python-guide/docs/scenarios/speed.rst

Speed
=====

CPython, the most commonly used implementation of Python, is slow for CPU bound
tasks. `PyPy`_ is fast.

Using a slightly modified version of `David Beazley's`_ CPU bound test code
(added loop for multiple tests), you can see the difference between CPython
and PyPy's processing.

.. code-block:: console

   # PyPy
   $ ./pypy -V
   Python 2.7.1 (7773f8fc4223, Nov 18 2011, 18:47:10)
   [PyPy 1.7.0 with GCC 4.4.3]
   $ ./pypy measure2.py
   0.0683999061584
   0.0483210086823
   0.0388588905334
   0.0440690517426
   0.0695300102234

.. code-block:: console

   # CPython
   $ ./python -V
   Python 2.7.1
   $ ./python measure2.py
   1.06774401665
   1.45412397385
   1.51485204697
   1.54693889618
   1.60109114647

Context
:::::::


The GIL
-------

`The GIL`_ (Global Interpreter Lock) is how Python allows multiple threads to
operate at the same time. Python's memory management isn't entirely thread-safe,
so the GIL is required to prevent multiple threads from running the same
Python code at once.

David Beazley has a great `guide`_ on how the GIL operates. He also covers the
`new GIL`_ in Python 3.2. His results show that maximizing performance in a
Python application requires a strong understanding of the GIL, how it affects
your specific application, how many cores you have, and where your application
bottlenecks are.

C Extensions
------------


The GIL
-------

`Special care`_ must be taken when writing C extensions to make sure you
register your threads with the interpreter.

C Extensions
::::::::::::


Cython
------

`Cython <http://cython.org/>`_ implements a superset of the Python language
with which you are able to write C and C++ modules for Python. Cython also
allows you to call functions from compiled C libraries. Using Cython allows
you to take advantage of Python's strong typing of variables and operations.

Here's an example of strong typing with Cython:

.. code-block:: cython

    def primes(int kmax):
    """Calculation of prime numbers with additional
    Cython keywords"""

        cdef int n, k, i
        cdef int p[1000]
        result = []
        if kmax > 1000:
            kmax = 1000
        k = 0
        n = 2
        while k < kmax:
            i = 0
            while i < k and n % p[i] != 0:
                i = i + 1
            if i == k:
                p[k] = n
                k = k + 1
                result.append(n)
            n = n + 1
        return result


This implementation of an algorithm to find prime numbers has some additional
keywords compared to the next one, which is implemented in pure Python:

.. code-block:: python

    def primes(kmax):
    """Calculation of prime numbers in standard Python syntax"""

        p= range(1000)
        result = []
        if kmax > 1000:
            kmax = 1000
        k = 0
        n = 2
        while k < kmax:
            i = 0
            while i < k and n % p[i] != 0:
                i = i + 1
            if i == k:
                p[k] = n
                k = k + 1
                result.append(n)
            n = n + 1
        return result

Notice that in the Cython version you declare integers and integer arrays
to be compiled into C types while also creating a Python list:


.. code-block:: cython

    def primes(int kmax):
        """Calculation of prime numbers with additional
        Cython keywords"""

        cdef int n, k, i
        cdef int p[1000]
        result = []


.. code-block:: python

    def primes(kmax):
        """Calculation of prime numbers in standard Python syntax"""

        p= range(1000)
        result = []

What is the difference? In the upper Cython version you can see the
declaration of the variable types and the integer array in a similar way as
in standard C. For example `cdef int n,k,i` in line 3. This additional type
declaration (i.e. integer) allows the Cython compiler to generate more
efficient C code from the second version. While standard Python code is saved
in :file:`*.py` files, Cython code is saved in :file:`*.pyx` files.

What's the difference in speed? Let's try it!

.. code-block:: python

	import time
	#activate pyx compiler
	import pyximport
	pyximport.install()
	#primes implemented with Cython
	import primesCy
	#primes implemented with Python
	import primes

	print "Cython:"
	t1= time.time()
	print primesCy.primes(500)
	t2= time.time()
	print "Cython time: %s" %(t2-t1)
	print ""
	print "Python"
	t1= time.time()
	print primes.primes(500)
	t2= time.time()
	print "Python time: %s" %(t2-t1)


These lines both need a remark:

.. code-block:: python

    import pyximport
    pyximport.install()


The `pyximport` module allows you to import :file:`*.pyx` files (e.g.,
:file:`primesCy.pyx`) with the Cython-compiled version of the `primes`
function. The `pyximport.install()` command allows the Python interpreter to
start the Cython compiler directly to generate C-code, which is automatically
compiled to a :file:`*.so` C-library. Cython is then able to import this
library for you in your Python code, easily and efficiently. With the
`time.time()` function you are able to compare the time between these 2
different calls to find 500 prime numbers. On a standard notebook (dual core
AMD E-450 1.6 GHz), the measured values are:

.. code-block:: console

    Cython time: 0.0054 seconds

    Python time: 0.0566 seconds


And here the output of an embedded `ARM beaglebone <http://beagleboard.org/Products/BeagleBone>`_ machine:

.. code-block:: console

    Cython time: 0.0196 seconds

    Python time: 0.3302 seconds


Pyrex
-----


Shedskin?
---------

Numba
-----
.. todo:: Write about Numba and the autojit compiler for NumPy

Threading
:::::::::


Threading
---------


Spawning Processes
------------------


Multiprocessing
---------------


.. _`PyPy`: http://pypy.org
.. _`The GIL`: http://wiki.python.org/moin/GlobalInterpreterLock
.. _`guide`: http://www.dabeaz.com/python/UnderstandingGIL.pdf
.. _`New GIL`: http://www.dabeaz.com/python/NewGIL.pdf
.. _`Special care`: http://docs.python.org/c-api/init.html#threads
.. _`David Beazley's`: http://www.dabeaz.com/GIL/gilvis/measure2.py