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Summary
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
- 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
