awesome-python-typing VS sb-simd

Awesome Python Typing

yes, and python has a large amount of analyzers:

https://github.com/typeddjango/awesome-python-typing

> fully dynamic

Well, no, it's SBCL. Common Lisp has support for types, but most compilers only use them for optimization, SBCL goes one step further and emits warnings when you mismatch types. And looking at the code, I can see lots of type declarations.

It's also interesting to note that the code does not seem to be using SBCL's new SIMD library*, so it could be sped up even more.

* <https://github.com/marcoheisig/sb-simd>, see the texinfo file for documentation.

I suppose that using arrays + using SIMD instructions could be even faster. Someone is already doing that: https://github.com/marcoheisig/sb-simd/blob/master/examples/simd-dot.lisp .

Common Lisp (sbcl) performance via native implementation of simd [0] is very impressive ! It is litteraly acheieving C/Cpp speeds (within few ms). Great work by Marco Heisig

[0] https://github.com/marcoheisig/sb-simd

Here is another demonstration of how effective SIMD vectorization can be using sb-simd.

You can see on Programming Language and Compiler Benchmark site the amazing speed of SBCL when sb-simd is used for vectorization.

For SBCL-specific, Marco and Bela have put in a ton of work at sb-simd - may be the OP finds the relevant simd interface there!

And sb-simd is getting very-very impressive to say the least thanks to Marco Heisig.

You can use sb-simd for manual vectorisation with SBCL. Manual vectorisation is definitely more hassle than automatic vectorisation, but often worth it.

Fully agreed. One more library that could open up areas is also coming soon. Though documentation is still to be written. Please check sb-simd I wish I could have supported Marco even more.