyme
Paper: https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b...
Basically the long story short is that Enzyme has a couple of interesting contributions:
1) Low-level Automatic Differentiation (AD) IS possible and can be high performance
2) By working at LLVM we get cross-language and cross-platform AD
3) Working at the LLVM level actually can give more speedups (since it's able to be performed after optimization)
4) We made a plugin for PyTorch/TF that uses Enzyme to import foreign code into those frameworks with ease!
In Enzyme – High-performance automatic differentiation of LLVMHi all, author here.
A couple of relevant links for the curious
Github: https://github.com/wsmoses/Enzyme
Paper: https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b...
Project: enzyme.mit.edu
Basically the long story short is that Enzyme has a couple of really interesting contributions:
1) Low-level AD IS possible and can be high performance
2) By working at LLVM we get cross-language and cross-platform AD
3) Working at the LLVM level actually can give more speedups (since it's able to be performed after optimization)
4) We made a plugin for PyTorch/TF that uses Enzyme to import foreign code into those frameworks with ease!
In Enzyme: Cross-language Automatic differentiation for LLVM IRIt looks like the links to the code have some junk characters at the end. It should be https://github.com/wsmoses/Enzyme.
In Enzyme: Cross-language Automatic differentiation for LLVM IR