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Comment by nologic01 | Hacker News Reader

In retrospect there is no particular reason why the "two-language" approach of Python to numerically intensive computations should not have been primarily in Fortran rather than C/C++.

A Python/Fortran combination would have made the development of high-performing HPC type applications more accessible to scientists, more fun, and would have given Fortran a more secure future.

The decisions or non-decisions, the culture and approach to community etc of key stakeholders of language ecosystems do have implications.

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> A Python/Fortran combination would have made the development of high-performing HPC type applications more accessible to scientists, more fun, and would have given Fortran a more secure future.

SciPy makes heavy use of Fortran. Numpy uses Fortran (but it's optional). f2py compiles fortran into python modules with little fuss. Python's ctypes module can directly call fortran libraries.

But to say Python should have been implemented in Fortran rather than C to enable Fortran interop by default -- that doesn't make sense. Python was designed as a scripting language for C-based operating systems, so it needed to be able to use pre-build unix libraries written in C, and it also needed to be embeddable in C programs. At the time neither of these things was easy via Fortran (cpython predates the Fortran 90 standard).

Cython got started in 2007. F90 was very mature by then. In any case its not a binary choice C vs Fortran, and definitely not about a Fortran implementation of Python itself. It is about the ease of integrating performant bits written in something other than C/C++, not by HPC specialists but domain scientists. The examples you mention show that this is possible but its not what people have been doing in the broader data science area. There might be other benefits for adopting C++ instead of F90 as the computing layer but overall it might have been a missed opportunity.

Cython is not cpython. cpython started in 1990.

Edit: In case you're confused, cpython is the de-facto standard implementation of Python. Cython is something entirely different (it compiles Python to C). Here's a good summary of Python's history: https://www.geeksforgeeks.org/history-of-python/.

But I never suggested that cpython should have been implemented in Fortran. The discussion is about how Python could have tapped F90's HPC capabilities instead of relying heavily on C++ for this. Think Cython, pybind11, Boost.python etc.

Except Python doesn't use C++ at all -- using C++ from Python is not very easy. The reason is that Python's native FFI interface is in C, because the interpreter is written in C. The reason Jython, PyPy, IronPython, etc. never got traction is because of all the Python core libraries written in C.

From a Python implementor standpoint, you have to implement Python's interpreter in the language you want to do the tight loops in. So for example, Jython was a reimplementation of Python on the JVM to make it possible to easily use Java for the tight loops. IronPython was the same, but for .NET.

You say it's cultural, but it's all about ergonomics and backwards compatibility. Making it easy to write tight loops in Fortran means changing the FFI API to be fortran friendly, which it is not.

Just try writing your MPI code with a combination of Fortran and Python. I did it circa 2005 and it was a technical nightmare.

Python has been around since early 90s and it’s main implementation was written in C though. It’s always had first class support for writing C extensions so it’s not illogical that wrapping functions written in other languages interface with it by targeting that C API.

> In retrospect there is no particular reason why the "two-language" approach of Python to numerically intensive computations should not have been primarily in Fortran rather than C/C++.

Python’s focus on C-extensibility predates its explosion in science and is a “particular reason” for this.

Its not an abstract, perfect-world, ivory tower reason, but most real reasons aren’t.

Its seems like a historical accident. Not every combination of things or a new direction that (in principle) might make some sense gets to receive timely attention.

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