Using the multiprocessing module to parallelize tasks

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import multiprocessing

def fib(n):
    """computing the Fibonacci in an inefficient way
    was chosen to slow down the CPU."""
    if n <= 2:
        return 1
    else:
        return fib(n-1)+fib(n-2) 
p = multiprocessing.Pool() 
print(p.map(fib,[38,37,36,35,34,33]))

# Out: [39088169, 24157817, 14930352, 9227465, 5702887, 3524578]

As the execution of each call to fib happens in parallel, the time of execution of the full example is 1.8× faster than if done in a sequential way on a dual processor.

Python 2.2+

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Parallel computation:
* Using the multiprocessing module to parallelize tasks

Table Of Contents
2 Filter
3 List
7 Loops
22 Reduce
27 Classes
31 Set
33 Parallel computation
42 Tuple
45 Enum
62 Sockets
89 urllib
92 Idioms
104 Stack
105 Profiling
109 Logging
111 os module
118 Mixins
120 ArcPy
126 Arrays
132 2to3 tool
135 Unicode
138 Neo4j
140 Curses
141 Templates
145 heapq
146 tkinter
154 Audio
155 pyglet
157 ijson
160 Flask
161 Groupby
163 pygame
165 hashlib
166 Gzip
167 ctypes
185 pyaudio
186 shelve