cl4py VS cl-cuda

py4cl and cl4py rely on uiop:launch-program and python's subprocess respectively. These are portable to the extent uiop and subprocess are portable and do not require any additional installation.

If you want to use Python libs from CL, see py4cl: https://github.com/bendudson/py4cl the other way around, calling your efficient CL library from Python: https://github.com/marcoheisig/cl4py/ There might be more CL libraries than you think! https://github.com/CodyReichert/awesome-cl (or at least a project sufficiently advanced on your field to join forces ;) )

FYI you can call Python from CL: https://github.com/bendudson/py4cl and CL from Python: https://github.com/marcoheisig/cl4py/

If you don't know Emacs, see other editors: https://lispcookbook.github.io/cl-cookbook/editor-support.ht... If you want the more Smalltalk-like experience I'd go with the free LispWorks version: it has many GUI panes that allow to watch and discover the state of the program.

I personally couldn't stay long with Hylang. You won't get CL niceties: more language features, performance, standalone binaries, interactive debugger (all the niceties of an image-based development)…

> 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]

[0] https://github.com/JuliaGPU/CUDA.jl

[1] https://github.com/takagi/cl-cuda

A Common Lisp Library to Use Nvidia CUDA\ (0 comments)

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.