CPython, the most commonly used implementation of Python, is slow for CPU bound tasks. PyPy is fast.
Using a slightly modified version of David Beazleys CPU bound test code (added loop for multiple tests), you can see the difference between CPython and PyPy’s processing.
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
CPython
$ ./python -V
Python 2.7.1
$ ./python measure2.py
1.06774401665
1.45412397385
1.51485204697
1.54693889618
1.60109114647
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 requried to prevents 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.
Special care must be taken when writing C extensions to make sure you r egister your threads with the interpreter.