When I did my testing and made those graphs, I ran Trilinos in serial. Syrupy didn't seem to track the other processes memory. I watched in real time as the parallel version ate all my ram though.
To make the program run longer while not changing the memory: steps = 100 # increase this, (limits the number of self-consistent iterations) accuracy = 10.0**-5 # make this number smaller, (relative energy eigenvalue change for being considered converged ) initial_solver_iterations_per_step = 7 # reduce this to 1, (number of solver iterations per self-consistent iteration, to small and it's slow, to high and the solutions are not stable) I did those tests on a machine with 128 GB of ram so I wasn't expecting any swapping. Thanks, -mike On 3/29/16 3:38 PM, Guyer, Jonathan E. Dr. (Fed) wrote: > I guess I spoke too soon. FWIW, I'm running Trilinos version: 11.10.2. > > > On Mar 29, 2016, at 3:34 PM, Guyer, Jonathan E. Dr. (Fed) > <[email protected]> wrote: > >> I'm not seeing a leak. The below is for trilinos. VSIZE grows to about 11 >> MiB and saturates and RSS saturates at around 5 MiB. VSIZE is more relevant >> for tracking leaks, as RSS is deeply tied to your system's swapping >> architecture and what else is running; either way, neither seems to be >> leaking, but this problem does use a lot of memory. >> >> What do I need to do to get it to run longer? >> >> >> >> On Mar 25, 2016, at 7:16 PM, Michael Waters <[email protected]> wrote: >> >>> Hello, >>> >>> I still have a large memory leak when using Trilinos. I am not sure where >>> to start looking so I made an example code that produces my problem in >>> hopes that someone can help me. >>> >>> But! my example is cool. I implemented Density Functional Theory in FiPy! >>> >>> My code is slow, but runs in parallel and is simple (relative to most DFT >>> codes). The example I have attached is just a lithium and hydrogen atom. >>> The electrostatic boundary conditions are goofy but work well enough for >>> demonstration purposes. If you set use_trilinos to True, the code will >>> slowly use more memory. If not, it will try to use Pysparse. >>> >>> Thanks, >>> -Michael Waters >>> <input.xyz><fipy-dft.py>_______________________________________________ >>> fipy mailing list >>> [email protected] >>> http://www.ctcms.nist.gov/fipy >>> [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] >> >> <attachment.png> >> _______________________________________________ >> fipy mailing list >> [email protected] >> http://www.ctcms.nist.gov/fipy >> [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] > > _______________________________________________ > fipy mailing list > [email protected] > http://www.ctcms.nist.gov/fipy > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] _______________________________________________ fipy mailing list [email protected] http://www.ctcms.nist.gov/fipy [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
