This is amazing.. ive been working with custom CUDA kernels and https://crates.io/crates/cudarc for a long time, and this honestly looks like it could be a near drop-in replacement.
im especially curious how build times would compare? Most Rust CUDA crates obv rely on calling CMake or nvcc, which can make compilation painfully slow. coincidentally, just last week i was profiling build times and found that tools like sccache can dramatically reduce rebuild times by caching artifacts - but you still end up paying for expensive custom nvcc invocations (e.g. candle by hugging face calls custom nvcc command in their kernel compilation): https://arpadvoros.com/posts/2026/05/05/speeding-up-rust-whi...
> Most Rust CUDA crates obv rely on calling CMake or nvcc, which can make compilation painfully slow.
I anecdotally haven't hit this; see the `cuda_setup` crate I made to handle the build scripts; it is a simple `build.rs` which only recompiles if the file changes, and it's a tiny compile time (compared to the rust CPU-side code)
perhaps not drop-in, but all my workflows with cudarc have always been "i make cuda kernel, i use cudarc for ffi to said kernels, i call via rust" - which for this case is pretty analogous
briefly looking at the repo, looks like the main workflow is using rustc-codegen-cuda to convert rust -> MIR -> pliron IR -> LLVM IR -> PTX, which is embedded in the host binary, where then cuda-core loads embedded PTX at runtime onto the GPU
but, if you arent directly making cuda kernels and just want cudarc for either calling existing kernels or other cuda driver api access then cudarc is lighter-weight option? or just use one of the sub-crates in this repo like cuda-core for those apis
Hi, author of cuda-oxide here. Yes, I think that’s basically the right framing: cudarc and cuda-oxide sit at different points in the stack.
cudarc is a host-side CUDA API for Rust: loading modules, managing contexts/streams/events/memory, launching kernels, and accessing CUDA libraries/driver APIs. If your workflow is “I already have CUDA C++/PTX/CUBIN kernels and want to call them from Rust”, cudarc is a very natural fit.
cuda-oxide is focused on the other side of the problem: writing the GPU kernel itself in Rust and compiling it through rustc/MIR into GPU code. The generated PTX is then embedded in the host binary and loaded at runtime by our host-side pieces.
We include cuda-core/cuda-host because we need an end-to-end path for “write Rust kernel, build it, launch it”, but that doesn’t mean the generated PTX is tied forever to our launcher. We’d like the PTX from cuda-oxide to be usable from other host-side CUDA APIs too, including cudarc, and we’re exploring ways to make that interop smoother.
So the short version is: cudarc is about driving CUDA from Rust; cuda-oxide is about generating CUDA device code from Rust. They’re complementary rather than replacements for each other.
I am observing the same from the article... is it heavily inspired by Cudarc, i.e. is this intentional, or are we reading too much into this, given Cudarc is a light abstraction over the CUDA api?
I'm quite interested in how they dealt with Rust's memory model, which might not neatly map to CUDA's semantics. Curious what the differences are compared to CUDA C++, and if the Rust's type system can actually bring more safety to CUDA (I do think writing GPU kernels is inherently unsafe, it's just too hard to create a safe language because of how the hardware works, and because of the fact that you're hyper-optimizing all the time)
1. use-after-free, drop semantics vs manual cudaFree
2. kernel args enforced using `cuda_launch!` whereas CPP void* args is just an array of pointers, validating count only
3. alias mutable writes. e.g. CPP can have more than one thread writing out[i] with same i and this will compile. but DisjointSlice<T> with ThreadIndex doesnt have any public constructor (see: https://github.com/NVlabs/cuda-oxide/blob/2a03dfd9d5f3ecba52...) and only using API of `index_1d` `index_2d` and `index_2d_runtime`
This is explained in some detail in the docs. There is a safe layer, a mostly safe layer, and an unsafe layer. Some clunkiness is needed for safe-yet-parallel work that they couldn’t easily fit into the Rust Send/Sync model.
FWIW, Rust’s memory model is more or less completely identical to C++’s, by design. Atomics work the same, there’s provenance, and so on.
Whether it is a convenient language for GPU programming probably remains to be seen, but I definitely wouldn’t be surprised if you could make a decent DSL-like API for writing safe code that leverages the full spectrum of GPU oddities. That’s what CUDA is, right?
Originally CUDA hardware was designed without a specific memory model, after C++11, NVidia went into a multi year effort to redesign the hardware to match C++ memory model semantics.
I think it depends on the objective. My pattern-matching brain says there will be interest in addressing this.
From my perspective of someone who writes applications in Rust and sometimes wants to use GPU compute in these applications: I don't care. If we can leverage the memory model or ownership model in a low-friction way, that's fine. If it makes it a high friction experience, I would prefer not to do it that way.
The baseline is IMO how Cudarc currently does it. I don't think there is much memory management involved; it's just imperative syntax wrapping FFI, and some lines in the build script to invoke nvcc if the kernels change.
I wonder what it means for Slang[0]. Presumably the point is that people want to do GPU programming with a more modern language. But now you can just use Rust...
There's library code in rust that manages GPU memory and schedules pipelines and use a slang reflection to ensure memory layouts between rust and shaders match.
Writing shaders is materially different from writing CUDA kernels, at least for now. Shaders are simultaneously higher and lower level, and have a lot of idiosyncrasies as a result of being designed for a specific and limited set of driver/GPU features.
