Shared memory arrays for NumPy
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README.txt

Shared memory arrays for NumPy and Multiprocessing

To build .pyd files:

> python setup.py build_ext
> python setup.py install

Usage:

> import sharedmem as shm
> array = shm.zeros((m,n), dtype=float)

These arrays can be passed to multiprocessing.Queue and are pickled
by the name of the segment rather than the contents of the buffer.
As pickle is slow, the intention is to save memory, not provide faster 
IPC than making a copy of the NumPy array would do. If memory is not 
an issue, just use normal NumPy arrays instead.



Warning about shared memory:

As always when using shared memory, beware of 'false sharing'. If you
don't know what that is, chances are that NOT USING SHARED MEMORY will
give you better performance. It is also for this reason that C programs
using multiple processes (e.g. MPI or fork) tend to perform better than 
programs using multithreading (e.g. OpenMP or pthreads).

http://en.wikipedia.org/wiki/False_sharing

Shared memory segments are also readable files in the file system 
(under /tmp on Linux). This might be a security issue on some systems.



Copyright (c) 2009, 2011, 2012, Sturla Molden
All rights reserved.

Redistribution and use in source and binary forms, with or without modification, 
are permitted provided that the following conditions are met:

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    this list of conditions and the following disclaimer in the documentation 
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