Nvidia Tilus: A Tile-Level GPU Kernel Programming Language

https://github.com/NVIDIA/tilus

by ashvardanian • 11 months ago

94 48 11 months ago

github.com

moralestapia• 11 months ago

New thing to experiment with.

Great :).

coderatlarge• 11 months ago

but why use “~float16” for something that in its own documentation the authors describe as a float16* ?

FilosofumRex• 11 months ago

seriously!!! there is so much more Nvidia and its billionaire managers can do to improve developers experience with CUDA/nvcc/PTX, instead of yet another barely functional, sparsely documented, rarely tested DSL

snarfy• 11 months ago

"...made incremental improvements to CUDA" just doesn't get you promoted like "creator of GPU Kernel Programming language Tilus"

skavi• 11 months ago

What’s the relationship between Tilus and cuTile (and maybe Warp)?

A slight tangent, but I really wish Nvidia would release more details on Tile IR. Specifically on what it enables vs PTX.

Is it just about moving towards more MLIR based infra? Maybe it’s higher level and thus can enable better codegen across generations?

dachworker• 11 months ago

My impression of NVIDIA is that internally the teams are quite independent and there's not much in terms of broad strategy. So it could be these two efforts are not related.

dagmx• 11 months ago

This is accurate. A lot of projects that come out are each teams research projects unless they’re tied to a specific product which is when they start working closer together and integrating further.

However this repo was specifically part of their acquisition of CentML

alwahi• 11 months ago

okay, I am not a systems level programmer but I am currently learning c with the aim of doing some gpgpu programming using Cuda etc., what is a tile level gpu kernel programming language? and how is it different from something like cuda?

I know i can ask a llm or search on google, but i was hoping someone in the community could explain it in a way i could understand.

taminka• 11 months ago

programming w/ tiles means working w/ contiguous-ish blocks/squares of data, to minimise cache misses (often major bottleneck in gpu programming), so it's just a nod to the fact that optimisations like these are included in the language or some such...

atq2119• 11 months ago

I'd say the main difference is that in traditional GPU languages, the thread of execution is a single lane of a warp or wave. You typically work with ~fp32-sized values, and those are mapped by the compiler to one lane of a 32-wide vector register in a wave (or 16- to 128-wide depending on the architecture). Control flow often has to be implemented through implicit masking as different threads mapped to lanes of the same vector can make different control flow decisions (that is, an if statement in the source program gets compiled to an instruction sequence that uses masking in some way - the details vary by vendor).

In tile languages, the thread of execution is an entire workgroup (or block in CUDA-speak). You typically work with large vector/matrix-sized values. The compiler decides how to distribute those values onto vector registers across waves of the workgroup. (Example: if your program has a value that is a 32x32 matrix of fp32 elements and a workgroup has 8 32-wide waves, the value will be implemented as 4 standard-sized vector registers in each wave of the workgroup.) All control flow affects the entire workgroup equally since the ToE is the entire workgroup, and so the compiler does not have to do implicit masking. Instead, tile languages usually have provisions for explicit masking using boolean vectors/matrices.

Tile languages are a new phenomenon and clearly disagree on what the exact level of abstraction should be. For example, Triton mostly hides the details of shared memory from the programmer and lets the compiler take care of software pipelined loads, while in this Tilus here, it looks like the programmer has to program shared memory use explicitly.

torginus2• 11 months ago

It's like writing code directly for the GPU's DSP-like SIMD cores in assembly, instead of taking the CUDA model of targeting a single SIMD thread, from which the compiler figures out how to write assembly for the core itself.

socalgal22• 11 months ago

Maybe this is my dad speaking through me when he got tired of answering questions and said "look it up" and pointed to our bookshelf but....

Copying and pasting your exact words above into an LLM (gemini/chatgpt) provided an answer arguably better than any of the human answers at the time of this post.

cultofmetatron• 11 months ago

while thats true, it sets a dangerous precedent. llms get their answers from being trained on a corpus of human knowledge created by humans. what is going to happen when we all collectively tell each other to copy paste into chatgpt and no one is actually answering any questions?

