cunumeric VS py2many

I am biased because this is my research area, but I have to respectfully disagree. Actor models are awful, and the only reason it's not obvious is because everything else is even more awful.

But if you look at e.g., the recent work on task-based models, you'll see that you can have literally sequential programs that parallelize automatically. No message passing, no synchronization, no data races, no deadlocks. Read your programs as if they're sequential, and you immediately understand their semantics. Some of these systems are able to scale to thousands of nodes.

An interesting example of this is cuNumeric, which allows you to take sequential Python programs that use NumPy, and by changing one line (the import statement), run automatically on clusters of GPUs. It is 100% pure awesomeness.

https://github.com/nv-legate/cunumeric

(I don't work on cuNumeric, but I do work on the runtime framework that cuNumeric uses.)

I know this probably isn't intended for performance, but it would be fun to run this in cuNumeric [1] and see how it scales.

[1]: https://github.com/nv-legate/cunumeric

If you want built-in GPU support (and distributed), you should check out cuNumeric (released by NVIDIA in the last week or so). Also avoids needing to manually specify chunk sizes, like it says in a sibling comment.

https://github.com/nv-legate/cunumeric

Try dask

Distribute your data and run everything as dask.delayed and then compute only at the end.

Also check out legate.numpy from Nvidia which promises to be a drop in numpy replacement that will use all your CPU cores without any tweaks on your part.

https://github.com/nv-legate/legate.numpy

Something for the HPC tools category: https://github.com/nv-legate/legate.numpy

You might be interested in Legate [1]. It supports the NumPy interface as a drop-in replacement, supports GPUs and also distributed machines. And you can see for yourself their performance results; they're not far off from hand-tuned MPI.

[1]: https://github.com/nv-legate/legate.numpy

Disclaimer: I work on the library Legate uses for distributed computing, but otherwise have no connection.

> Another problem is that there are hundreds of built-in library functions that need to be compiled from Python from C

An approach I've advocated as one of the main authors of py2many is that all of the python builtin functions be written in a subset of python[1] and then compiled into native code. This has the benefit of avoiding GIL, problems with C-API among other things.

Do checkout the examples here[2] which work out of the box for many of the 8-9 supported backends.

[1] https://github.com/py2many/py2many/blob/main/doc/langspec.md

https://github.com/py2many/py2many/blob/main/doc/langspec.md

Reimplement a large enough, commonly used subset of python stdlib using this dialect and we may be in the business of writing cross platform apps (perhaps start with android and Ubuntu/Gnome)

For py2many, there is an informal specification here:

https://github.com/py2many/py2many/blob/main/doc/langspec.md

Would be great if all the authors of "python-like" languages get together and come up with a couple of specs.

I say a couple, because there are ones that support the python runtime (such as cython) and the ones which don't (like py2many).

It'd not fully solve your issue, but have you ever seen https://github.com/py2many/py2many ?

Cython doesn't consume python3 type hints and needs special type hints of its own. But it's certainly more mature than other players in the field.

What we need is a rpython suitable for app programming and a stdlib written in that dialect.

https://github.com/py2many/py2many/blob/main/doc/langspec.md

No intermediate AST. To understand the various stages of transpilation and separation of language specific and independent rewriters, this file is a good starting point:

https://github.com/adsharma/py2many/blob/main/py2many/cli.py...

Link to the test that's relevant for this discussion:

https://github.com/adsharma/py2many/blob/main/tests/cases/in...

This is an explicit deviation from python's bigint, which doesn't map very well to systemsey languages. The next logical step is to build on this to have dependent and refinement types.

Work in progress here:

https://github.com/adsharma/Typpete