VkFFT
ROCm
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VkFFT | ROCm | |
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37 | 198 | |
1,441 | 3,637 | |
- | - | |
8.1 | 0.0 | |
about 1 month ago | 4 months ago | |
C++ | Python | |
MIT License | MIT License |
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.
VkFFT
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[P] - VkFFT now supports quad precision (double-double) FFT computation on GPU
Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero and Metal. In the latest update, I have added support for quad-precision double-double emulation for FFT calculation on most modern GPUs. I understand that modern ML is going in the opposite low-precision direction, but I still think that it may be useful to have this functionality at least for some prototyping and development of concepts.
- VkFFT now supports quad precision (double-double) FFT computation on GPU
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VkFFT: Vulkan/CUDA/Hip/OpenCL/Level Zero/Metal Fast Fourier Transform Library
Not quite what I asked for, but close enough for now...
https://github.com/DTolm/VkFFT/discussions/126
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Implementing complex numbers (and FFT) elegantly with just algebraic datatypes (no machine floats)
Source - I have made a somewhat functional programming-like FFT library (https://github.com/DTolm/VkFFT/tree/develop) which also operates on abstract data containers. Maybe it can be interesting to you from the algorithmic point of view.
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how does Vulkan compare to CUDA?
VkFFT is a use-case I've heard of where Vulkan-Compute is faster than its Cuda and OpenCL counter-part: https://github.com/DTolm/VkFFT
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VkFFT now supports Apple Metal API - M1 Pro GPU FFT benchmarking
Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL and Level Zero. In the latest update, I have added support for Apple Metal API, which will allow VkFFT to run natively on modern Apple SoC. I have tested it on MacBook Pro with an M1 Pro 8c CPU/14c GPU SoC single precision on 1D batched FFT test of all systems from 2 to 4096. Achieved bandwidth is calculated as 2*system size divided by the time taken per FFT - minimum memory that has to be transferred between DRAM and GPU:
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Any good compute shader tutorials?
Another possible project to look at is https://github.com/DTolm/VkFFT
- VkFFT now supports Rader's algorithm - A100 and MI250 benchmarks: Part 2
- VkFFT now supports Rader's algorithm - A100 and MI250 benchmarks
ROCm
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AMD May Get Across the CUDA Moat
Yep, did exactly that. IMO he threw a fit, even though AMD was working with him squashing bugs. https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...
- ROCm 5.7.0 Release
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ROCm Is AMD's #1 Priority, Executive Says
Ok, I wonder what's wrong. maybe it's this? https://stackoverflow.com/questions/4959621/error-1001-in-cl...
Nope. Anything about this on the arch wiki? Nope
This bug report[2] from 2021? Maybe I need to update my groups.
[2]: https://github.com/RadeonOpenCompute/ROCm/issues/1411
$ ls -la /dev/kfd
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Simplifying GPU Application Development with HMM
HMM is, I believe, a Linux feature.
AMD added HMM support in ROCm 5.0 according to this: https://github.com/RadeonOpenCompute/ROCm/blob/develop/CHANG...
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AMD Ryzen APU turned into a 16GB VRAM GPU and it can run Stable Diffusion
Woot AMD now supports APU? I sold my notebook as i hit a wall when trying rocm [1] Is there a list oft Wirkung apu's ?
[1] https://github.com/RadeonOpenCompute/ROCm/issues/1587
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Nvidia's CUDA Monopoly
Last I heard he's abandoned working with AMD products.
https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...
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Nvidia H100 GPUs: Supply and Demand
They're talking about the meltdown he had on stream [1] (in front of the mentioned pirate flag), that ended with him saying he'd stop using AMD hardware [2]. He recanted this two weeks after talking with AMD [3].
Maybe he'll succeed, but this definitely doesn't scream stability to me. I'd be wary of investing money into his ventures (but then I'm not a VC, so what do I know).
[1] https://www.youtube.com/watch?v=Mr0rWJhv9jU
[2] https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...
[3] https://twitter.com/realGeorgeHotz/status/166980346408248934...
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Open or closed source Nvidia driver?
As for rocm support on consumer devices, AMD wont even clarify what devices are supported. https://github.com/RadeonOpenCompute/ROCm/pull/1738
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Why Nvidia Keeps Winning: The Rise of an AI Giant
He flamed out, then is back after Lisa Su called him (lmao)
https://geohot.github.io/blog/jekyll/update/2023/05/24/the-t...
https://www.youtube.com/watch?v=Mr0rWJhv9jU
https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...
https://geohot.github.io/blog/jekyll/update/2023/06/07/a-div...
On a personal level that youtube doesn't make him come off looking that good... like people are trying to get patches to him and generally soothe him/damage control and he's just being a bit of a manchild. And it sounds like that's the general course of events around a lot of his "efforts".
On the other hand he's not wrong either, having this private build inside AMD and not even validating official, supported configurations for the officially supported non-private builds they show to the world isn't a good look, and that's just the very start of the problems around ROCm. AMD's OpenCL runtime was never stable or good either and every experience I've heard with it was "we spent so much time fighting AMD-specific runtime bugs and specs jank that what we ended up with was essentially vendor-proprietary anyway".
On the other other hand, it sounds like AMD know this is a mess and has some big stability/maturity improvements in the pipeline. It seems clear from some of the smoke coming out of the building that they're cooking on more general ROCm support for RDNA cards, and generally working to patch the maturity and stability issues he's talking about. I hate the "wait for drivers/new software release bro it's gonna fix everything" that surrounds AMD products but in this case I'm at least hopeful they seem to understand the problem, even if it's completely absurdly late.
Some of what he was viewing as "the process happening in secret" was likely people doing rush patches on the latest build to accommodate him, and he comes off as berating them over it. Again, like, that stream just comes off as "mercurial manchild" not coding genius. And everyone knew the driver situation is bad, that's why there's notionally alpha for him to realize here in the first place. He's bumping into moneymakers, and getting mad about it.
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Disable "SetTensor/CopyTensor" console logging.
I tried to train another model using InceptionResNetV2 and the same issues happens. Also, this happens even using the model.predict() method if using the GPU. Probably this is an issue related to the AMD Radeon RX 6700 XT or some mine misconfiguration. System Inormation: ArchLinux 6.1.32-1-lts - AMD Radeon RX 6700 XT - gfx1031 Opened issues: - https://github.com/RadeonOpenCompute/ROCm/issues/2250 - https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/issues/2125
What are some alternatives?
wgpu - Cross-platform, safe, pure-rust graphics api.
tensorflow-directml - Fork of TensorFlow accelerated by DirectML
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.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧
rocm-arch - A collection of Arch Linux PKGBUILDS for the ROCm platform
cuda-samples - Samples for CUDA Developers which demonstrates features in CUDA Toolkit
oneAPI.jl - Julia support for the oneAPI programming toolkit.
rocFFT - Next generation FFT implementation for ROCm
SHARK - SHARK - High Performance Machine Learning Distribution
xNVMe - Portable and high-performance libraries and tools for NVMe devices as well as support for traditional/legacy storage devices/interfaces.
llama.cpp - LLM inference in C/C++