rocFFT
VkFFT
| rocFFT | VkFFT | |
|---|---|---|
| 1 | 37 | |
| 146 | 1,443 | |
| 0.7% | - | |
| 9.0 | 8.1 | |
| 7 days ago | about 1 month ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | MIT License |
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rocFFT
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"error making: rocfft" when installing rocm-tensorflow
Might make sense to raise an issue at https://github.com/ROCmSoftwarePlatform/rocFFT ?
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
What are some alternatives?
HIP-CPU - An implementation of HIP that works on CPUs, across OSes.
wgpu - Cross-platform, safe, pure-rust graphics api.
rocm-arch - A collection of Arch Linux PKGBUILDS for the ROCm platform
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.
OceanFFT - OpenGL Demo: Simulating Ocean Waves with FFT
rust-gpu - š Making Rust a first-class language and ecosystem for GPU shaders š§
aomp - AOMP is an open source Clang/LLVM based compiler with added support for the OpenMPĀ® API on Radeonā¢ GPUs. Use this repository for releases, issues, documentation, packaging, and examples.
cuda-samples - Samples for CUDA Developers which demonstrates features in CUDA Toolkit
benchmarking-fft - choosing FFT library...
xNVMe - Portable and high-performance libraries and tools for NVMe devices as well as support for traditional/legacy storage devices/interfaces.
ROCm - AMD ROCmā¢ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
neuronika - Tensors and dynamic neural networks in pure Rust.