Stuff like descriptor sets, resource registers, dispatch limitations, …
Weird. There's a recent NVIDIA MLIR that is quite good and fast. Or they could target the even easier and more recent/fashionable tile IR [1] used by CuTile [2] (a little bit higher level but significantly easier to target, only loses on epilogue fusion and similar).
> (em dash) no DSLs, no foreign language bindings, just Rust.
Official CUDA port and they couldn't even bother with the introductory paragraph.
Okay, I'll try to ignore it and read the docs. Hey a custom IR, this sounds interesti-
> MLIR’s implementation, however, is C++ with a side of TableGen, a build system that requires you to compile all of LLVM, and debugging sessions that make you question your career choices.
if they didnt use AI for their webpage people would say "why doesnt NVIDIA write its website and documentation with AI? don't they believe their own story about AI factories and employees managing thousands of agents doing the work for them?"
this is exactly on brand dog-fooding I would expect from an AI hyper
that ship has long sailed, "it no longer matters"
saying a codebase, an article was written with AI doesn't mean much, it could be good, it could be bad. folks often say it to generate outrage, but that means nothing. is the codebase great, good, bad, terrible? that's the only thing that matters.
Even as someone who uses a lot of AI, if you can't be bothered to at least give it a prompt like "Go through the documentation and comments in detail and remove any obvious AI shibboleths like emdashes, it's not x it's y, rule-of-three, 'delve', excessive grandiosity and flourishes, boldness, bullet points, etc", you should receive a brisk kick in the rear.
Not really but they jump right out at you after a few minutes chatting with it. I also asked the AI and it was pretty subjectively accurate, especially if you force it to cross reference with web searches and especially google's ngram corpus (you can readily see that 'delve' and some of the other rhetorical constructs are quite uncommon in human speech)
Might be the only thing that matters to you. And, perhaps, the only thing that matters in a functional sense. But, whether it’s human-coded/written or not matters deeply to some.
No, it's always bad. It's just on a spectrum of how competent is the viewer (you and me) to notice how bad it is. It's all shit, the question is whether you are skilled and experienced enough to tell.
That's a lost battle even in the Rust community: Firefox's oxidation, Ferrous Systems, Redox, OxidOS, OxCaml (OCaml extensions partly inspired by Rust)… and every crate referencing oxidation in its name.
The quoted writing is AI slop, and OP is reacting to the fact that they did not write even the introductory text themselves (or at least bother to edit out clear AI/slop indicators)
agree not related to the rust to cuda compiler, you are right!
But I have to say worth to look at upcoming new stuff, as this is kind a wow rust on good old CUDA.
Does anyone know if this will let you share structs between host and device? That is the big thing missing so far with existing rust/CUDA workflows. (Plus the serialization/bytes barrier between them)
Yes, absolutely. That is one of the advantages of cuda-oxide being single-source Rust: the host and device code can refer to the same Rust types, and the compiler has enough information to make the device-side layout match what rustc chose on the host.
So the intended workflow is not “define a Rust struct on the host, define a matching CUDA C++ struct for the device, then serialize bytes between them.” It is much closer to “define `MyStruct` once in Rust, put a `DeviceBuffer<MyStruct>` on the GPU, and write kernels that take `&[MyStruct]`, `*const MyStruct`, etc.”
There are two important pieces under the hood:
1. At the kernel boundary, cuda-oxide scalarizes aggregate parameters where needed. For example, slices become pointer + length, and simple structs can be flattened into fields for launch ABI purposes.
2. For actual struct layout, we use rustc’s computed layout rather than assuming declaration order or a C ABI. That matters because Rust is allowed to reorder/pad `repr(Rust)` structs. The device lowering carries those offsets/padding through so field access on the GPU matches the host-side layout.
So for plain data structs, nested structs, numeric fields, arrays, etc., yes, this is very much the goal: share the type directly instead of maintaining a separate CUDA representation or crossing a bytes/serialization boundary.
The caveat is the usual one: this does not make arbitrary host-owned Rust heap graphs GPU-addressable. A `Vec`, `String`, `Box`, trait object, or host pointer still contains an address, and that address has to refer to memory the GPU can actually access. For those cases you still need device allocation, unified/HMM memory, or a GPU-friendly representation.
But for the common “I have a Rust data type and want kernels to consume/update arrays of it” case: yes, that is exactly the kind of friction cuda-oxide is meant to remove.
One thing I’ve been wary about with Rust for CUDA is the bit of overhead that Rust adds that is usually negligible but might matter here, like bounds checks on arrays. Could it cause additional registers to get used, lowering the concurrency of a kernel?
Oh lord. If this is the trend, I probably can't avoid improving my Rust language knowledge in the long term. I hate reading Rust so much right now. I guess I just have to get over that hump.
Learning Rust is more alike to learning a new programming paradigm (e.g. functional when you only know imperative) than a new language with different syntax only. If you ignore that and try to jump directly to writing code more or less the same way as you used to, it will be painful. So take it slow and follow along with The Book (https://doc.rust-lang.org/book/). It all makes sense eventually and is very much worth it!
Hell yea! I have been doing it with Cudarc (Kernels) and FFI (cuFFT). Using manual [de]serialization between byte arrays and rust data structs. I hope this makes it lower friction!
> A GPU kernel runs thousands of threads that all see the same memory at the same time. On a CPU, Rust prevents data races through ownership and borrowing – one mutable reference, no aliases, enforced at compile time. On a GPU, you have 2048 threads per SM, all launched from the same function, all pointing at the same output buffer. The borrow checker was not designed for this.
