<div dir="ltr"><div><div><div><div><div>If you look in the file _differentialevolution.py, there is a comment<br><br># do the optimisation.<br><br></div>at the top of the per-generation loop. It is inside this loop that each generation is processed. As processing of each generation progresses within this loop, the population ia modified - that is, mutation is using a population that changes while the generation is running.<br><br></div>To parallelize this loop, the creation of the trial items (crossover) must be independent of modifying the changes to the population from which the new trials are drawn.<br><br></div>So it looks like this loop must be run seriallyin order to modify the population in-place, and cannot be parallelized to yield identical results between serial and parallel versions. Based on my experience, it will have quite superior performance compared to a version that is made suitable for parallelization but run serially.<br><br></div>James<br></div><br></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Mar 25, 2015 at 4:16 AM, James Phillips <span dir="ltr"><<a href="mailto:zunzun@zunzun.com" target="_blank">zunzun@zunzun.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div>I just had a cup of coffee and downloaded the 0.15.1 source, investigating now.<span class="HOEnZb"><font color="#888888"><br><br></font></span></div><span class="HOEnZb"><font color="#888888">James<br></font></span></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Mar 25, 2015 at 3:50 AM, Andrew Nelson <span dir="ltr"><<a href="mailto:andyfaff@gmail.com" target="_blank">andyfaff@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><p dir="ltr">Scipy 0.15 now has a (serial) DE implementation. I'd be interested to now how the two implementations compare in terms of total number of function evaluations. There are a few papers out there that discuss parallel DE, but I can't remember where they were. I'd be interested to know how easy it is to parallelise function evaluations in scipy. I'm guessing cython and openmp might be a way to go. It's a pity that clang doesn't have openmp.</p>
<div class="gmail_quote"><div><div>On 25/03/2015 6:00 AM, "James Phillips" <<a href="mailto:zunzun@zunzun.com" target="_blank">zunzun@zunzun.com</a>> wrote:<br type="attribution"></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div><div dir="ltr"><div><div><div><div><div><div>I have been using Robert Kern's implementation of the Differential Evolution (DE) genetic algorithm for a decade, for the purpose of guessimating initial parameter estimates for curve fitting and surface fitting in my oprn source pyeq2 fitting library.<br><br></div>I can't find anything that works better, which gives rise to my current problem.<br><br></div>In trying to improve performance of my fitting library, I tried to use GPU calculations for each generation of the genetic algorithm, I found the following:<br><br></div>1) Robert's 2005 implementation of DE is not parallelizeable, as each crossover withing a generation can affect the population from which new items will be created. That is, within a given generation the population *changes* as the algorithm runs the generation itself, and it must run serially in it's present form.<br><br></div>2) I can rework the algorithm to be parallelizeable by separating out "crossover", "breeding" and "evolving" into three separate steps, but months of testing show that population size and number of generations must beconsiderably increased to match the results from Robert's version. That is, making the algorithm parallelizable means slowing it down so I can speed it up!<br><br></div>I would like to increase performance, but cannot find any way to equal Robert's results without reducing performance prior to parallelization.<br><br>Any suggestions?<br><br></div>James<br></div>
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