Archit3ch• 11 months ago

I'm hoping that something like MoYe.jl moves from Nvidia-only to a vendor-agnostic tile DSL.

lukax• 11 months ago

NVIDIA does not want CUDA development (e.g. flash attention) to move to Triton because Triton also supports AMD and if ecosystem moves from pure CUDA to Triton, that's bad for NVIDIA's lock-in. That's why there is so much focus on CUDA Python (lower level) and Tilus (higher level, more similar to Triton).

archerx• 11 months ago

What’s bad for Nvidia is good for everyone else. The cuda lock-in needs to die.

pjmlp6• 11 months ago

It is on AMD and Intel to deliver.

dismalaf• 11 months ago

What's crazy to me is that ROCm and SYCL are open-source, but somehow more difficult to install and support less hardware for their respective brands than CUDA does for Nvidia...

torginus2• 11 months ago

After looking up Triton this seems quite different - Triton is a high-level CUDA competitor with Python-like syntax, and this seems to be a library aimed at generating GPU assembly for micro-optimizing kernels.

YetAnotherNick• 11 months ago

Looks pretty similar to me[1]

[1] https://nvidia.github.io/tilus/getting-started/tutorials/mat...

mdaniel2• 11 months ago

My experience searching for "nvidia triton" coughed up oppressive number of results for the similarly named inference server but I think the Triton discussed here is https://github.com/triton-lang/triton (although the commits that I spot checked were all from @openai.com emails, not nvidia)

ddtaylor• 11 months ago

Nvidia lock in attempt.

Keyframe• 11 months ago

So, SIMD but in python and on GPU?

camdroidw• 11 months ago

I'm a little out of the loop these days but I thought webgpu obviated the use of platform specific language (say Cuda)?

lights0123• 11 months ago

There are a lot of hardware features inaccessible from webgpu because its devs still have a lot of work to do for the current implementations—no browser is even shipping it on Linux by default.

Vulkan is closer, but CUDA still exposes more features.

pjmlp6• 11 months ago

Not quite right, Chrome is shipping with support on ChromeOS, Android and WebOS.

It is GNU/Linux that Google/Chrome sees as low priority.

bobajeff• 11 months ago

WebGPU is a API that sits above Vulkan, DirectX and Metal. For CUDA, ROCm and oneAPI there is SYCL. But it's not used too much and it seems mostly just Intel is invested.

pjmlp6• 11 months ago

Additionally, designed for 2015 hardware capabilities, with JavaScript in mind, in a sandboxed environment.

Some folks are reaching out to WebGPU outside the browser, because Vulkan is a pain to program for, and they misuse WebGPU as middleware, although a less capable one, due to its original design, and who is driving its standardisation process.

Regarding SYCL, Intel basically bought the only company that was shipping a good developer experience for it, CodePlay, which used to do specialized compilers for game consoles, and pivoted into GPGPU.

m-schuetz• 11 months ago

WebGPU is the lowest common denominator over ancient mobile phones and modern hardware; as well as Vulkan, DirectX and Metal; effectively making it an ancient-mobile-phone-graphics-API. It is at the time of its releas as outdated as WebGL was when it came out.

pjmlp6• 11 months ago

Additionally Chrome sabotaged the WebGL 2.0 compute effort in name of WebGPU, which 5 years later still isn't fully available.

However despite all of this, there is hardly any Web 3D experience, or game, at the level of iPhone games from its OpenGL ES 3.0 glory days, like Infinity Blade, from 2011!

pjmlp6• 11 months ago

The recent interest on NVidia making Python first class on CUDA ecosystem is what makes me wonder how successful Mojo migth becomem, if they aren't faster than NVidia with Python.

All the attempts to attack CUDA fail to undestand why most researchers flock to it, instead of enduring the pain of the competion tooling, and they tend to focus on a single aspect of CUDA, be it C++, or something else, but never the polyglot support, the libraries, the IDE integration, the graphical debuggers, the compiler backends for other developers to target CUDA.

drdirk• 11 months ago

The linked paper in the GitHub repository doesn’t contain an NVIDIA email. There is an Amazon email and a bunch of university emails.

How come that this paper has become an NVIDIA project?

Paper: https://arxiv.org/pdf/2504.12984

Nvidia Tilus: A Tile-Level GPU Kernel Programming Language

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