> cuda-oxide solves the problem in layers. The common case – one thread writes one element – is safe by construction, no unsafe required. The uncommon cases – shared memory, warp shuffles, hardware intrinsics – require unsafe with documented contracts. And the frontier cases – TMA, tensor cores, cluster-level communication – are fully manual, matching the complexity of the hardware they control.
That's.. not really Rusty. In Rust, we create new safe abstractions when the existing ones don't quite map to the problem at hand. See for example what's done in Rust for Linux
If it's not safe.. what's the point of Rust?
(it's okay to offer unsafe APIs for people that need to squeeze the last bit of performance, but this shouldn't be the baseline)
I compare this with userspace libs for APIs like io_uring and vulkan. designing safe APIs for them stuff is kind of hard (there's even some unsound attempts)
There is actually work on adding autodiff to Rust, maybe not really first class citizen, but at least build in: https://doc.rust-lang.org/std/autodiff/index.html (it is still at a pre-RFC stage so it is not something that soon will be added)
Incredible, I have never heard of std::autodiff before. Isn't it rare for a programming language to provide AD within the standard library? Even Julia doesn't have it built-in, I wouldn't expect Rust out of all languages to experiment it in std.
It 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.
So, we have stainless, which means Linux code that never rusted. Now we need someone to make phosphorus so that we can turn rusty code into old iron. Then GPL fans can run Rust boxes, Stainless machines, or future proofed iron work horses.
All software can come on three editions. Stainless drivers that were never rusty, oxidized drivers that used Rust on existing code, and Iron editions which is where someone converted the Rust back to C using the new phosphoric tool...
Diversity can be our strength.
Making Iron C/c++ code can be called acid washing if it was rusted.
This is a bit good for Rust if you want to use the language with CUDA. The problem is, it still doesn't really move the needle if you really don't like running closed source drivers and runtime binaries and care about open source.
Continuing from this discussion [0], this only makes it a Rust or a CUDA problem rather than a Python, CUDA and a PyTorch one if there bug in one of them.
Yet at the end of the day, it still uses Nvidia's closed source CUDA compiler 'nvcc' which they will never open source. A least Mojo promises to open source their own compiler which compiles to different accelerators with multiple backend support.
> it still doesn't really move the needle if you really don't like running closed source drivers and runtime binaries
Those people probably did not buy an Nvidia GPU for themselves. It should be common knowledge that the "Open" Nvidia drivers still run gigantic firmware blobs to dispatch complex workloads. And Nouveau is close to useless for GPGPU compute.
The claim is that people are running CUDA on Windows for business analytics and data science? This feels less likely an accurate picture and more likely any mass data processing is already happening on Linux K8s clusters.
The K8s clusters do exist, but I've never met anyone in my life that develops their Jupyter notebooks from their business' Kubernetes setup. Most of them don't even use WSL, to my chagrin (and to their detriment).
And they’re running these Jupyter notebooks on CUDA? I’m suspicious there’s a problem size where the complexity of GPU processing is warranted but it’s small enough to be workable with a local Windows machine.
AFAIK neither excel nor Tableau has any CUDA functionality to begin with so I’m not sure what point you’re trying to make. No one is doing CUDA number crunching on local laptops - either the problem is big enough to warrant a proper data center or it’s small enough that a local CPU is fine. Local CUDA is a weird middle ground that requires a lot of complexity for marginal compute capability.
Linux support for CUDA in such an environment is irrelevant.
Considering I’ve worked for the world’s biggest corporations, medium sized corporations, and startups, I don’t think it’s lost on me. All I said is that the market for single user machine CUDA is marginal which is precisely where Windows dominates, thus even if Mojo doesn’t have windows support to start, it’s not competitively where they need to be.
You’ve been rude and dismissive and raised completely unrelated software, but you haven’t actually figured out a way to attack that claim that single user GPU compute isn’t actually important.
AMD seems to be doing quite well and NVidia is also getting all its revenue and and market cap from cloud GPUs not single user desktops and laptops so I really don’t understand your point - it doesn’t seem to be grounded in the tactical and strategic decisions anyone is making. That’s precisely why Nvidia continue to cripple their consumer parts to avoid them being used by corporations who don’t want to pay the markup for server parts. But even those corporations who would buy NVidia GPUs in bulk would still be doing compute with Linux clusters, not Windows.
Small correction: cuda-oxide does not use `nvcc` to compile Rust kernels.
The device path is roughly: rustc frontend/MIR -> cuda-oxide’s Pliron-based IR/lowering pipeline -> LLVM IR -> PTX via LLVM’s NVPTX backend. Host code still goes through normal rustc/LLVM codegen.
You still need the NVIDIA driver/toolkit pieces to run CUDA code and load PTX on NVIDIA GPUs, so this does not change the broader CUDA ecosystem/dependency story. But it is not “Rust source handed to nvcc”; it is a Rust compiler backend generating device code.
Why do we bother with programming languages today? Why not have the LLMs just write assembly code and skip the human readable part? We are not reviewing it anymore anyway.
1) Higher level code is easier for LLMs to review and iterate upon. The more the intent is clear from the code, the easier it is for humans and LLMs to work with.
2) LLMs get stuck or fail to solve a problem sometimes. It is preferable to have artifacts that humans can grok without the massive extra effort of parsing out assembly code.
