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Python thread parallelization escaping the GIL

I'm rephrasing my question because I think many thought it was the question "does python have threads". It does, but CPython also has the GIL, which will never schedule more than one thread at any given time. That makes CPython threads useless for cpu-intensive computations.

I need to use threads; process parallelism won't work for me because of the IPC costs (I have large shared objects).

I'm currently using Jython (no GIL) with JyNI so that I can use numpy. JyNI is alpha, but it does now support numpy. I got this to work. However, JyNI is alpha and buggy, and the whole process is slow.

I've read a bunch of old threads. I wonder whether there has been a viable option since then? I'm forced to use python 2.7.

Thanks.

Solution

  • At the moment, Jython is still considerably slower than CPython. Depending on the program and how well the JIT can optimize it, multithreading might or might not pay off. Jython's primary design goal is compatibility, before performance. It is mainly intended for glue code and there is still a lot of potential for efficiency improvements. See e.g. zippy for a blazingly fast Python implementation in Java, however it is experimental and lacks Jython's compatibility level. In a way this represents the opposite design goal.

    Now adding JyNI to Jython does not exactly make it faster, but so far I found that performace optimization in JyNI would be premature and usually the Jython part dominates the runtime anyway. Also, e.g. for NumPy the native numerics workload vastly dominates the glue code cost.

    Finally, note that JyNI must emulate a GIL on C side. For details have a look at the paper https://arxiv.org/abs/1607.00825. Maybe it will be possible to operate certain extensions without a GIL - it depends on implementation detail, how sensitive an extension is to that. Currently the C-side GIL is mandatory. That's why you might not benefit from Java multithreading when using NumPy. C-extensions have the option to explicitly release the GIL e.g. during computationally intense operations that don't interact with the interpreter. I don't know if NumPy makes use of this.

    JyNI is alpha and buggy

    Please make sure to report bugs at the issue tracker.