xNVMe VS VkFFT

Basically io_uring grew a lot, the latest API offers incredible tools but there are very few examples in any language (xnvme and fio), and in particular I'm struggling to understand how to do it in Rust: where should unsafe code stop? should I simply expose the io_uring api as unsafe, or should I do more work in C and present the ring when ready to rust?

The foundation of xNVMe is libxnvme, a user space library for working with NVMe devices. It provides a C API for memory management, that is, for allocating physical / DMA transferable memory when needed. An NVMe command interface allowing you to submit and complete NVMe commands in a synchronous as well as an asynchronous manner. Similar to libnvme, however, with a focus on I/O performance and portability. Thus, xNVMe and the libxnvme library works not just on Linux, but also on FreeBSD, Windows, and MacOS.

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.

Not quite what I asked for, but close enough for now...

https://github.com/DTolm/VkFFT/discussions/126

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.

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

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:

Another possible project to look at is https://github.com/DTolm/VkFFT