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    <p>Well this is kind of interesting note if you run the following commands:</p> <pre><code>import numpy from multiprocessing import Pool a = numpy.arange(1000000) pool = Pool(processes = 5) result = pool.map(numpy.sin, a) UnpicklingError: NEWOBJ class argument has NULL tp_new </code></pre> <p>wasn't expecting that, so whats going on, well:</p> <pre><code>&gt;&gt;&gt; help(numpy.sin) Help on ufunc object: sin = class ufunc(__builtin__.object) | Functions that operate element by element on whole arrays. | | To see the documentation for a specific ufunc, use np.info(). For | example, np.info(np.sin). Because ufuncs are written in C | (for speed) and linked into Python with NumPy's ufunc facility, | Python's help() function finds this page whenever help() is called | on a ufunc. </code></pre> <p>yep numpy.sin is implemented in c as such you can't really use it directly with multiprocessing.</p> <p>so we have to wrap it with another function</p> <p>perf:</p> <pre><code>import time import numpy from multiprocessing import Pool def numpy_sin(value): return numpy.sin(value) a = numpy.arange(1000000) pool = Pool(processes = 5) start = time.time() result = numpy.sin(a) end = time.time() print 'Singled threaded %f' % (end - start) start = time.time() result = pool.map(numpy_sin, a) pool.close() pool.join() end = time.time() print 'Multithreaded %f' % (end - start) $ python perf.py Singled threaded 0.032201 Multithreaded 10.550432 </code></pre> <p>wow, wasn't expecting that either, well theres a couple of issues for starters we are using a python function even if its just a wrapper vs a pure c function, and theres also the overhead of copying the values, multiprocessing by default doesn't share data, as such each value needs to be copy back/forth.</p> <p>do note that if properly segment our data:</p> <pre><code>import time import numpy from multiprocessing import Pool def numpy_sin(value): return numpy.sin(value) a = [numpy.arange(100000) for _ in xrange(10)] pool = Pool(processes = 5) start = time.time() result = numpy.sin(a) end = time.time() print 'Singled threaded %f' % (end - start) start = time.time() result = pool.map(numpy_sin, a) pool.close() pool.join() end = time.time() print 'Multithreaded %f' % (end - start) $ python perf.py Singled threaded 0.150192 Multithreaded 0.055083 </code></pre> <p>So what can we take from this, multiprocessing is great but we should always test and compare it sometimes its faster and sometimes its slower, depending how its used ...</p> <p>Granted you are not using <code>numpy.sin</code> but another function I would recommend you first verify that indeed multiprocessing will speed up the computation, maybe the overhead of copying values back/forth may affect you.</p> <p>Either way I also do <i>believe</i> that using <code>pool.map</code> is the best, safest method of multithreading code ...</p> <p>I hope this helps.</p>
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    1. POParallelizing a Numpy vector operation
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    1. COThanks a lot! This is very informative. I had assumed, based on what I read, that `Pool`'s `map()` function would work somewhat intelligently on the data, but I guess segmenting it first makes a huge difference. Is there any other way to avoid the overhead of the processes copying over the data? Do you expect any performance difference if I use `math.sin()` instead?
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    2. COI actually tried `math.sin` and well its a lot slower, even multithreaded then single threaded numpy.sin, though it was faster (took `6.435199`s than the multithreaded `numpy.sin` which took `10.5`, probably due to the fact that `numpy.sin` can handle arrays, the `numpy` guys are really good at math ;), yes there is a way using `shared memory` http://docs.python.org/library/multiprocessing.html but please don't use its quite dangerous and has limited support, or at the very least tread carefully.
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    3. COIf your only doing reads then it maybe safe, the subprocess only need to keep track of their corresponding index or subset of indices ...
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