kompute
ZLUDA
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kompute | ZLUDA | |
---|---|---|
37 | 35 | |
1,486 | 7,617 | |
6.9% | - | |
8.1 | 6.1 | |
4 days ago | 5 days ago | |
C++ | Rust | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
kompute
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
The two I know of are IREE and Kompute[1]. I'm not sure how much momentum the latter has, I don't see it referenced much. There's also a growing body of work that uses Vulkan indirectly through WebGPU. This is currently lagging in performance due to lack of subgroups and cooperative matrix mult, but I see that gap closing. There I think wonnx[2] has the most momentum, but I am aware of other efforts.
[1]: https://kompute.cc/
[2]: https://github.com/webonnx/wonnx
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[P] - VkFFT version 1.3 released - major design and functionality improvements
Great to see the positive momentum of this framework! Best wishes and upvotes from the Vulkan Kompute team :)
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VkFFT: Vulkan/CUDA/Hip/OpenCL/Level Zero/Metal Fast Fourier Transform Library
To a first approximation, Kompute[1] is that. It doesn't seem to be catching on, I'm seeing more buzz around WebGPU solutions, including wonnx[2] and more hand-rolled approaches, and IREE[3], the latter of which has a Vulkan back-end.
[1]: https://kompute.cc/
[2]: https://github.com/webonnx/wonnx
[3]: https://github.com/openxla/iree
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I'm Having Trouble Building this Library...
I look in an example and see similar instructions, stating that the build should be quite simple. But again, it doesn't work. It generates a bunch of folders with Visual Studio stuff, but no executables, no libraries, or anything like that.
I can't figure out how, and there are no tutorials. According to https://kompute.cc/overview/build-system.html I should simply run "cmake -Bbuild". But this doesn't output what I need, and when I look in the Makefile I get the sense that this is more an example Makefile... but then that contradicts the above tutorial.
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How to properly construct an abstraction layer with Vulkan
Kompute is in my opinion good example to take inspiration for abstractions.
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Vulkan for Image Processing? Good choice?
Currently, there's a few Vulkan compute frameworks floating around (like Kompute). I would work with those. Kompute simplifies a lot of the biolerplate and seems like you could benefit from using it.
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Paralell computing project
Try Kompute, a project from the Linux foundation. It is quite simple to use, and does not require deep knowledge of graphics API. It’s a bit painful to setup, but it kinda works well (and I have a project going on on it)
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Bootstrapping Vulkan for Scientific Compute Applications?
This so much.
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[D] PyTorch is moving to the Linux Foundation
This makes alot of sense considering the Linux Foundation is also in charge of Kompute which is likely to be the basis of vendor agnostic GPGPU, and thus the basis of vendor agnostic GPU-based machine learning.
ZLUDA
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Open-source project ZLUDA lets CUDA apps run on AMD GPUs
It now supports AMD GPUs since 3 weeks ago, check the latest commit at the repo:
https://github.com/vosen/ZLUDA
The article also mentions exactly this fact.
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Nvidia bans using translation layers for CUDA software
Looks like nvidia is trying to keep the lynchpin of their entire business model from crumbling underneath them. ZLUDA lets you run unmodified CUDA applications with near-native performance on AMD GPUs.
https://github.com/vosen/ZLUDA
With Triton looking to eclipse CUDA entirely, im not sure this prohibition does anything more than placate casual shareholders.
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Nvidia bans using translation layers for CUDA software to run on other chips
>Dark API functions are reverse-engineered and implemented by ZLUDA on a case-by-case basis once we observe an application making use of it.
https://github.com/vosen/ZLUDA/blob/master/ARCHITECTURE.md
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Nvidia hits $2T valuation as AI frenzy grips Wall Street
> I know AMD have their competition, but their GPU software division keeps tripping over itself.
They are actively stepping on every rake there is. Eg they just stopped supporting the drop-in-cuda project everyone is waiting for, due to there being "no business-case for CUDA on AMD GPUs" [0].
[0] https://github.com/vosen/ZLUDA?tab=readme-ov-file#faq
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Nvidia Is Now More Valuable Than Amazon and Google
https://github.com/vosen/ZLUDA
They still funded it and it was created.
- Debian on Apple hardware (M1 and later)
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
From the same repo, I found this excellent, well-written architecture document: https://github.com/vosen/ZLUDA/blob/master/ARCHITECTURE.md
I love the direct, "no bullshit" style of writing.
Some gems:
> Anyone familiar with C++ will instantly understand that compiling it is a complicated affair.
> Additionally CUDA allows, to a large degree, mixing CPU code and GPU code. What does all this complexity mean for ZLUDA? Absolutely nothing
> Since an application can dynamically link to either Driver API or Runtime API, it would seem that ZLUDA needs to provide both. In reality very few applications dynamically link to Runtime API. For the vast majority of applications it's sufficient to provide Driver API for dynamic (runtime) linking.
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
CUDA is huge and nvidia spent a ton in a lot of "dead end" use cases optimizing it. There have been experiments with CUDA translation layers with decent performance[1]. There are two things that most projects hit:
1. The CUDA API is huge; I'm sure Intel/AMD will focus on what they need to implement pytorch and ignore every other use case ensuring that CUDA always has the leg up in any new frontier
2. Nvidia actually cares about developer experience. The most prominent example is Geohotz with tinygrad - where AMD examples didn't even work or had glaring compiler bugs. You will find nvidia engineer in github issues for CUDA projects. Intel/AMD hasn't made that level of investment and thats important because GPUs tend to be more fickle than CPUs.
[1] https://github.com/vosen/ZLUDA
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Why Nvidia Keeps Winning: The Rise of an AI Giant
> I don't think you understand just how insanely difficult it is to break into that market.
You're right, I have no clue nor have I ever tried myself.
> Even with apple money or something like that, it's a losing prospect because in the time it'll take you to get up and off the ground (which is FOREVER) your competition will crush you.
This I find hard to believe, do you have a source or reference for that claim? Companies with that amount of cash are hardly going to be crushed by competition be it direct or indirect. Anyway, I'm talking more about the Intels and AMDs of this world.
We have very lacklustre efforts from players I won't name with their Zluda library (https://github.com/vosen/ZLUDA) which I got REALLY excited about, until I read the README.txt. Four contributors, last commit early 2021.
Why, oh why, is it this bad?
- Intel Arc Graphics Driver Change Leads To A Big Speed-Up Under Linux
What are some alternatives?
rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
HIPIFY - HIPIFY: Convert CUDA to Portable C++ Code [Moved to: https://github.com/ROCm/HIPIFY]
VkFFT - Vulkan/CUDA/HIP/OpenCL/Level Zero/Metal Fast Fourier Transform library
OpenCLOn12 - The OpenCL-on-D3D12 mapping layer
HIPIFY - HIPIFY: Convert CUDA to Portable C++ Code
godot-proposals - Godot Improvement Proposals (GIPs)
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability
VulkanExamples - Examples and demos for the Vulkan C++ API
arrow - 🏹 Better dates & times for Python