cl-cuda
MGL
Our great sponsors
cl-cuda | MGL | |
---|---|---|
5 | 22 | |
270 | 726 | |
- | 2.2% | |
0.0 | 4.2 | |
almost 3 years ago | 6 months ago | |
Common Lisp | C++ | |
MIT License | GNU Lesser General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cl-cuda
-
Why Lisp? (2015)
> You can write a lot of macrology to get around it, but there's a point where you want actual compiler writers to be doing this
this is not the job of compiler writers (although writing macros is akin to writing a compiler but i do not think that this is what you mean). in julia the numerical programming packages are not part of the standard library and a lot of it is wrappers around C++ code especially when the drivers to the underlining hardware are closed-source [0]. also here is the similar library in common lisp [1]
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
-
Hacker News top posts: Aug 14, 2021
A Common Lisp Library to Use Nvidia CUDA\ (0 comments)
- A Common Lisp Library to Use Nvidia CUDA
-
Machine Learning in Lisp
Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.
MGL
-
Is BC6H (COMPRESSED_RGB_BPTC_UNSIGNED_FLOAT_ARB) supported on Silicon Macs?
There's this repo which gives you OpenGL 4.6 thru Metal mappings https://github.com/openglonmetal/MGL
- Zink brings conformant OpenGL on Imagination GPUs
-
macOS OpenGL?
There is this github project that may help as a starting point to develop your code against https://github.com/openglonmetal/MGL but note it does say:
-
What is the best OpenGL version for cross platform application?
MacOS: GL 4.1 or use OpenGL 4.6 on Metal
- I want to talk about WebGPU
-
Opinion for graphic api's?
There is this https://github.com/openglonmetal/MGL which could give you OpenGL 4.6 over metal.
- Can I work on OpenGL with Mac M1 ?
- Mac + opengl
-
Using Metal framework
Are you sure about that? AFAIK OpenGL up to 4.1 is still supported on OSX. For anything higher there is also this project: https://github.com/openglonmetal/MGL
- How is Vulkan supposed to supersede OpenGL in practice?
What are some alternatives?
numcl - Numpy clone in Common Lisp
Cemu - Cemu - Wii U emulator
criterium - Benchmarking library for clojure
angle - A conformant OpenGL ES implementation for Windows, Mac, Linux, iOS and Android.
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
TH - Deep Learning Library for Common Lisp.
py4cl - Call python from Common Lisp
LearnOpenGL - Code repository of all OpenGL chapters from the book and its accompanying website https://learnopengl.com
hash-array-mapped-trie - A hash array mapped trie implementation in c.
semantic-release - :package::rocket: Fully automated version management and package publishing
LoopVectorization.jl - Macro(s) for vectorizing loops.
julia - The Julia Programming Language