Python Global Interpreter Lock (GIL) workaround on multi-core systems using taskset on Linux?

Another solution is:http://docs.python.org/library/multiprocessing.html

Note 1: This is not a limitation of the Python language, but of CPython implementation.

Note 2: With regard to affinity, your OS shouldn't have a problem doing that itself.

I have never heard of anyone using taskset for a performance gain with Python. Doesn't mean it can't happen in your case, but definitely publish your results so others can critique your benchmarking methods and provide validation.

Personally though, I would decouple your I/O threads from the CPU bound threads using a message queue. That way your front end is now completely network I/O bound (some with HTTP interface, some with message queue interface) and ideal for your threading situation. Then the CPU intense processes can either use multiprocessing or just be individual processes waiting for work to arrive on the message queue.

In the longer term you might also want to consider replacing your threaded I/O front-end with Twisted or some thing like eventlets because, even if they won't help performance they should improve scalability. Your back-end is now already scalable because you can run your message queue over any number of machines+cpus as needed.

An interesting solution is the experiment reported by Ryan Kelly on his blog: http://www.rfk.id.au/blog/entry/a-gil-adventure-threading2/