VkFFT
vuh
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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
vuh
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GLSL shaders for OpenCL
The only one that is relatively lightweight (and doesn't have dependencies) is vuh (https://github.com/Glavnokoman/vuh) that looks unsupported.
- Resources for Vulkan GPGPU searched
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Blender 3.0 takes support for AMD GPUs to the next level
Don't try to use it directly, you need a higher level library.
https://github.com/KomputeProject/kompute seems like what's taking off. There are also smaller ones like https://github.com/Glavnokoman/vuh etc.
What are some alternatives?
wgpu - Cross-platform, safe, pure-rust graphics api.
GLSL - GLSL Shading Language Issue Tracker
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.
ocl - OpenCL for Rust
rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧
uVkCompute - A micro Vulkan compute pipeline and a collection of benchmarking compute shaders
cuda-samples - Samples for CUDA Developers which demonstrates features in CUDA Toolkit
rocFFT - Next generation FFT implementation for ROCm
liblava - Modern and easy-to-use library for Vulkan
xNVMe - Portable and high-performance libraries and tools for NVMe devices as well as support for traditional/legacy storage devices/interfaces.
cuda-api-wrappers - Thin C++-flavored header-only wrappers for core CUDA APIs: Runtime, Driver, NVRTC, NVTX.