Cython VS mypyc

The approach that I favour is to use Cython. The nice thing with this approach is that your code is still written as (almost) Python, but so long as you define all required types correctly it will automatically create the C extension for you. Early versions of Cython required using Cython specific typing (Python didn't have type hints when Cython was created), but it can now use Python's type hints.

and again, everything that was released after using an older version of cython.

Just for reference,

* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."

* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.

* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."

* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."

* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"

[0] https://github.com/Nuitka/Nuitka

[1] https://www.pypy.org/

[2] https://cython.org/

[3] https://numba.pydata.org/

[4] https://github.com/pyston/pyston

Profile and optimize the hotspots with cython (or whatever the cool kids are using these days... It's been a while.)

JIT essentially means generating machine code for the language on the fly, either during loading of the interpreter (method JIT), or by profiling and optimizing hotspots (tracing JIT). The language itself can be statically or dynamically typed. You could also compile a dynamic language ahead of time, for example, cython.

s/will never be/already is/g

https://github.com/mypyc/mypyc

You can compile python to c. Right now. Compatibility with extensions still needs a bit of work. But you can write extremely strict python.

That's without getting into things like cython.

That would be interesting! You might already be aware. But there's mypyc[0], which is an AOT compiler for Python code with type hints (that, IIRC, mypy uses to compile itself into a native extension).

Wanted to give you a head-start on the lit-review for your students I guess :)

[0] https://github.com/mypyc/mypyc

https://github.com/mypyc/mypyc

> Mypyc compiles Python modules to C extensions. It uses standard Python type hints to generate fast code. Mypyc uses mypy to perform type checking and type inference.

> Mypyc can compile anything from one module to an entire codebase. The mypy project has been using mypyc to compile mypy since 2019, giving it a 4x performance boost over regular Python.

I have not experience a 4x boost, rather between 1.5x and 2x. I guess it depends on the code.

Funny how emergence works with tools. Give a language too few tools but viral circumstances - the ecosystem diverges (Lisps, Javascript). Give it too long an iteration time but killer guarantees, you end up with committees. Python not falling into either of these traps should be understood as nothing short of magic in emergence.

I only recently discovered that python's reference typechecker, mypy, has a small side project for typed python to emit C [1], written entirely in python. Nowadays with python's rich specializer ecosystem (LLVM, CUDA, and just generally vectorized math), the value of writing a small program in anything else diminishes quickly.

Imagine reading the C++wg release notes in the same mood that you would the python release notes.

[1] https://github.com/mypyc/mypyc

> Note that the mypyc issue tracker lives in this repository! Please don't file mypyc issues in the mypy issue tracker.

See https://github.com/mypyc/mypyc/blob/master/show_me_the_code....

And mypyc https://github.com/mypyc/mypyc

More recent approaches include mypyc which is (on the tin) quite close to what you describe, and taichi that lives in between.

Have you encountered mypyc?