3) Assembly code varies massively across targets. We want provable, deterministic transformation from the intent (specified in a higher level language) to the target assembly language. LLMs can't reliably output many artifacts for different platforms that behave the same.
4) Hopefully, we are still reviewing the code output by LLMs to some extent.
1.5) Having a compiler in the loop that does things like enforcing type constraints (and in the case if Rust in particular, therefore memory safety guarantees) is really useful both for humans and LLMs.
> 1) Higher level code is easier for LLMs to review and iterate upon. The more the intent is clear from the code, the easier it is for humans and LLMs to work with.
The counter-argument, and one that matches my experience is working at a lower level is actually beneficial for LLMs since they can see the whole picture and don’t have to guess at abstractions.
Feel free to post a project of yours where you gave a bunch of prompts to an LLM and it produced a working application written in assembly without you having to check for anything
Programming languages are tools for thinking. It's not clear that assembly code has the right abstractions to encourage the kind of thinking that programming large systems requires. After all, human intelligence found assembly insufficient and went on to invent better languages for thinking, why should artificial intelligence, trained on human intelligence, be any different? Maybe AI in the future will have its own languages for thinking, but assembly is likely not that.
I get what you mean but I think if anything AI pairs extremely well with strongly typed languages that are at times cumbersome for humans, but decrease the latency at which AI can get feedback on its code. In my (very) limited experience Rust is an excellent target for AI codegen.
This is a Rust to CUDA converter so I guess it is for codes where the programmer wants it to function properly (Rust) and have good performance (CUDA).
It’s just a matter of different workflows for different users and application.
I mean, AI is not good at writing x86-64 assembly code. Last time I tried (with both Claude and ChatGPT), the AI failed to even create basic programs other than Hello World.
This is amazing.. ive been working with custom CUDA kernels and https://crates.io/crates/cudarc for a long time, and this honestly looks like it could be a near drop-in replacement.
im especially curious how build times would compare? Most Rust CUDA crates obv rely on calling CMake or nvcc, which can make compilation painfully slow. coincidentally, just last week i was profiling build times and found that tools like sccache can dramatically reduce rebuild times by caching artifacts - but you still end up paying for expensive custom nvcc invocations (e.g. candle by hugging face calls custom nvcc command in their kernel compilation): https://arpadvoros.com/posts/2026/05/05/speeding-up-rust-whi...
Cudarc slaps!
> Most Rust CUDA crates obv rely on calling CMake or nvcc, which can make compilation painfully slow.
I anecdotally haven't hit this; see the `cuda_setup` crate I made to handle the build scripts; it is a simple `build.rs` which only recompiles if the file changes, and it's a tiny compile time (compared to the rust CPU-side code)
i'll have to check this out, thanks!
Do other people agree cuda-oxide looks like a near dorp in replacement for cudarc?
That would be amazing, but probably not imo complementarily so.
I am curious what distinguished cuda-oxide. Beyond it being totally under nv control.
perhaps not drop-in, but all my workflows with cudarc have always been "i make cuda kernel, i use cudarc for ffi to said kernels, i call via rust" - which for this case is pretty analogous
briefly looking at the repo, looks like the main workflow is using rustc-codegen-cuda to convert rust -> MIR -> pliron IR -> LLVM IR -> PTX, which is embedded in the host binary, where then cuda-core loads embedded PTX at runtime onto the GPU
but, if you arent directly making cuda kernels and just want cudarc for either calling existing kernels or other cuda driver api access then cudarc is lighter-weight option? or just use one of the sub-crates in this repo like cuda-core for those apis
Hi, author of cuda-oxide here. Yes, I think that’s basically the right framing: cudarc and cuda-oxide sit at different points in the stack.
cudarc is a host-side CUDA API for Rust: loading modules, managing contexts/streams/events/memory, launching kernels, and accessing CUDA libraries/driver APIs. If your workflow is “I already have CUDA C++/PTX/CUBIN kernels and want to call them from Rust”, cudarc is a very natural fit.
cuda-oxide is focused on the other side of the problem: writing the GPU kernel itself in Rust and compiling it through rustc/MIR into GPU code. The generated PTX is then embedded in the host binary and loaded at runtime by our host-side pieces.
We include cuda-core/cuda-host because we need an end-to-end path for “write Rust kernel, build it, launch it”, but that doesn’t mean the generated PTX is tied forever to our launcher. We’d like the PTX from cuda-oxide to be usable from other host-side CUDA APIs too, including cudarc, and we’re exploring ways to make that interop smoother.
So the short version is: cudarc is about driving CUDA from Rust; cuda-oxide is about generating CUDA device code from Rust. They’re complementary rather than replacements for each other.
We also have a short ecosystem note in the book that talks about cudarc: https://nvlabs.github.io/cuda-oxide/appendix/ecosystem.html#...
I am observing the same from the article... is it heavily inspired by Cudarc, i.e. is this intentional, or are we reading too much into this, given Cudarc is a light abstraction over the CUDA api?
