tags:

views:

2635

answers:

8

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.

+2  A: 

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.

Robert Gould
+22  A: 

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++: 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.

  • 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 (yesterday), 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!
ascobol
thanks, swig has evolved in the last years. You just %include your .h file, and everything is done (another hour for unicode support and you are really done :-)My code works fine with swig - my question was if it is worth manually going through the generated .py code with all the pySwigObjects ...
RSabet
These objects add some overhead. Pybindgen can probably generate much cleaner C code for your module. I played a little bit with it and in fact it generated a code that was very close to what I would have done manually.
ascobol
+3  A: 

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.

Bluebird75
thanks, but those cannot compare to plain C/C++.
RSabet
I mention boost, which is pure C++, so your comment is inappropriate.
Bluebird75
I was refering to PyRex. Boost would just be a maybe more flexible alternative to swig. But what i would like to know if there is a significant performance gain, when hand writing the interface, instead of letting swig or boost create the code. Still thanks for the answer. ShedSkin was new to me.
RSabet
+8  A: 

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 Cpython if you want to use only python code in your program.

Mapad
you mistyped cython. If you are mindful of how cython works, it can be a very quick process to convert a python function to cython. I have had 30x speedups doing this for not much extra effort.
gnibbler
+8  A: 

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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/* Type declarations */
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static  int __pyx_f_10helloworld_hello(int __pyx_v_a, int __pyx_v_b) {
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#if PY_MAJOR_VERSION < 3
PyMODINIT_FUNC inithelloworld(void); /*proto*/
PyMODINIT_FUNC inithelloworld(void)
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PyMODINIT_FUNC PyInit_helloworld(void)
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        return val;
    }
    else {
        return __pyx_PyInt_AsLong(x);
    }
}

static INLINE long __pyx_PyInt_long(PyObject* x) {
    if (sizeof(long) < sizeof(long)) {
        long long_val = __pyx_PyInt_AsLong(x);
        long val = (long)long_val;
        if (unlikely((val != long_val) )) {
            PyErr_SetString(PyExc_OverflowError, "value too large to convert to long");
            return (long)-1;
        }
        return val;
    }
    else {
        return __pyx_PyInt_AsLong(x);
    }
}

static INLINE signed char __pyx_PyInt_signed_char(PyObject* x) {
    if (sizeof(signed char) < sizeof(long)) {
        long long_val = __pyx_PyInt_AsLong(x);
        signed char val = (signed char)long_val;
        if (unlikely((val != long_val) )) {
            PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed char");
            return (signed char)-1;
        }
        return val;
    }
    else {
        return __pyx_PyInt_AsLong(x);
    }
}

static INLINE signed short __pyx_PyInt_signed_short(PyObject* x) {
    if (sizeof(signed short) < sizeof(long)) {
        long long_val = __pyx_PyInt_AsLong(x);
        signed short val = (signed short)long_val;
        if (unlikely((val != long_val) )) {
            PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed short");
            return (signed short)-1;
        }
        return val;
    }
    else {
        return __pyx_PyInt_AsLong(x);
    }
}

static INLINE signed int __pyx_PyInt_signed_int(PyObject* x) {
    if (sizeof(signed int) < sizeof(long)) {
        long long_val = __pyx_PyInt_AsLong(x);
        signed int val = (signed int)long_val;
        if (unlikely((val != long_val) )) {
            PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed int");
            return (signed int)-1;
        }
        return val;
    }
    else {
        return __pyx_PyInt_AsLong(x);
    }
}

static INLINE signed long __pyx_PyInt_signed_long(PyObject* x) {
    if (sizeof(signed long) < sizeof(long)) {
        long long_val = __pyx_PyInt_AsLong(x);
        signed long val = (signed long)long_val;
        if (unlikely((val != long_val) )) {
            PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed long");
            return (signed long)-1;
        }
        return val;
    }
    else {
        return __pyx_PyInt_AsLong(x);
    }
}

static INLINE long double __pyx_PyInt_long_double(PyObject* x) {
    if (sizeof(long double) < sizeof(long)) {
        long long_val = __pyx_PyInt_AsLong(x);
        long double val = (long double)long_val;
        if (unlikely((val != long_val) )) {
            PyErr_SetString(PyExc_OverflowError, "value too large to convert to long double");
            return (long double)-1;
        }
        return val;
    }
    else {
        return __pyx_PyInt_AsLong(x);
    }
}
nosklo
+4  A: 

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.

Jorenko
+3  A: 

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)
sstock
+1  A: 

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.

schollii