Hi Ian, Could you possible post your code or a version of the code that demonstrates the problem? Also, do you have the same issue with different solver suites?
Cheers, Daniel On Fri, Sep 30, 2016 at 12:41 PM, Campbell, Ian <i.campbel...@imperial.ac.uk > wrote: > Hi All, > > > > We are sweeping six PDEs in a time-stepping loop. We’ve noticed that as > CPU time progresses, the duration of each time-step increases, although the > sweep count remains constant. This is illustrated in the Excel file of data > logged from the simulation, which is available at the first hyperlink below. > > > > Hence, we suspected a memory leak may be occurring. After conducting > memory-focused line-profiling with the vprof tool, we observed a linear > increase in total memory consumption at a rate of approximately 3 MB per > timestep loop. This is evident in the graph at the second link below, which > illustrates the memory increase over three seconds of simulation. > > > > As a further step, we used Pympler to investigate the source of RAM > consumption increase for each timestep. The table below is an output from > Pympler’s SummaryTracker().print_diff(), which describe the additional > objects created within every time-step. Clearly, there are ~3.2 MB of > additional data being generated with every loop – this correlates perfectly > with the total rate of increase of memory consumption reported by vprof. > Although we are not yet sure, we suspect that the increasing time spent per > loop is the result of this apparent memory leak. > > > > We suspect this is the result of the calls to .sweep, since we are not > explicitly creating these objects. Can the origin of these objects be > traced, and furthermore, is there a way to avoid re-creating them and > consuming more memory with every loop? Without some method of unloading or > preventing this object build-up, it isn’t feasible to run our simulation > for long durations. > > dict > > 2684 > > 927.95 > > KB > > type > > 1716 > > 757.45 > > KB > > tuple > > 9504 > > 351.31 > > KB > > list > > 4781 > > 227.09 > > KB > > str > > 2582 > > 210.7 > > KB > > numpy.ndarray > > 396 > > 146.78 > > KB > > cell > > 3916 > > 107.08 > > KB > > property > > 2288 > > 98.31 > > KB > > weakref > > 2287 > > 98.27 > > KB > > function (getName) > > 1144 > > 67.03 > > KB > > function (getRank) > > 1144 > > 67.03 > > KB > > function (_calcValue_) > > 1144 > > 67.03 > > KB > > function (__init__) > > 1144 > > 67.03 > > KB > > function (_getRepresentation) > > 1012 > > 59.3 > > KB > > function (__setitem__) > > 572 > > 33.52 > > KB > > SUM > > 3285.88 > > KB > > > > > > https://imperialcollegelondon.box.com/s/zp9jj67du3mxdcfgbc4el8cqpxwnv0y4 > > > > https://imperialcollegelondon.box.com/s/ict9tnswqk9z57ovx8r3ll5po5ccrib9 > > > > With best regards, > > > > - Ian & Krishna > > > > P.S. Daniel, thank you very much for the excellent example solution you > provided in response to our question on obtaining the sharp discontinuity. > > > > Ian Campbell | PhD Candidate > > Electrochemical Science & Engineering Group > > Imperial College London, SW7 2AZ, United Kingdom > > > > _______________________________________________ > fipy mailing list > fipy@nist.gov > http://www.ctcms.nist.gov/fipy > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] > > -- Daniel Wheeler
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