Extending python – to swig, not to swig or Cython

Question:

I found the bottleneck in my python code, played around with psycho etc. Then decided to write a c/c++ extension for performance.

With the help of swig you almost don’t need to care about arguments etc. Everything works fine.

Now my question: swig creates a quite large py-file which does a lot of ‘checkings’ and ‘PySwigObject’ before calling the actual .pyd or .so code.

Does anyone of you have any experience whether there is some more performance to gain if you hand-write this file or let swig do it.

Asked By: RSabet

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Answers:

If its not a big extension, boost::python might also be an option, it executes faster than swig, because you control what’s happening, but it’ll take longer to dev.

Anyways swig’s overhead is acceptable if the amount of work within a single call is large enough. For example if you issue is that you have some medium sized logic block you want to move to C/C++, but that block is called within a tight-loop, frequently, you might have to avoid swig, but I can’t really think of any real-world examples except for scripted graphics shaders.

Answered By: Robert Gould

You should consider Boost.Python if you are not planning to generate bindings for other languages as well with swig.

If you have a lot of functions and classes to bind, Py++ is a great tool that automatically generates the needed code to make the bindings.

Pybindgen may also be an option, but it’s a new project and less complete that Boost.Python.


Edit:

Maybe I need to be more explicit about pro and cons.

  • Swig:

    pro: you can generate bindings for many scripting languages.

    cons: I don’t like the way the parser works. I don’t know if the made some progress but two years ago the C++ parser was quite limited. Most of the time I had to copy/past my .h headers add some % characters and give extra hints to the swig parser.

    I was also needed to deal with the Python C-API from time to time for (not so) complicated type conversions.

    I’m not using it anymore.

  • Boost.Python:

    pro:
    It’s a very complete library. It allows you to do almost everything that is possible with the C-API, but in C++. I never had to write C-API code with this library. I also never encountered bug due to the library. Code for bindings either works like a charm or refuse compile.

    It’s probably one of the best solutions currently available if you already have some C++ library to bind. But if you only have a small C function to rewrite, I would probably try with Cython.

    cons: if you don’t have a pre-compiled Boost.Python library you’re going to use Bjam (sort of make replacement). I really hate Bjam and its syntax.

    Python libraries created with B.P tend to become obese. It also takes a lot of time to compile them.

  • Py++ (discontinued): it’s Boost.Python made easy. Py++ uses a C++ parser to read your code and then generates Boost.Python code automatically. You also have a great support from its author (no it’s not me 😉 ).

    cons: only the problems due to Boost.Python itself. Update: As of 2014 this project now looks discontinued.

  • Pybindgen:

    It generates the code dealing with the C-API. You can either describe functions and classes in a Python file, or let Pybindgen read your headers and generate bindings automatically (for this it uses pygccxml, a python library wrote by the author of Py++).

    cons: it’s a young project, with a smaller team than Boost.Python. There are still some limitations: you cannot use multiple inheritance for your C++ classes, Callbacks (not automatically, custom callback handling code can be written, though). Translation of Python exceptions to C.

    It’s definitely worth a good look.

  • A new one:
    On 2009/01/20 the author of Py++ announced a new package for interfacing C/C++ code with python. It is based on ctypes. I didn’t try it already but I will! Note: this project looks discontiued, as Py++.

  • CFFI: I did not know the existence of this one until very recently so for now I cannot give my opinion. It looks like you can define C functions in Python strings and call them directly from the same Python module.

  • Cython: This is the method I’m currently using in my projects. Basically you write code in special .pyx files. Those files are compiled (translated) into C code which in turn are compiled to CPython modules.
    Cython code can look like regular Python (and in fact pure Python are valid .pyx Cython files), but you can also more information like variable types. This optional typing allows Cython to generate faster C code. Code in Cython files can call both pure Python functions but also C and C++ functions (and also C++ methods).

    It took me some time to think in Cython, that in the same code call C and C++ function, mix Python and C variables, and so on. But it’s a very powerful language, with an active (in 2014) and friendly community.

Answered By: ascobol

Before giving up on your python code, have a look at ShedSkin. They claim better performance than Psyco on some code (and also state that it is still experimental).

Else, there are several choices for binding C/C++ code to python.

Boost is lengthy to compile but is really the most flexible and easy to use solution.

