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    <p>You may be interested in a tiny piece of code I wrote: <a href="https://github.com/vmlaker/benchmark-sharedmem" rel="noreferrer">github.com/vmlaker/benchmark-sharedmem</a></p> <p>The only file of interest is <code>main.py</code>. It's a benchmark of <a href="https://bitbucket.org/cleemesser/numpy-sharedmem" rel="noreferrer">numpy-sharedmem</a> -- the code simply passes arrays (either <code>numpy</code> or <code>sharedmem</code>) to spawned processes, via Pipe. The workers just call <code>sum()</code> on the data. I was only interested in comparing the data communication times between the two implementations.</p> <p>I also wrote another, more complex code: <a href="https://github.com/vmlaker/sherlock" rel="noreferrer">github.com/vmlaker/sherlock</a>.</p> <p>Here I use the <a href="https://bitbucket.org/cleemesser/numpy-sharedmem" rel="noreferrer">numpy-sharedmem</a> module for real-time image processing with OpenCV -- the images are NumPy arrays, as per OpenCV's newer <code>cv2</code> API. The images, actually references thereof, are shared between processes via the dictionary object created from <a href="http://docs.python.org/2/library/multiprocessing.html#multiprocessing-managers" rel="noreferrer"><code>multiprocessing.Manager</code></a> (as opposed to using Queue or Pipe.) I'm getting great performance improvements when compared with using plain NumPy arrays.</p> <p><strong>Pipe vs. Queue</strong>:</p> <p>In my experience, IPC with Pipe is faster than Queue. And that makes sense, since Queue adds locking to make it safe for multiple producers/consumers. Pipe doesn't. But if you only have two processes talking back-and-forth, it's safe to use Pipe, or, as the docs read: </p> <blockquote> <p>... there is no risk of corruption from processes using different ends of the pipe at the same time.</p> </blockquote> <p><strong><code>sharedmem</code> safety</strong>:</p> <p>The main issue with <code>sharedmem</code> module is the possibility of memory leak upon ungraceful program exit. This is described in a lengthy discussion <a href="http://grokbase.com/t/python/python-list/1144s75ps4/multiprocessing-shared-memory-vs-pickled-copies" rel="noreferrer">here</a>. Although on Apr 10, 2011 Sturla mentions a fix to memory leak, I have still experienced leaks since then, using both repos, Sturla Molden's own on GitHub (<a href="https://github.com/sturlamolden/sharedmem-numpy" rel="noreferrer">github.com/sturlamolden/sharedmem-numpy</a>) and Chris Lee-Messer's on Bitbucket (<a href="https://bitbucket.org/cleemesser/numpy-sharedmem" rel="noreferrer">bitbucket.org/cleemesser/numpy-sharedmem</a>).</p>
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    1. POShare Large, Read-Only Numpy Array Between Multiprocessing Processes
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    1. USVelimir Mlaker
    1. USVelimir Mlaker
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    1. COThanks, very very informative. The memory leak in `sharedmem` sounds like a big deal, though. Any leads on solving that?
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    2. COBeyond just noticing the leaks, I haven't looked for it in the code. I added to my answer, under "sharedmem safety" above, keepers of the two open source repos of the ``sharedmem`` module, for reference.
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