<br><div class="gmail_quote"><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><div class="gmail_quote"><div>You are right on your suspicion. I was making a clean run on each file. That is deleting everything except the sea files in the folders. With this configuration multiprocessing module's pooling approach doesn't work. It cannot branch into the external script completely. However when I leave the processed outputs in the folders and run the script it works and takes much less than IPython's parallelism. Not the question is how to explain this behaviour.<br>
<br>End of my 2.4 to 2.7X speed-up happiness :)<br><br></div></div></blockquote><div><br>I know this form experience. Every time I have had speedups that were too good to be true, there was always something hidden<br>that ended the pipe-dream.<br>
<br>But I just want to clarify a few points:<br><br>* What is the len of the iterable that you pass to the various parallel versions of map?<br><br>* About how long on average does it take to compute things for one element of the iterable?<br>
<br>Because I don't have the data, I can't tell these things from your scripts.<br><br>Cheers,<br><br>Brian<br></div></div>