Displaying 1 to 9 from 9 results

emu - a language for programming GPUs, with a focus on ergonomics first and performance second

  •    Rust

⚠ Please note that while Emu 0.2.0 is quite usable, it suffers from 2 key issues. It firstly does nothing to minimize CPU-GPU data transfer and secondly it's compiler is not well-tested. These can be reasons not to use Emu 0.2.0. A new version of Emu is in the works, however, with significant improvements in the language, compiler, and compile-time checker. This new version of Emu should be released some time in Q4 of 2019. But unlike OpenCL/CUDA/Halide/Futhark, Emu is embedded in Rust. This lets it take advantage of the ecosystem in ways...

awesome-metal - A collection of Metal and MetalKit projects and resources

  •    

I don't know how to ogranize or name these. This can just be the default catch at the end of the page. Just dump links here if you don't have time to properly place. This all is an experiment in a shared Slack note. Everyone can and should edit to make better.

OpenCL-101 - Learn OpenCL step by step.

  •    C

Learn OpenCL step by step as below. Using Docker is convenient, which you don't need config and install enviroments for all about OpenCL. Of course, install Docker Community Edition first and then search relative images in DockerHub.




metal-gpgpu - Collection of notes on how to use Apple’s Metal API for compute tasks

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Metal is a low-level graphics programming API for iOS and macOS but it can also be used for general-purpose compute on these devices. Unfortunately, Metal is not extensively documented. And the existing docs assume that you already know other graphics or compute APIs such as OpenGL, OpenCL, or CUDA. Hopefully these notes will help you understand Metal a little better. Mostly I collected this information because my poor brain can't possibly remember all this stuff, and I thought it would be useful to turn it into a community thing.

vuh - Vulkan compute for people

  •    C++

Vulkan is the most widely supported GPU programming API on modern hardware/OS. It allows to write truly portable and performant GPU accelerated code that would run on iOS, Android, Linux, Windows, macOS... NVidia, AMD, Intel, Adreno, Mali... whatever. At the price of ridiculous amount of boilerplate. Vuh aims to reduce the boilerplate to (a reasonable) minimum in most common GPGPU computing scenarios. The ultimate goal is to beat OpenCL in usability, portability and performance. saxpy implementation using vuh.


cuda-kat - CUDA kernel author's tools

  •    Cuda

... while not committing to any particular framework, paradigm or class hierarchy - and not compromising performance. The library has Doxygen documentation, available here. However - it is far from being complete.






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