[ Thanks to An Anonymous Reader for
this link. ]
“In a previous article for IBM® developerWorks, I
demonstrated a simple and effective pattern for implementing
threaded programming in Python. One downside of this approach,
though, is that it won’t always speed up your application, because
the GIL (global interpreter lock) effectively limits threads to one
core. If you need to use all of the cores on your machine, then
typically you will need to fork processes, to increase speed.
Dealing with a flock of processes can be a challenge, because if
communication between processes is needed, it can often get
complicated to coordinate all of the calls.“Fortunately, as of version 2.6, Python includes a module called
“multiprocessing” to help you deal with processes. The API of the
processing module has some similarities to the way the threading
API works, but there are also few differences to keep in mind. One
of the main differences is that processes have subtle underlying
behavior that a high-level API will never be able to completely
abstract away. You can read more about this in the official
documentation for the multiprocessing module (see the Resources
section).”