ZLUDA VS HIPIFY

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?

Right. I can't speak to its correctness/completeness as I've only done a quick installation and smoke test of the ROCm/HIP/MIOpen stack, but there's even a tool that automates the translation [1].

[1] https://github.com/ROCm-Developer-Tools/HIPIFY

From https://news.ycombinator.com/item?id=32904285 re: AMD Rocm, HIPIFY, :

>> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :

>> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :

>> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

> AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs: https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...

> Thus, the idea is that through typically negligible effort porting to HiP, your code becomes vendor-independent.

Here, the big AMD mistake was to rename those function prefixes in the first place. It's a mistake that they could have avoided...

What a lot of SW codebases did to support AMD (see PyTorch code notably): codebase is still CUDA, have the conversion pass to HIP done at build time.

See https://github.com/ROCm-Developer-Tools/HIPIFY/blob/amd-stag... for the Perl script to do it.

Then comes the problem of AMD not supporting ROCm HIP on most of their hardware or user base.

On Windows, the ROCm HIP SDK is private and only available under NDA. This means that while you can use Blender w/ HIP on Windows, the Blender builds that you compile yourself will not be able to use ROCm HIP.

On Linux, the supported GPUs are few and far between, Vega20 onwards are supported today. APUs, RDNA1, and lower end RDNA2 w/o unsupported hacks (6700 XT and below) are excluded.

https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...

RadeonOpenCompute/ROCm_Documentation: https://github.com/RadeonOpenCompute/ROCm_Documentation

ROCm-Developer-Tools/HIPIFYhttps://github.com/ROCm-Developer-Tools/HIPIFY :

> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

ROCmSoftwarePlatform/gpufort: https://github.com/ROCmSoftwarePlatform/gpufort :

> GPUFORT: S2S translation tool for CUDA Fortran and Fortran+X in the spirit of hipify

ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP:

> HIP is a C++ Runtime API and Kernel Language that allows developers to create portable applications for AMD and NVIDIA GPUs from single source code. [...] Key features include:

> - HIP is very thin and has little or no performance impact over coding directly in CUDA mode.

> - HIP allows coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more.

> - HIP allows developers to use the "best" development environment and tools on each target platform.

> - The [HIPIFY] tools automatically convert source from CUDA to HIP.

> - * Developers can specialize for the platform (CUDA or AMD) to tune for performance or handle tricky cases.*

并且有一个自动转换工具 https://github.com/ROCm-Developer-Tools/HIPIFY https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-porting-guide.html

It might be worth checking out HIPIFY, which lets you automatically convert CUDA code to vendor neutral code that can be run on any GPU. Disclaimer, I have never used it and have no idea how it works.

CUDA zu AMD HIP conversion: https://github.com/ROCm-Developer-Tools/HIPIFY