I have never used SWIG but compared to boost, it’s not as flexible as it’s generic binding framework, not a framework dedicated to python.

Next choice is Pyrex. It allows to write pseudo python code that gets compiled as a C extension.

Answered By: Philippe F

For sure you will always have a performance gain doing this by hand, but the gain will be very small compared to the effort required to do this. I don’t have any figure to give you but I don’t recommend this, because you will need to maintain the interface by hand, and this is not an option if your module is large!

You did the right thing to chose to use a scripting language because you wanted rapid development. This way you’ve avoided the early optimization syndrome, and now you want to optimize bottleneck parts, great! But if you do the C/python interface by hand you will fall in the early optimization syndrome for sure.

If you want something with less interface code, you can think about creating a dll from your C code, and use that library directly from python with cstruct.

Consider also Cython if you want to use only python code in your program.

Answered By: Mapad

Using Cython is pretty good. You can write your C extension with a Python-like syntax and have it generate C code. Boilerplate included. Since you have the code already in python, you have to do just a few changes to your bottleneck code and C code will be generated from it.

Example. hello.pyx:

cdef int hello(int a, int b):
    return a + b

That generates 601 lines of boilerplate code:

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Answered By: nosklo

There be dragons here. Don’t swig, don’t boost. For any complicated project the code you have to fill in yourself to make them work becomes unmanageable quickly. If it’s a plain C API to your library (no classes), you can just use ctypes. It will be easy and painless, and you won’t have to spend hours trawling through the documentation for these labyrinthine wrapper projects trying to find the one tiny note about the feature you need.

Answered By: Jorenko

Since you are concerned with speed and overhead, I suggest considering PyBindGen .

I have experience using it to wrap a large internal C++ library. After trying SWIG, SIP, and Boost.Python I prefer PyBindGen for the following reasons:

  1. A PyBindGen wrapper is pure-Python, no need to learn another file format
  2. PyBindGen generates Python C API calls directly, there is no speed-robbing indirection layer like SWIG.
  3. The generated C code is clean and simple to understand. I like Cython too, but trying to read its C output can be difficult at times.
  4. STL sequence containers are supported (we use a lot of std::vector’s)
Answered By: sstock

An observation: Based on the benchmarking conducted by the pybindgen developers, there is no significant difference between boost.python and swig. I haven’t done my own benchmarking to verify how much of this depends on the proper use of the boost.python functionality.

Note also that there may be a reason that pybindgen seems to be in general quite a bit faster than swig and boost.python: it may not produce as versatile a binding as the other two. For instance, exception propagation, call argument type checking, etc. I haven’t had a chance to use pybindgen yet but I intend to.

Boost is in general quite big package to install, and last I saw you can’t just install boost python you pretty much need the whole Boost library. As others have mentioned compilation will be slow due to heavy use of template programming, which also means typically rather cryptic error messages at compile time.

Summary: given how easy SWIG is to install and use, that it generates decent binding that is robust and versatile, and that one interface file allows your C++ DLL to be available from several other languages like LUA, C#, and Java, I would favor it over boost.python. But unless you really need multi-language support I would take a close look at PyBindGen because of its purported speed, and pay close attention to robustness and versatility of binding it generates.

Answered By: schollii

SWIG 2.0.4 has introduced a new -builtin option that improves performance.
I did some benchmarking using an example program that does a lot of fast calls to a C++ extension.
I built the extension using boost.python, PyBindGen, SIP and SWIG with and without the -builtin option. Here are the results (average of 100 runs):

SWIG with -builtin     2.67s
SIP                    2.70s
PyBindGen              2.74s
boost.python           3.07s
SWIG without -builtin  4.65s

SWIG used to be slowest. With the new -builtin option, SWIG seems to be fastest.

Answered By: Johan Råde

There is an article worth reading on the topic Cython, pybind11, cffi – which tool should you choose?

Quick recap for the impatient:

  • Cython compiles your python to C/C++ allowing you to embed your C/C++ into python code. Uses static binding. For python programmers.

  • pybind11 (and boost.python) is the opposite. Bind your stuff at compile time from the C++ side. For C++ programmers.

  • CFFI allows you to bind the native stuff dynamically at runtime. Simple to use, but higher performance penalty.

Answered By: Thinkeye
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