I'm quite interested in how they dealt with Rust's memory model, which might not neatly map to CUDA's semantics. Curious what the differences are compared to CUDA C++, and if the Rust's type system can actually bring more safety to CUDA (I do think writing GPU kernels is inherently unsafe, it's just too hard to create a safe language because of how the hardware works, and because of the fact that you're hyper-optimizing all the time)
the main 4 i see are:
1. use-after-free, drop semantics vs manual cudaFree
2. kernel args enforced using `cuda_launch!` whereas CPP void* args is just an array of pointers, validating count only
3. alias mutable writes. e.g. CPP can have more than one thread writing out[i] with same i and this will compile. but DisjointSlice<T> with ThreadIndex doesnt have any public constructor (see: https://github.com/NVlabs/cuda-oxide/blob/2a03dfd9d5f3ecba52...) and only using API of `index_1d` `index_2d` and `index_2d_runtime`
4. im pretty sure you can cuda memcpy a std::string and literally any other POD and "corrupt" its state making it unusable. here it ONLY accepts DisjointSlice<T>, scalars, and closures (https://nvlabs.github.io/cuda-oxide/gpu-programming/memory-a...)
but most of the nitty gritty is in these sections
* https://nvlabs.github.io/cuda-oxide/gpu-safety/the-safety-mo...
* https://nvlabs.github.io/cuda-oxide/gpu-programming/memory-a...
edit: that being said, not like this catch everything, just looks to give much more guardrails against UB with raw .cu files
This is explained in some detail in the docs. There is a safe layer, a mostly safe layer, and an unsafe layer. Some clunkiness is needed for safe-yet-parallel work that they couldn’t easily fit into the Rust Send/Sync model.
FWIW, Rust’s memory model is more or less completely identical to C++’s, by design. Atomics work the same, there’s provenance, and so on.
Whether it is a convenient language for GPU programming probably remains to be seen, but I definitely wouldn’t be surprised if you could make a decent DSL-like API for writing safe code that leverages the full spectrum of GPU oddities. That’s what CUDA is, right?
Originally CUDA hardware was designed without a specific memory model, after C++11, NVidia went into a multi year effort to redesign the hardware to match C++ memory model semantics.
CppCon 2017: "Designing (New) C++ Hardware”
https://www.youtube.com/watch?v=86seb-iZCnI
Thanks, that’s interesting. It feels like a sensible choice.
I think it depends on the objective. My pattern-matching brain says there will be interest in addressing this.
From my perspective of someone who writes applications in Rust and sometimes wants to use GPU compute in these applications: I don't care. If we can leverage the memory model or ownership model in a low-friction way, that's fine. If it makes it a high friction experience, I would prefer not to do it that way.
The baseline is IMO how Cudarc currently does it. I don't think there is much memory management involved; it's just imperative syntax wrapping FFI, and some lines in the build script to invoke nvcc if the kernels change.
I wonder what it means for Slang[0]. Presumably the point is that people want to do GPU programming with a more modern language. But now you can just use Rust...
(Disclaimer: I like Slang a lot.)
[0]: https://shader-slang.org/
They serve different public, Slag folks are more interested in graphics programming not AI algorithms.
Also shading languages are more user friendly given their features.
Finally NVida already has Slang in production and those folks aren't going to rewrite shader pipelines into Rust.
I am working on a graphics library that integrates slang into rust: https://github.com/koubaa/goldy
There's library code in rust that manages GPU memory and schedules pipelines and use a slang reflection to ensure memory layouts between rust and shaders match.
Oh and it supports metal/vulkan/dx12
Writing shaders is materially different from writing CUDA kernels, at least for now. Shaders are simultaneously higher and lower level, and have a lot of idiosyncrasies as a result of being designed for a specific and limited set of driver/GPU features.
Stuff like descriptor sets, resource registers, dispatch limitations, …
Re: Rust (and "safe" programming languages).
Does anyone have more details on NVIDIAs use of Spark/Ada?
All I can find is what's listed below:
https://www.adacore.com/case-studies/nvidia-adoption-of-spar...
They gave a detailed talk last DEF CON: https://www.youtube.com/watch?v=KhWtkZmOPn4
Here's a recording of a 2020 presentation ("Securing the Future of Safety and Security of Embedded Software") from NVIDIA at the AdaCore conference:
https://www.youtube.com/watch?v=2YoPoNx3L5E
> directly to PTX
Weird. There's a recent NVIDIA MLIR that is quite good and fast. Or they could target the even easier and more recent/fashionable tile IR [1] used by CuTile [2] (a little bit higher level but significantly easier to target, only loses on epilogue fusion and similar).
[1] https://docs.nvidia.com/cuda/tile-ir/
[2] https://developer.nvidia.com/cuda/tile
> (em dash) no DSLs, no foreign language bindings, just Rust.
Official CUDA port and they couldn't even bother with the introductory paragraph.
Okay, I'll try to ignore it and read the docs. Hey a custom IR, this sounds interesti-
> MLIR’s implementation, however, is C++ with a side of TableGen, a build system that requires you to compile all of LLVM, and debugging sessions that make you question your career choices.
I can't take this industry seriously anymore.
if they didnt use AI for their webpage people would say "why doesnt NVIDIA write its website and documentation with AI? don't they believe their own story about AI factories and employees managing thousands of agents doing the work for them?"
this is exactly on brand dog-fooding I would expect from an AI hyper
Literally no one would ever say that simply for editing the LLMisms away.
why would you edit them away? they are a signal that you are an "AI first" company
I think the whole codebase was more or less written by AI...
that ship has long sailed, "it no longer matters" saying a codebase, an article was written with AI doesn't mean much, it could be good, it could be bad. folks often say it to generate outrage, but that means nothing. is the codebase great, good, bad, terrible? that's the only thing that matters.
