lofn VS numcl

I wrote a templating library that was used for a Slack close we developed a number of years back.

It parses the HTML with embedded code, and generates a Lisp function which is compiled on the fly thanks to Lisp giving runtime access to the compiler. Whenever the source file is changed, the code is regenerated and compiled.

Other languages do the same, but in those cases they have to generate source code and call the compiler as an external program, and then load the generated code into the running program using dynamic linking. The Lisp approach is much more efficient.

Thanks to the quality of SBCL, the templates execute with native performance, which is something very few other solutions do.

The project is unfortunately not documented, but the code is available here: https://github.com/lokedhs/lofn

Is it me or numcl is faster than magicl? Matrix multiplication on magicl with pure lisp backend is

Interesting, I will, thanks! I am aware of numcl for CL, but I don't think it is "there" yet :).

The first that comes to mind is numcl. This works if (i) performance is not seriously a concern, (ii) you are not annoyed by julia-like JIT/JAOT compilation delays, (iii) copy-based slicing won't be a performance issue for you. To be fair, limitation (i) might be overcome by writing a better (simd-based) backend for numcl. numcl is fast, it compiles to fairly good code, but simd can boost the performance by another 4-8 times or so.

am I missing something that already exists (numcl / Alexandria / core language, etc?)

>Either implement numpy equivalent on your own or half of your code is data massaging data between libraries

I haven't tested this but here you go:

https://github.com/numcl/numcl

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.

numcl - Numpy clone in Common Lisp. [LGPL3][9].

[3] https://github.com/numcl/numcl