i-love-compute
HIP
i-love-compute | HIP | |
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2 | 30 | |
- | 3,478 | |
- | 2.0% | |
- | 8.9 | |
- | 6 days ago | |
C++ | ||
- | MIT License |
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i-love-compute
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LXC GPU Passthrough with AMD GPU Pro drivers?
Good luck. Regarding amd opencl on linux you can find more resources and install scripts here
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AI Seamless Texture Generator Built-In to Blender
From the Arch wiki, which has a list of GPU runtimes (but not TPU or QPU runtimes) and arch package names: OpenCL, SYCL, ROCm, HIP,: https://wiki.archlinux.org/title/GPGPU :
> GPGPU stands for General-purpose computing on graphics processing units.
- "PyTorch OpenCL Support" https://github.com/pytorch/pytorch/issues/488
- Blender re: removal of OpenCL support in 2021 :
> The combination of the limited Cycles split kernel implementation, driver bugs, and stalled OpenCL standard has made maintenance too difficult. We can only make the kinds of bigger changes we are working on now by starting from a clean slate. We are working with AMD and Intel to get the new kernels working on their GPUs, possibly using different APIs (such as CYCL, HIP, Metal, …).
- https://gitlab.com/illwieckz/i-love-compute
- https://github.com/vosen/ZLUDA
- https://github.com/RadeonOpenCompute/clang-ocl
AMD ROCm: https://en.wikipedia.org/wiki/ROCm
AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs:
HIP
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Porting HPC Applications to AMD Instinct MI300A Using Unified Memory and OpenMP
>ROCm or HIP?
I'm not sure that's even the right question to ask. Afaik ROCm is the name of that entire tech stack and HIP is AMD's equivalent to CUDA C++ (they basically replicated the API and replaced every "CUDA" by "hip", they have functions called "hipmalloc" and "hipmemcpy").
The repository is located at https://github.com/ROCm/HIP.
- Hip: Runtime API and Kernel Language for Portable Apps for AMD and Nvidia GPUs
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Open-source project ZLUDA lets CUDA apps run on AMD GPUs
Is it perhaps because they want people to use HIP?
> HIP is very thin and has little or no performance impact over coding directly in CUDA mode.
> The HIPIFY tools automatically convert source from CUDA to HIP.
1. https://github.com/ROCm/HIP
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AMD's Next GPU Is a 3D-Integrated Superchip
AMD has released HIP and a tool called HIPIFY which kind of behaves like this but at the source level¹. Rather than try and just translate CUDA to work on AMD compute they are more focused on higher level tooling.
Currently they seem to have a particular focus on AI frameworks and tools like PyTorch/Tensorflow/ONNX. They have sponsored and helped with a lot of PyTorch development for example, so PyTorch support for AMD is much better than it was this time last year².
¹(https://github.com/ROCm/HIP)
²(https://pytorch.org/blog/experience-power-pytorch-2.0/)
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
> what would be the point for someone to add ROCm support to various pieces of software which currently require CUDA
It isn't just old cards though, CUDA is a point of centralization on a single provider during a time when access to that providers higher end cards isn't even available and that is causing people to look elsewhere.
ROCm supports CUDA through the included HIP projects...
https://github.com/ROCm/HIP
https://github.com/ROCm/HIPCC
https://github.com/ROCm/HIPIFY
The later will regex replace your CUDA methods with HIP methods. If it is as easy as running hipify on your codebase (or just coding to HIP apis), it certainly makes sense to do so.
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Nvidia on the Mountaintop
AMD's equivalent is HIP [1], for sufficiently flexible definitions of "equivalent". I can't speak to how complete/correct/performant it is (I'm just a guy running tutorial/toy-level ML stuff on an RDNA1 card), but part of AMD's problem is that it might not practically matter how well they do this because the broader ecosystem support specifically for the CUDA stack is so entrenched.
[1] https://github.com/ROCm-Developer-Tools/HIP
- Stable Diffusion in pure C/C++
- Would love to hear your information and knowledge to simplify my understanding on AMD's positioning in the AI market
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Ask HN: C++ still dominates on GPUs, why not Rust?
From what I know, modern GPUs are still programmed with C++ exclusively. See CUDA [0] for Nvidia and ROCm [1] for AMD.
Why is this? Why Rust is not loved there?
[0] https://docs.nvidia.com/cuda/
[1] https://github.com/ROCm-Developer-Tools/HIP
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[P] RWKV C++ Cuda library with no dependencies, no torch, and no python
Go ahead and try to ship ROCm code that works on multiple consumer graphics cards on Linux, MacOS, and Windows. As an example of how much AMD cares about it, the installation notes linked to in the readme returns a 404.
What are some alternatives?
HIPIFY - HIPIFY: Convert CUDA to Portable C++ Code [Moved to: https://github.com/ROCm/HIPIFY]
AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
ZLUDA - CUDA on AMD GPUs
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
futhark - :boom::computer::boom: A data-parallel functional programming language
stable_diffusion.openvino
kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
CLIP-Mesh - Official implementation of CLIP-Mesh: Generating textured meshes from text using pretrained image-text models
ginkgo - Numerical linear algebra software package
dream-textures - Stable Diffusion built-in to Blender
rocm-arch - A collection of Arch Linux PKGBUILDS for the ROCm platform