At least as fast as C++ is a bold claim, but this is an interestingly documented process. I'm keen.
I'm quite partial to Nuitka at this point, but I'm open to other Python compilers.
Yes, offloading to GPU we want to support naturally via NumPy syntax. We will look at this very soon, most likely via annotating that a given array lives on a GPU or host, and then array copy will copy it from host to device, etc.
However, it really is different from projects like this one, in that it doesn't attempt to obtain C-like speed (but does hope to do some optimisations). For example, x+=1 will still dynamically dispatch depending on the runtime type of x, and (if it's an int) do the normal Python arbitrary precision operation. But those will be called from machine code rather than interpreted byte code.
(Essentially, it unrolls the main loop of the CPython interpreter, which is written in C, for every byte code operation, and eliminates every case of the switch statement inside except the one that corresponds to this operation. That's what gets compiled.)
> Nuitka is the Python compiler. ... It then executes uncompiled code and compiled code together in an extremely compatible manner. Nuitka translates the Python modules into a C level program that then uses libpython and static C files of its own to execute in the same way as CPython does.
I'm happy to stick by the tool's own description of itself. But if you don't accept that, and Nuitka still isn't a compiler by you, sure. Go ahead with your pedantic and strictest definition of the meaning of a 'compiler'.
Learn to read English, before insisting on your pedantry as some sort of truth, in disregard of the tool's own intent/description. (And I do hope you take issue with everyone's description of Typescript as a compiled programming language too.)
I think my comment being devoid of content might have caused some frustration. So to make the best of everyone's time:
You can look into cython and pythran to see what I'm talking about. cython lets you optimize code step by step via generating an html page with your code and highlighting lines that still require the use of the python runtime. It lets you add types and cdef function definitions in order to reduce your dependency on the python runtime.
Another good example is pythran, which takes your python code and turns it into c++ code to be compiled by a c++ compiler. I understand that this isn't a direct compilation to machine code, but a middle step which lets you compile the output to machine code.
Then there is numba and taichi which have just-in-time compilation decorators. Taichi also provides a sophisticated runtime which lets you run parts of the code on a GPU.
Surprisingly, the best performance I've experienced among these examples was numba + numpy, even though numba alone can sometimes have optimizations that surpasses all compilation efforts, because it turns your loops into mathematical formulas and runs them at O(1) complexity when it can.