VC4CL
ZLUDA
VC4CL | ZLUDA | |
---|---|---|
6 | 35 | |
721 | 7,671 | |
- | - | |
0.0 | 7.0 | |
over 1 year ago | 4 days ago | |
C++ | Rust | |
MIT License | 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.
VC4CL
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How to OpenCL on a raspberry Pi
There is a rough impl for the 3. https://github.com/doe300/VC4CL
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Raspberry Pi Zero 2 for image processing.
The Zero 2 still uses VideoCore IV for which we have the documentation and various projects making use of it. There is a open source Mesa driver and also OpenCL support: https://github.com/doe300/VC4CL
- 2022 Mar 14 (Pi Day!) Stickied π ΅π °π & ππππππππ thread - Boot problems? Power supply problems? Display problems? Networking problems? Need ideas? Get help with these and other questions! π¨πΊπ² π―π¬πΉπ¬ ππ°πΉπΊπ»
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How do I get OpenCL on a Raspberry Pi 1B?
Can't help more than a random repo I found showing its possible https://github.com/doe300/VC4CL
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Zluda: CUDA on Intel GPUs
Excellent effort. Nvidia has become defacto GPGPU hardware vendor due to CUDA, but I wish it was OpenCL or other general API instead. Even Raspberry Pi's VideoCore has OpenCL support[1].
But a look at HW Acceleration support table at FFmpeg[2] shows why GPGPU Platform API is such a mess. But performance benefits are incredible, using VAAPI for FFmpeg to encode 1080p 2560x1080 screen capture at 60fps reduces CPU usage from 90% to 10% on a old corei5 with intel HD 3000; An old laptop could be perfectly used as an encoding machine for streaming just by using HW Acceleration.
What's funny is that the laptop also has Radeon HD 6490M with 1GB GDDR5 dedicated memory and it's not supported by VAAPI for encoding! Thereby proving the point that GPGPU API/Platform Support are astonishingly messy.
[1]https://github.com/doe300/VC4CL
[2]https://trac.ffmpeg.org/wiki/HWAccelIntro
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?
ROCm - AMD ROCmβ’ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
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