Early Binding for Speed

Note

This page uses two different syntax variants:

  • Cython specific cdef syntax, which was designed to make type declarations concise and easily readable from a C/C++ perspective.

  • Pure Python syntax which allows static Cython type declarations in pure Python code, following PEP-484 type hints and PEP 526 variable annotations.

    To make use of C data types in Python syntax, you need to import the special cython module in the Python module that you want to compile, e.g.

    import cython
    

    If you use the pure Python syntax we strongly recommend you use a recent Cython 3 release, since significant improvements have been made here compared to the 0.29.x releases.

As a dynamic language, Python encourages a programming style of considering classes and objects in terms of their methods and attributes, more than where they fit into the class hierarchy.

This can make Python a very relaxed and comfortable language for rapid development, but with a price - the ‘red tape’ of managing data types is dumped onto the interpreter. At run time, the interpreter does a lot of work searching namespaces, fetching attributes and parsing argument and keyword tuples. This run-time ‘late binding’ is a major cause of Python’s relative slowness compared to ‘early binding’ languages such as C++.

However with Cython it is possible to gain significant speed-ups through the use of ‘early binding’ programming techniques.

For example, consider the following (silly) code example:

@cython.cclass
class Rectangle:
    x0: cython.int
    y0: cython.int
    x1: cython.int
    y1: cython.int

    def __init__(self, x0: cython.int, y0: cython.int, x1: cython.int, y1: cython.int):
        self.x0 = x0
        self.y0 = y0
        self.x1 = x1
        self.y1 = y1

    def area(self):
        area = (self.x1 - self.x0) * (self.y1 - self.y0)
        if area < 0:
            area = -area
        return area

def rectArea(x0, y0, x1, y1):
    rect = Rectangle(x0, y0, x1, y1)
    return rect.area()

In the rectArea() method, the call to rect.area() and the area() method contain a lot of Python overhead.

However, in Cython, it is possible to eliminate a lot of this overhead in cases where calls occur within Cython code. For example:

@cython.cclass
class Rectangle:
    x0: cython.int
    y0: cython.int
    x1: cython.int
    y1: cython.int

    def __init__(self, x0: cython.int, y0: cython.int, x1: cython.int, y1: cython.int):
        self.x0 = x0
        self.y0 = y0
        self.x1 = x1
        self.y1 = y1

    @cython.cfunc
    def _area(self) -> cython.int:
        area: cython.int = (self.x1 - self.x0) * (self.y1 - self.y0)
        if area < 0:
            area = -area
        return area

    def area(self):
        return self._area()

def rectArea(x0, y0, x1, y1):
    rect: Rectangle = Rectangle(x0, y0, x1, y1)
    return rect._area()

Here, in the Rectangle extension class, we have defined two different area calculation methods, the efficient _area() C method, and the Python-callable area() method which serves as a thin wrapper around _area(). Note also in the function rectArea() how we ‘early bind’ by declaring the local variable rect which is explicitly given the type Rectangle. By using this declaration, instead of just dynamically assigning to rect, we gain the ability to access the much more efficient C-callable _area() method.

But Cython offers us more simplicity again, by allowing us to declare dual-access methods - methods that can be efficiently called at C level, but can also be accessed from pure Python code at the cost of the Python access overheads. Consider this code:

@cython.cclass
class Rectangle:
    x0: cython.int
    y0: cython.int
    x1: cython.int
    y1: cython.int

    def __init__(self, x0: cython.int, y0: cython.int, x1: cython.int, y1: cython.int):
        self.x0 = x0
        self.y0 = y0
        self.x1 = x1
        self.y1 = y1

    @cython.ccall
    def area(self)-> cython.int:
        area: cython.int = (self.x1 - self.x0) * (self.y1 - self.y0)
        if area < 0:
            area = -area
        return area

def rectArea(x0, y0, x1, y1):
    rect: Rectangle = Rectangle(x0, y0, x1, y1)
    return rect.area()

Here, we just have a single area method, declared as cpdef or with @ccall decorator to make it efficiently callable as a C function, but still accessible from pure Python (or late-binding Cython) code.

If within Cython code, we have a variable already ‘early-bound’ (ie, declared explicitly as type Rectangle, (or cast to type Rectangle), then invoking its area method will use the efficient C code path and skip the Python overhead. But if in Cython or regular Python code we have a regular object variable storing a Rectangle object, then invoking the area method will require:

  • an attribute lookup for the area method

  • packing a tuple for arguments and a dict for keywords (both empty in this case)

  • using the Python API to call the method

and within the area method itself:

  • parsing the tuple and keywords

  • executing the calculation code

  • converting the result to a python object and returning it

So within Cython, it is possible to achieve massive optimisations by using strong typing in declaration and casting of variables. For tight loops which use method calls, and where these methods are pure C, the difference can be huge.