schism VS Numba

I don't know why you've been downvoted, I've given you an upvote for linking to an interesting project (even if it's linked in some way to Google). I'd also like to link to the updated GH link: <https://github.com/schism-lang/schism>.

Looks like Schism (https://github.com/schism-lang/schism) got part of the way there, but it unfortunately seems to be dead.

It would be interesting to reboot something like Lush but using Wasm and Scheme with https://github.com/schism-lang/schism then you could use code generation internally be emitting wasm from your schism code and then reloading the entire environment.

I love E! Or at least the problems it is trying to solve. As you know Wasm also has a capabilities model. And it is fairly trivial to persist the Wasm heap, it just an array of bytes. I think Wasm aligns nicely.

Chez is a great Scheme, but it doesn't have a Wasm backend. I find https://github.com/schism-lang/schism very interesting.

As for C programs going crazy, well yeah. I did a thing where I would copy of the body of functions around in memory, it worked on some version of Linux and GCC, but only by accident. I would be much less comfortable doing this kind of circuit bending than modifying Python stack frames. If I were to achieve a similar goal in the future, I'd use TCC, generate C code and compile directly into memory.

Framehacks aren't going to do the same thing, and one should have tests for it regardless. Framehacks get you tail calls, stack scope and a bunch of other nice properties.

Happy Hacking!

There is a WIP unofficial project from developers at Google called Schism [1].

[1] https://github.com/schism-lang/schism

Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.

Simulations are, at least in my experience, numba’s [0] wheelhouse.

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

That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.

A super-fast backtesting engine built in NumPy and accelerated with Numba.

Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.

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

For the benefit of future readers: https://numba.pydata.org/

Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.