Even as someone who uses a lot of AI, if you can't be bothered to at least give it a prompt like "Go through the documentation and comments in detail and remove any obvious AI shibboleths like emdashes, it's not x it's y, rule-of-three, 'delve', excessive grandiosity and flourishes, boldness, bullet points, etc", you should receive a brisk kick in the rear.
I'd be curious to know if there is a list of these "AI shibboleths" somewhere
Wikipedia maintains a list of smells for LLM text.
https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
Not really but they jump right out at you after a few minutes chatting with it. I also asked the AI and it was pretty subjectively accurate, especially if you force it to cross reference with web searches and especially google's ngram corpus (you can readily see that 'delve' and some of the other rhetorical constructs are quite uncommon in human speech)
Might be the only thing that matters to you. And, perhaps, the only thing that matters in a functional sense. But, whether it’s human-coded/written or not matters deeply to some.
No, it's always bad. It's just on a spectrum of how competent is the viewer (you and me) to notice how bad it is. It's all shit, the question is whether you are skilled and experienced enough to tell.
And an LLM-written codebase is strongly correlated with a terrible codebase. So much so, that it's rarely worth your time to seriously evaluate it.
They also named it CUDA-oxide, flaunting their ignorance of what Rust lang is named after (fungi, not oxidation).
That's a lost battle even in the Rust community: Firefox's oxidation, Ferrous Systems, Redox, OxidOS, OxCaml (OCaml extensions partly inspired by Rust)… and every crate referencing oxidation in its name.
Yes, but have you seen the official logo? :)
What exactly are you upset about? Someone observing that MLIR is extremely complex and dependent on LLVM...?
The quoted writing is AI slop, and OP is reacting to the fact that they did not write even the introductory text themselves (or at least bother to edit out clear AI/slop indicators)
... Who cares...
Clearly I.
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Orange Reddit. Unfortunately that rings a little too true these days. Hopefully a stage that reverts at some point.
TileLang https://github.com/tile-ai/tilelang and stuff like Tile Kernels https://github.com/deepseek-ai/TileKernels will make CUDA obsolete one day.
CUDA is nearly 20 years old, and is not going anywhere, for many years to come
this dude is a distinguished engineer at siemens commenting the dopiest/reddit level takes. lolol.
agree not related to the rust to cuda compiler, you are right! But I have to say worth to look at upcoming new stuff, as this is kind a wow rust on good old CUDA.
That's quite a claim for very little evidence.
is this even comparable? lol
Does anyone know if this will let you share structs between host and device? That is the big thing missing so far with existing rust/CUDA workflows. (Plus the serialization/bytes barrier between them)
Yes, absolutely. That is one of the advantages of cuda-oxide being single-source Rust: the host and device code can refer to the same Rust types, and the compiler has enough information to make the device-side layout match what rustc chose on the host.
So the intended workflow is not “define a Rust struct on the host, define a matching CUDA C++ struct for the device, then serialize bytes between them.” It is much closer to “define `MyStruct` once in Rust, put a `DeviceBuffer<MyStruct>` on the GPU, and write kernels that take `&[MyStruct]`, `*const MyStruct`, etc.”
There are two important pieces under the hood:
1. At the kernel boundary, cuda-oxide scalarizes aggregate parameters where needed. For example, slices become pointer + length, and simple structs can be flattened into fields for launch ABI purposes.
2. For actual struct layout, we use rustc’s computed layout rather than assuming declaration order or a C ABI. That matters because Rust is allowed to reorder/pad `repr(Rust)` structs. The device lowering carries those offsets/padding through so field access on the GPU matches the host-side layout.
So for plain data structs, nested structs, numeric fields, arrays, etc., yes, this is very much the goal: share the type directly instead of maintaining a separate CUDA representation or crossing a bytes/serialization boundary.
The caveat is the usual one: this does not make arbitrary host-owned Rust heap graphs GPU-addressable. A `Vec`, `String`, `Box`, trait object, or host pointer still contains an address, and that address has to refer to memory the GPU can actually access. For those cases you still need device allocation, unified/HMM memory, or a GPU-friendly representation.
But for the common “I have a Rust data type and want kernels to consume/update arrays of it” case: yes, that is exactly the kind of friction cuda-oxide is meant to remove.
Here's the repo link https://github.com/NVlabs/cuda-oxide
One thing I’ve been wary about with Rust for CUDA is the bit of overhead that Rust adds that is usually negligible but might matter here, like bounds checks on arrays. Could it cause additional registers to get used, lowering the concurrency of a kernel?
Oh lord. If this is the trend, I probably can't avoid improving my Rust language knowledge in the long term. I hate reading Rust so much right now. I guess I just have to get over that hump.
Learning Rust is more alike to learning a new programming paradigm (e.g. functional when you only know imperative) than a new language with different syntax only. If you ignore that and try to jump directly to writing code more or less the same way as you used to, it will be painful. So take it slow and follow along with The Book (https://doc.rust-lang.org/book/). It all makes sense eventually and is very much worth it!
Fully agree
Hell yea! I have been doing it with Cudarc (Kernels) and FFI (cuFFT). Using manual [de]serialization between byte arrays and rust data structs. I hope this makes it lower friction!
https://nvlabs.github.io/cuda-oxide/gpu-safety/the-safety-mo...
> A GPU kernel runs thousands of threads that all see the same memory at the same time. On a CPU, Rust prevents data races through ownership and borrowing – one mutable reference, no aliases, enforced at compile time. On a GPU, you have 2048 threads per SM, all launched from the same function, all pointing at the same output buffer. The borrow checker was not designed for this.
