HIP
SHARK
HIP | SHARK | |
---|---|---|
30 | 84 | |
3,462 | 1,385 | |
1.5% | 1.6% | |
8.9 | 9.4 | |
3 days ago | 3 days ago | |
C++ | Python | |
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.
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.
SHARK
- Llama 2 on ONNX runs locally
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[D] Confusion over AMD GPU Ai benchmarking
https://github.com/AUTOMATIC1111/stable-diffusion-webui, https://github.com/nod-ai/SHARK, those are the repos for the open source tools mentioned. u/CeFurkan has really nice tutorial videos on YouTube for stable diffusion. Automatic1111 is the most popular open source stable diffusion ui and has the biggest open source plug-in ecosystem currently. Nvidia’s compute driver is separate from normal driver and called cuda. Amd’s compute driver is called rocm. Most windows programs like games use apis like directx, Vulkan,metal, web gpu and not cuda. Most ml code was originally intended to run in on scientific computing systems that were Linux. Today the traditional windows gpu apis are tying to get better at gpu ml supports. Amd has no official windows ml code support and is Hoping that other developers figure it out for them but amd made their ml driver open source but no support for consumer graphics cards. Nvidia is proprietary ml driver but guaranteed support across all cards including consumer
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Amd Gpu not utilised
I got it working using SHARK with an AMD RX 480 on Windows 10.
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New to SD - Slow working
Here the link for shark, faster (uses vulkan) than automatic1111 with directml but has less functions https://github.com/nod-ai/SHARK
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7900 XTX Stable Diffusion Shark Nod Ai performance on Windows 10. Seem to have gotten a bump with the latest prerelease drivers 23.10.01.41
I would recommend trying out Nod AI's Shark (That is the link for the most recent 786.exe release), and see how it works for you. From others I've read, it does 512x512 pics at around 3 it/s, which I know isn't mind blowing, but it's good enough to do a pic in about 30 seconds.
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New here
Problem solve, i had it to work i simply put this nod's ai shark exe in my stabble diffusion folder and launch it instead of Webui-user -> Release nod.ai SHARK 20230623.786 · nod-ai/SHARK (github.com)
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I built the easiest-to-use desktop application for running Stable Diffusion on your PC - and it's free for all of you
How does it compare with Shark SD (I am not affiliated with it in any way)? (https://github.com/nod-ai/SHARK)
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after changing GPU from RX 470 4gb to RTX 3060 12GB, I decided to make a few cozy houses, and these are a few of them
you should if you want to run SD on your card https://github.com/nod-ai/SHARK
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20 minute load time per image on high end pc?
Forgive me for not reading you whole comment. I suspect you're version of the SD eb UI doesn't recognize the AMD GPU., so you're using the CPU. AMD GPUs only work with a few web UIs. Try Nod.ai's Shark variant
- AMD support for Microsoft® DirectML optimization of Stable Diffusion
What are some alternatives?
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!
stable-diffusion-webui - Stable Diffusion web UI
ZLUDA - CUDA on AMD GPUs
stable-diffusion-webui-directml - Stable Diffusion web UI
futhark - :boom::computer::boom: A data-parallel functional programming language
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
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.
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
ginkgo - Numerical linear algebra software package
AMD-Stable-Diffusion-ONNX-FP16 - Example code and documentation on how to get FP16 models running with ONNX on AMD GPUs [Moved to: https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16]
rocm-arch - A collection of Arch Linux PKGBUILDS for the ROCm platform
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.