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PO
primarykey
Id
6652658
data
AcceptedAnswerId
0
AnswerCount
0
ClosedDate
CommentCount
0
CommunityOwnedDate
CreationDate
2011-07-11T15:36:28.507
FavoriteCount
0
LastActivityDate
2011-07-20T11:17:21.833
LastEditDate
2011-07-20T11:17:21.833
LastEditorUserId
367273
OwnerUserId
367273
ParentId
6652124
PostTypeId
2
Score
9
ViewCount
0
LastEditorDisplayName
text
Body
From my experience, multi-threading is probably not going to be a viable option for speeding things up (due to the <a href="http://en.wikipedia.org/wiki/Global_Interpreter_Lock" rel="noreferrer">Global Interpreter Lock</a>). A good alternative is the <a href="http://docs.python.org/library/multiprocessing.html" rel="noreferrer"><code>multiprocessing</code></a> module. This may or may not work well, depending on how much data you end up having to pass around from one process to another. Another good alternative would be to consider using <a href="http://numpy.scipy.org/" rel="noreferrer"><code>numpy</code></a> for your computations (if you aren't already). If you can vectorize your code, you should be able to achieve significant speedups even on a single core. Depending on what exactly you're doing and on your build of <code>numpy</code>, it might even be able to transparently distribute the computations across multiple cores. edit Here is a complete example of using the <code>multiprocessing</code> module to perform a simple computation. It uses four processes to compute the squares of the numbers from zero to nine. <pre><code>from multiprocessing import Pool def f(x): return x*x if __name__ == '__main__': pool = Pool(processes=4) # start 4 worker processes inputs = range(10) result = pool.map(f, inputs) print result </code></pre> This is meant as a simple illustration. Given the trivial nature of <code>f()</code>, this parallel version will almost certainly be slower than computing the same thing serially.
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1. PONaive and easiest way to decompose independent loop into parallel threads/processes
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1. PONaive and easiest way to decompose independent loop into parallel threads/processes
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