> cuda-oxide solves the problem in layers. The common case – one thread writes one element – is safe by construction, no unsafe required. The uncommon cases – shared memory, warp shuffles, hardware intrinsics – require unsafe with documented contracts. And the frontier cases – TMA, tensor cores, cluster-level communication – are fully manual, matching the complexity of the hardware they control.
That's.. not really Rusty. In Rust, we create new safe abstractions when the existing ones don't quite map to the problem at hand. See for example what's done in Rust for Linux
If it's not safe.. what's the point of Rust?
(it's okay to offer unsafe APIs for people that need to squeeze the last bit of performance, but this shouldn't be the baseline)
I compare this with userspace libs for APIs like io_uring and vulkan. designing safe APIs for them stuff is kind of hard (there's even some unsound attempts)
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Personally I really don't want new GPU languages that do not have AD as a first class citizen. I mean rust is an improvement over C++ CUDA but still.
There is actually work on adding autodiff to Rust, maybe not really first class citizen, but at least build in: https://doc.rust-lang.org/std/autodiff/index.html (it is still at a pre-RFC stage so it is not something that soon will be added)
Incredible, I have never heard of std::autodiff before. Isn't it rare for a programming language to provide AD within the standard library? Even Julia doesn't have it built-in, I wouldn't expect Rust out of all languages to experiment it in std.
It 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.
You can read some motivation for it at the following link
https://rust-lang.github.io/rust-project-goals/2024h2/Rust-f...
note that it also discusses `std::offload`, which might also be of interest.
That's awesome, I didn't know that!
Sorry, what is AD in this context?
edit: oh, automatic differentiation?
Active Directory
This isn't a new GPU language; it's a lib which might replace FFI and third party libs.
This is definitely not just a lib. This compiles rust to CUDA. If you call a full on compiler stack a lib, everything may as well be a lib.
Ok. I am calling it a lib because to use, you add it as a dependency in cargo.toml then import it in your rust modules.
That's after you have installed their entire build infra + dependencies. They ship their own cargo subcommand.
Really hard to find alternatives to Julia for AD as a first class citizen
I think the parent is mostly referring to solutions like Slang.D
So, https://shader-slang.org/ then :)
every GPU related post has a comment which makes my eyes roll all the way back. this is the one for this post.
So, we have stainless, which means Linux code that never rusted. Now we need someone to make phosphorus so that we can turn rusty code into old iron. Then GPL fans can run Rust boxes, Stainless machines, or future proofed iron work horses.
All software can come on three editions. Stainless drivers that were never rusty, oxidized drivers that used Rust on existing code, and Iron editions which is where someone converted the Rust back to C using the new phosphoric tool...
Diversity can be our strength.
Making Iron C/c++ code can be called acid washing if it was rusted.
> we need someone
> Then GPL fans can
Checks out
This is a bit good for Rust if you want to use the language with CUDA. The problem is, it still doesn't really move the needle if you really don't like running closed source drivers and runtime binaries and care about open source.
Continuing from this discussion [0], this only makes it a Rust or a CUDA problem rather than a Python, CUDA and a PyTorch one if there bug in one of them.
Yet at the end of the day, it still uses Nvidia's closed source CUDA compiler 'nvcc' which they will never open source. A least Mojo promises to open source their own compiler which compiles to different accelerators with multiple backend support.
Unlike this...but uses Rust.
[0] https://news.ycombinator.com/item?id=48067228
IMO this has nothing to do with open source as an ideology; just a practical (and official?) lib for adding GPU interaction to your rust programs.
> it still doesn't really move the needle if you really don't like running closed source drivers and runtime binaries
Those people probably did not buy an Nvidia GPU for themselves. It should be common knowledge that the "Open" Nvidia drivers still run gigantic firmware blobs to dispatch complex workloads. And Nouveau is close to useless for GPGPU compute.
Considering how fast everything is changing with GPUs and how competitive it is. It doesn't make sense to have an open source driver.
Mojo remains to be seen if it isn't another Swift for Tensorflow, apparently 1.0 won't even support Windows properly.
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All the game devs that forced Valve to come up Proton for Steam Deck to have any content.
The majority of computer owners on planet Earth
But also the majority of programmers?
Yes, because Windows software doesn't sprung into existence out of nowhere.
Neither did Linux software.
It actually did, plenty of it are UNIX ports that predate the Linux kernel, looking for a cheaper home.
Note how Linux only has a relevant market share on deployment scenarios that were previously UNIX/POSIX turf.
In AI-focused fields like business analytics and data science, yeah.
The claim is that people are running CUDA on Windows for business analytics and data science? This feels less likely an accurate picture and more likely any mass data processing is already happening on Linux K8s clusters.
The K8s clusters do exist, but I've never met anyone in my life that develops their Jupyter notebooks from their business' Kubernetes setup. Most of them don't even use WSL, to my chagrin (and to their detriment).
And they’re running these Jupyter notebooks on CUDA? I’m suspicious there’s a problem size where the complexity of GPU processing is warranted but it’s small enough to be workable with a local Windows machine.
Yes, if they happen to run tooling like Excel, PowerBI, Tableau,....
Also Linux support for CUDA on laptops, especially with dual GPU setup isn't particularly great.
Most workstation class laptops are Windows based.
