How efficient is threading in Python?

Question

I heard threading is not very efficient in Python (compared to other languages).

Is this true? If so, how can a Python programmer overcome this?

Answer 1

CPython uses reference counting with a cyclic garbage collector for memory management. To make this practical, it has a mechanism called the "global interpreter lock" which protects the reference counting system, along with all the other interpreter internals.

On a single-core machine, this doesn't matter - all threading is faked via time-slicing, anyway. On a multiple core machine, it makes a difference: a CPU bound Python program running on CPython won't make use of all of the available cores.

There are a number of possible responses to this:

• use multiple processes instead of multiple threads (also opens up future scalability to multiple machines, rather than different cores within a single machine)
• use a Python implementation with a more multicore friendly garbage collection mechanism (such as Jython, IronPython or PyPy)
• move the more intensive CPU operations out to C code and release the GIL while doing the calculations (thus allowing the C code to run on other cores even though only a single Python thread is active at any one time)

If threads are being used to convert blocking IO to a non-blocking operation, then that works just fine in standard CPython without any special modifications - IO operations already release the GIL.