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POIs python automagically parallelizing IO- and CPU- or memory-bound sections?
 

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    <p>This is a follow-up questions on a <a href="https://stackoverflow.com/questions/849058/is-it-possible-to-speed-up-python-io">previous one</a>.</p> <p>Consider this code, which is less toyish than the one in the <a href="https://stackoverflow.com/questions/849058/is-it-possible-to-speed-up-python-io">previous question</a> (but still much simpler than my real one)</p> <pre><code>import sys data=[] for line in open(sys.argv[1]): data.append(line[-1]) print data[-1] </code></pre> <p>Now, I was expecting a longer run time (my benchmark file is 65150224 lines long), possibly much longer. This was not the case, it runs in ~ 2 minutes on the same hw as before!</p> <p>Is it data.append() very lightweight? I don't believe so, thus I wrote this fake code to test it:</p> <pre><code>data=[] counter=0 string="a\n" for counter in xrange(65150224): data.append(string[-1]) print data[-1] </code></pre> <p>This runs in 1.5 to 3 minutes (there is strong variability among runs)</p> <p>Why don't I get 3.5 to 5 minutes in the former program? Obviously data.append() is happening in parallel with the IO. </p> <p>This is good news! </p> <p>But how does it work? Is it a documented feature? Is there any requirement on my code that I should follow to make it works as much as possible (besides load-balancing IO and memory/CPU activities)? Or is it just plain buffering/caching in action?</p> <p>Again, I tagged "linux" this question, because I'm interested only in linux-specific answers. Feel free to give OS-agnostic, or even other-OS answers, if you think it's worth doing.</p>
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    1. COThis is different, but still worth reading: http://stackoverflow.com/questions/172720/speeding-up-python
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