Hi, I was trying to parallelize some algorithms and needed a writable array shared between processes. It turned out to be quite simple and gave a nice speed up almost linear in number of cores. Of course you need to know what you are doing to avoid segfaults and such. But I still think something like this should be included with NumPy for power users. This works by inheriting anonymous mmaped memory. Not sure if this works on windows. import numpy as np import multiprocessing as mp class shared(np.ndarray): """Shared writable array""" def __new__(subtype, shape, interface=None): size = np.prod(shape) if interface == None: buffer = mp.RawArray('d', size) self = np.ndarray.__new__(subtype, shape, float, buffer) else: class Dummy(object): pass buffer = Dummy() buffer.__array_interface__ = interface a = np.asarray(buffer) self = np.ndarray.__new__(subtype, shape=a.shape, buffer=a) return self def __reduce_ex__(self, protocol): return shared, (self.shape, self.__array_interface__) def __reduce__(self): return __reduce_ex__(self, 0) Also see attached file for example usage. Erik