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    <p>Yes, because of the Global Interpreter Lock (GIL) there can only run one thread at a time. Here are some links with some insights about this:</p> <ul> <li><a href="http://www.artima.com/weblogs/viewpost.jsp?thread=214235" rel="noreferrer">http://www.artima.com/weblogs/viewpost.jsp?thread=214235</a></li> <li><a href="http://smoothspan.wordpress.com/2007/09/14/guido-is-right-to-leave-the-gil-in-python-not-for-multicore-but-for-utility-computing/" rel="noreferrer">http://smoothspan.wordpress.com/2007/09/14/guido-is-right-to-leave-the-gil-in-python-not-for-multicore-but-for-utility-computing/</a></li> </ul> <p>From the last link an interesting quote:</p> <blockquote> <p>Let me explain what all that means. Threads run inside the same virtual machine, and hence run on the same physical machine. Processes can run on the same physical machine or in another physical machine. If you architect your application around threads, you’ve done nothing to access multiple machines. So, you can scale to as many cores are on the single machine (which will be quite a few over time), but to really reach web scales, you’ll need to solve the multiple machine problem anyway.</p> </blockquote> <p>If you want to use multi core, <a href="http://www.python.org/dev/peps/pep-0371/" rel="noreferrer">pyprocessing</a> defines an process based API to do real parallelization. The <a href="http://en.wikipedia.org/wiki/Python_Enhancement_Proposal#Development" rel="noreferrer">PEP</a> also includes some interesting benchmarks.</p>
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    1. POHow do threads work in Python, and what are common Python-threading specific pitfalls?
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    1. USPeter Hoffmann
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    1. COReally a comment on the smoothspan quote: surely Python threading effectively limits you to one core, even if the machine has several? There may be benefits from multicore as the next thread can be ready to go without a context switch, but your Python threads can never make use of >1 core at a time.
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      1. USJames Brady
    2. COCorrect, python threads are practically limited to the one core, UNLESS a C module interacts nicely with the GIL, and runs it's own native thread.
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      1. USArafangion
    3. COActually, multiple cores make threads _less_ efficient as there's a lot of churn with checking if each thread can access the GIL. Even wit the new GIL, performance is still worse... http://www.dabeaz.com/python/NewGIL.pdf
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