AFAIK neither excel nor Tableau has any CUDA functionality to begin with so I’m not sure what point you’re trying to make. No one is doing CUDA number crunching on local laptops - either the problem is big enough to warrant a proper data center or it’s small enough that a local CPU is fine. Local CUDA is a weird middle ground that requires a lot of complexity for marginal compute capability.
Linux support for CUDA in such an environment is irrelevant.
Because you are lost how a day in the office of a big corporation looks like.
Also this attitude about people not doing compute on laptops, is yet another reason why Intel and AMD keep losing to NVidia.
Considering I’ve worked for the world’s biggest corporations, medium sized corporations, and startups, I don’t think it’s lost on me. All I said is that the market for single user machine CUDA is marginal which is precisely where Windows dominates, thus even if Mojo doesn’t have windows support to start, it’s not competitively where they need to be.
You’ve been rude and dismissive and raised completely unrelated software, but you haven’t actually figured out a way to attack that claim that single user GPU compute isn’t actually important.
AMD seems to be doing quite well and NVidia is also getting all its revenue and and market cap from cloud GPUs not single user desktops and laptops so I really don’t understand your point - it doesn’t seem to be grounded in the tactical and strategic decisions anyone is making. That’s precisely why Nvidia continue to cripple their consumer parts to avoid them being used by corporations who don’t want to pay the markup for server parts. But even those corporations who would buy NVidia GPUs in bulk would still be doing compute with Linux clusters, not Windows.
many people
you mom!
My sentiment matches your exactly. I'm sick and tired of CUDA - but it's really not going to change.
Could maybe be forked with some dynamic smarts, HIP is basically 1:1 with CUDA: https://github.com/amd/amd-lab-notes/blob/release/hipify%2Fs...
Does it support a graphical GPU debugging for C++, Fortran and Python JIT GPU code?
Otherwise it isn't 1:1 with CUDA, and I am not counting everything else on CUDA ecosystem
Debugging (gdb, but GUI frontends exist for gdb in general): https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/debu...
Fortran: https://github.com/ROCm/hipfort
Python (not sure about JIT): https://rocm.docs.amd.com/projects/hip-python/en/latest/
All those are far from the 1:1 CUDA experience.
Small correction: cuda-oxide does not use `nvcc` to compile Rust kernels.
The device path is roughly: rustc frontend/MIR -> cuda-oxide’s Pliron-based IR/lowering pipeline -> LLVM IR -> PTX via LLVM’s NVPTX backend. Host code still goes through normal rustc/LLVM codegen.
You still need the NVIDIA driver/toolkit pieces to run CUDA code and load PTX on NVIDIA GPUs, so this does not change the broader CUDA ecosystem/dependency story. But it is not “Rust source handed to nvcc”; it is a Rust compiler backend generating device code.
AWESOME!
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This is solved by Mojo already, they must be rushing something to compete, since Mojo is in version 1.0beta1
Why do we bother with programming languages today? Why not have the LLMs just write assembly code and skip the human readable part? We are not reviewing it anymore anyway.
A lot of really good reasons:
1) Higher level code is easier for LLMs to review and iterate upon. The more the intent is clear from the code, the easier it is for humans and LLMs to work with.
2) LLMs get stuck or fail to solve a problem sometimes. It is preferable to have artifacts that humans can grok without the massive extra effort of parsing out assembly code.
3) Assembly code varies massively across targets. We want provable, deterministic transformation from the intent (specified in a higher level language) to the target assembly language. LLMs can't reliably output many artifacts for different platforms that behave the same.
4) Hopefully, we are still reviewing the code output by LLMs to some extent.
I'd add to that
1.5) Having a compiler in the loop that does things like enforcing type constraints (and in the case if Rust in particular, therefore memory safety guarantees) is really useful both for humans and LLMs.
In addition LLMs also make bugs, and debugging assembler is more difficult, wasting more tokens, thus more money.
A very big practical reason is also that assembler code would eat context like no other.
> 1) Higher level code is easier for LLMs to review and iterate upon. The more the intent is clear from the code, the easier it is for humans and LLMs to work with.
The counter-argument, and one that matches my experience is working at a lower level is actually beneficial for LLMs since they can see the whole picture and don’t have to guess at abstractions.
Feel free to post a project of yours where you gave a bunch of prompts to an LLM and it produced a working application written in assembly without you having to check for anything
Is this a serious question or are you just trolling?
I'll bite:
Programming languages are tools for thinking. It's not clear that assembly code has the right abstractions to encourage the kind of thinking that programming large systems requires. After all, human intelligence found assembly insufficient and went on to invent better languages for thinking, why should artificial intelligence, trained on human intelligence, be any different? Maybe AI in the future will have its own languages for thinking, but assembly is likely not that.
I get what you mean but I think if anything AI pairs extremely well with strongly typed languages that are at times cumbersome for humans, but decrease the latency at which AI can get feedback on its code. In my (very) limited experience Rust is an excellent target for AI codegen.
(I meant statically typed / high level of type safety here not strongly typed)
This is a Rust to CUDA converter so I guess it is for codes where the programmer wants it to function properly (Rust) and have good performance (CUDA).
It’s just a matter of different workflows for different users and application.
Because when this idiotic hypemachinery finally dies an agonising, painful death, some of us still want to work with computers
I mean, AI is not good at writing x86-64 assembly code. Last time I tried (with both Claude and ChatGPT), the AI failed to even create basic programs other than Hello World.