Kay Hayen on Nuitka


October 23rd, 2015

1 hr 34 mins 35 secs

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Kay Hayen is a systems engineer from Germany who has dedicated his spare time to the creation of Nuitka, a library that will compile your Python project to C++. In this episode we talked to Kay about what inspired him to create the project, how it operates, and some of the challenges he has faced. It is a very interesting project and it has the potential to let you run your Python code in a whole new way!

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  • We are recording today on October 6th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Kay Hayen about the Nuitka project

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Interview with Kay Hayen

  • Introductions
    • German, family with 2 kids, one cat
    • Working in ATM (Air Traffic Management), tracker product
    • Systems Engineer
    • Nuitka as a hobbyist

  • How did you get introduced to Python?

    • Once was Perl “Guru”.
    • Python was getting a lot of positive press
    • Team decision to want to use readable stuff
    • CPAN was still more complete, but Python was making inroads

  • Can you describe how to pronounce the name of your project?

    • Wife Anna, Russian, Annuitka -> Nuitka

  • Can you briefly describe what Nuitka is and what your motivation was for creating it?

    • I was thinking a fully integrated and compatible compiler should be possible.
    • Why is nobody doing it?
    • I can do it.
    • I am doing it.
    • Take Python beyond current use cases.
      • Everbody currently using Python needs no compiler, or wouldn’t use it
      • Less need for time consuming C++/Python hybrid coding
      • Simple code should compile to fast code by default
      • Complex code should still work

  • On the project web site it says that Nuitka does a lot of clever things after being fed a Python project. Can you provide some details as to what some of that cleverness is?

    • Re-formulations of Python into simpler Python
      • No “class”
      • No “assert”
      • No complex assignments

    • SSA tracing

      • Attaching uses to assignments properly
        • Despite try/finally
        • Loops

      • Avoids checks for known defined/undefined values

    • Function inlining (coming)

    • Constant propagation

    • Closure variable removal

  • What is libpython and how is it used in both Nuitka and CPython?

    • Core of the Python interpreter
    • With Python VM and C interface
    • Nuitka can fall back to it
    • Avoiding it as often as we can, key to performance

  • Is there any way to provide hints to Nuitka to generate more optimized output?

    • Nuitka is yet to make a difference based on type information
    • Not yet there, but coming soonish. SSA was pre-requisite
    • PEP 484 will be unreliable type information, mostly useless
    • I want type hints that are checked at Python run time

  • What are some of the biggest challenges in generating statically compiled code from a language as dynamic as Python?

    • Python is compiled to .pyc files
    • Compatible Frame stack, cached
    • Exception handling of Python is terrible
    • CPython type system designed to be extensible
      • Extension types for functions, bound/unbound methods, generators, etc.

    • Many details to get right

  • Are there any particular Python constructs that Nuitka is unable to translate and as a corollary to that is the compilation step lossy at all or do you have some way of ensuring that the functionality of the program remains unaltered?

    • Big point, no price attached
    • Except for not having bytecode, there is nothing missing
    • No pdb support
    • Edit / run cycle is not accelerated
    • That said: PyQt (integrated), PySide (available, unmerged), wxPython (available, maybe merged) needed patches to take compiled function/method objects for function objects too

  • Are there any particular types of programs that benefit the most from Nuitka’s compilation?

    • Bindings with ctypes of cffi compile into zero overhead C calls (planned)
    • Scientific programs are the most obvious goal (float type inference)
    • CPU bound or low latency programs

  • Is it possible to feed an entire project with multiple modules into Nuitka all at once or is the standard use to perform compilation one source file or submodule at a time?

    • You give it the main program and it recurses imports according to “PYTHONPATH”
    • nuitka –recurse-all “/usr/bin/hg” supposed to work
    • Might have to give directories with program plug-ins

  • I’m curious about what led you to choose compilation to C++ for Nuitka rather than making Nuitka an LLVM back end like Numba?

    • When I started Nuitka, I was using C++0x and variadic templates
    • Wanted to make a proof of concept that compatibility and integration is feasible
    • From there, code generation got less high level to goto ridden C

  • How does Nuitka compare to projects like Numba or Cython?

    • Graceful degradation goal
    • Complete compatibility with Python whole stack

  • How does Nuitka compare to PyPy? – Kay

    • PyPy is the coolest project ever
    • Pure Python goals shared

  • How can users evaluate the performance of Nuitka – Kay

    • They currently cannot
    • Developing a tool to compare CPython and Nuitka runs
      • Based on vmprof from PyPy people
      • Identify parts of program where Nuitka is slower
      • Links to source code

    • To be done, help needed.

    • Nuitka is only starting to get to serious performance

      • Compatibility is such a high bar to take
      • C++ to C took a year (avoiding C++ exceptions)
      • SSA literally took forever


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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA