cannoli VS pymartini

This is a great read, and it's fantastic to see all the work being done to evaluate and improve the language!

Shameless plug alert: the dynamic-nature of the language is actually something that I had studied a few years back [1]. Particularly the variable and object attribute look ups! My work was just a master's thesis, so we didn't go too deep into more tricky dynamic aspects of the language (e.g. eval, which we restricted entirely). But we did see performance improvements by restricting the language in certain ways that aid in static analysis, which allowed for more performant runtime code. But for those interested, the abstract of my thesis [2] gives more insight into what we were evaluating.

Our results showed that restricting dynamic code (code that is constructed at run time from other source code) and dynamic objects (mutation of the structure of classes and objects at run time) significantly improved the performance of our benchmarks.

[1]: https://github.com/joncatanio/cannoli

[2]: https://digitalcommons.calpoly.edu/theses/1886/

They can! Numpy exposes a C API to other Python programs [0]. It's not hard to write a Cython library that uses the Numpy C API directly and does not cross into Python [1].

[0]: https://numpy.org/doc/stable/reference/c-api/index.html

[1]: https://github.com/kylebarron/pymartini/blob/4774549ffa2051c...