Dear Numpy developers, I propose a pull request https://github.com/numpy/numpy/pull/7533 that features numpy arrays that can be shared among processes (with some effort).
Why: In CPython, multiprocessing is the only way of how to exploit multi-core CPUs if your parallel code can't avoid creating Python objects. In that case, CPython's GIL makes threads unusable. However, unlike with threading, sharing data among processes is something that is non-trivial and platform-dependent. Although numpy (and certainly some other packages) implement some operations in a way that GIL is not a concern, consider another case: You have a large amount of data in a form of a numpy array and you want to pass it to a function of an arbitrary Python module that also expects numpy array (e.g. list of vertices coordinates as an input and array of the corresponding polygon as an output). Here, it is clear GIL is an issue you and since you want a numpy array on both ends, now you would have to copy your numpy array to a multiprocessing.Array (to pass the data) and then to convert it back to ndarray in the worker process. This contribution would streamline it a bit - you would create an array as you are used to, pass it to the subprocess as you would do with the multiprocessing.Array, and the process can work with a numpy array right away. How: The idea is to create a numpy array in a buffer that can be shared among processes. Python has support for this in its standard library, so the current solution creates a multiprocessing.Array and then passes it as the "buffer" to the ndarray.__new__. That would be it on Unixes, but on Windows, there has to be a a custom pickle method, otherwise the array "forgets" that its buffer is that special and the sharing doesn't work. Some of what has been said in the pull request & my answer to that: * ... I do see some value in providing a canonical right way to construct shared memory arrays in NumPy, but I'm not very happy with this solution, ... terrible code organization (with the global variables): * I understand that, however this is a pattern of Python multiprocessing and everybody who wants to use the Pool and shared data either is familiar with this approach or has to become familiar with[2, 3]. The good compromise is to have a separate module for each parallel calculation, so global variables are not a problem. * Can you explain why the ndarray subclass is needed? Subclasses can be rather annoying to get right, and also for other reasons. * The shmarray class needs the custom pickler (but only on Windows). * If there's some way to we can paper over the boilerplate such that users can use it without understanding the arcana of multiprocessing, then yes, that would be great. But otherwise I'm not sure there's anything to be gained by putting it in a library rather than referring users to the examples on StackOverflow [1] [2]. * What about telling users: "You can use numpy with multiprocessing. Remeber the multiprocessing.Value and multiprocessing.Aray classes? numpy.shm works exactly the same way, which means that it shares their limitations. Refer to an example: <link to numpy doc>." Notice that although those SO links contain all of the information, it is very difficult to get it up and running for a newcomer like me few years ago. * This needs tests and justification for custom pickling methods, which are not used in any of the current examples. ... * I am sorry, but don't fully understand that point. The custom pickling method of shmarray has to be there on Windows, but users don't have to know about it at all. As noted earlier, the global variable is the only way of using standard Python multiprocessing.Pool with shared objects. [1]: http://stackoverflow.com/questions/10721915/shared-memory-objects-in-python-multiprocessing [2]: http://stackoverflow.com/questions/7894791/use-numpy-array-in-shared-memory-for-multiprocessing [3]: http://stackoverflow.com/questions/1675766/how-to-combine-pool-map-with-array-shared-memory-in-python-multiprocessing _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
