oneDNN VS oidn

If you are talking about non-small matrix multiplication in MKL, is now in opensource as a part of oneDNN. It literally has exactly the same code, as in MKL (you can see this by inspecting constants or doing high-precision benchmarks).

For small matmul there is libxsmm. It may take tremendous efforts make something faster than oneDNN and libxsmm, as jit-based approach of https://github.com/oneapi-src/oneDNN/blob/main/src/gpu/jit/g... is too flexible: if someone finds a better sequence, oneDNN can reuse it without major change of design.

But MKL is not limited to matmul, I understand it...

For completeness, it looks like this question was posted to the oneDNN GitHub repo and the response was to stay tune for updates.

From the release note of oneDNN v2.5:

Intel are trying to support their oneAPI compute framework on Arm and IBM POWER and z/Architecture (s390x) but since they ever released only a single discrete GPU with the Xe architecture it's unclear whether they'll support Xe GPU compute on e.g. ARM https://github.com/oneapi-src/oneDNN

For instance, try to build this project: https://github.com/OpenImageDenoise/oidn - it uses CMake. It will take you some time to build it - since you need first to figure out its dependencies and how to install them - if you have no idea of its dependencies it might take you a few days to figure out how to build it for your platfom. I call this the Build Problem - this is the struggle to build something.

At the heart of the Intel Open Image Denoise library is a collection of efficient deep learning based denoising filters, which were trained to handle a wide range of samples per pixel (spp), from 1 spp to almost fully converged. Thus it is suitable for both preview and final frame rendering.

Intel® Open Image Denoise

IntelDenoiser

Hooray! Now to wait for OIDN to upgrade to this version of oneDNN.

Results from low ray-count path tracing can be fairly noisy, especially with reflections and emissive materials, so I'm using Intel Open Image Denoise. The download for this at 45Mb is over 5x larger than our Avoyd installer, so we don't distribute it but instead link to it as a plugin the user can install. Adding documentation for this was fairly easy using our open sourced imgui_markdown.h.

It was this one: https://www.openimagedenoise.org/

Thanks! It's all done on the CPU, yep. It took about ~40 minutes to render this, but I probably took way more samples than I actually needed to. I did later decide to denoise it using the Open Image Denoiser (https://github.com/OpenImageDenoise/oidn), which made it look quite a bit better, but I'm not sure where the aliasing along the mirror edge came from.