ZLUDA VS nvidia-patch

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.

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.

>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

> 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

https://github.com/vosen/ZLUDA

They still funded it and it was created.

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.

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

> 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?

This can be patched out. https://github.com/keylase/nvidia-patch

NVIDIA Corporation GA106 [GeForce RTX 3060] and I applied the patch here https://github.com/keylase/nvidia-patch

> CUDA, and pretty much all optimization(hacks) done to run games better

And arbitrary limitations implemented at the driver level to force you to purchase their enterprise GPUs, see https://github.com/keylase/nvidia-patch#nvenc-and-nvfbc-patc...

Nvidia has no motivation to make a consumer card with lots of VRAM, that's basically the only (relevant) separator between the GeForce family and the Quadro lineup.

There are restrictions on NVENC streams with consumer cards, but that has been a solved problem for a while [0].

If they were to make a consumer card with more VRAM, it would immediately undercut their own Quadro/Tesla lineup, which cost substantially more. I don't see a reason for them to do it.

0: https://github.com/keylase/nvidia-patch

I have never had to do the session limit bump thing from the last link. I have a 3090 as well and simply did the initial unlock, which worked fine. I would reinstall fresh drivers from Nvidia, making sure you install the newest one that is supported by the unlock tool (536.40 as of this post, the GitHub for the patch has links to the drivers - https://github.com/keylase/nvidia-patch/tree/master/win)

Aren't these Quadro versions. The patch here. https://github.com/keylase/nvidia-patch supports Quadro versions of you click on the win clickable.

Second, the GPU. The GPU is probably as important as the CPU, and in some cases more important, and when we talk about GPUs we will primarily talk about Nvidia GPUs as they are officialy supported by the Plex team. NVIDIA GPUs are important for Plex hardware transcoding due to their dedicated video encoding/decoding units, superior performance, wide codec support, improved video quality, reduced CPU load, power efficiency. They offer a powerful hardware acceleration solution that can greatly enhance the transcoding capabilities of a Plex server. It's also important to note that Nvidia GPUs require a patch to unlock the number of HW transcoding streams. Dedicated GPUs are large pieces of hardware and have their place in desktop PCs. However, they can also be used with mini-PCs by using an external GPU enclosure.

As there's no NVENC patch available (yet) for the Beta driver branch - referring to this one: https://github.com/keylase/nvidia-patch - which can lift the limits of HW transcoding, I was now wondering a little, as I can see 5 (hw) streams on Plex, which actually shouldn't/cannot be the case no?

I haven’t done this yet but there is a patch on GitHub that removes the limitation for consumer GPUs. Makes lower end cards more attractive for this type of work