hissp VS cl-cuda

Hissp is a Python library that can compile a whole program into one Python expression.

If you want Python to be as customizable as Lissp, check out Hissp (and Hebigo).

Hissp is really cool for metaprogramming Python. There's also macropy, but it's harder to use.

I prefer https://github.com/gilch/hissp, where Hy has to use shims to pretend statements are expressions, Hissp just targets the expression subset in the first place. (though as you mentioned, hy has a lot of literature and support around it, where as you're going to have to find your own way around hissp)

No shortage of options, e.g. Dg, Mochi, Coconut, and Hebigo (based on Hissp[1]).

[1]: https://github.com/gilch/hissp

If you want Lisp metaprogramming plus Python ecosystem, check out Hissp

Hissp is really interesting. Read through the docs and you'll understand Python more deeply. It works well with Toolz and Pyrsistent.

> 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.