Hi Carsten, Have you looked at:
https://www.ctcms.nist.gov/fipy/documentation/EFFICIENCY.html http://nbviewer.jupyter.org/github/wd15/fipy-efficiency/blob/master/notebooks/FiPy-IPython.ipynb https://www.mail-archive.com/fipy@nist.gov/msg03180.html thread I struggled to install weave and get --inline working. I didn't use it much yet, but my initial impression was the speedup was modest. Remeshing seems like a great possibility for making problems such as examples.phase.anisotropy run faster (use higher mesh density at the interface), but I am afraid that FiPy architecture (lazy evaluation etc) could interfere. I hope you share what you find with remeshing. Thanks On Tue, Jul 24, 2018 at 10:56 AM Carsten Langrock <langr...@stanford.edu> wrote: > Thanks for pointing out the performance order of the solvers. I’ll try to > get pySparse to work to compare it other solvers. It’s also good to know > that I shouldn’t give up on 2D just yet;-) > > Regards, > Carsten > > _____________________________________ > *Dipl.-Phys. Carsten Langrock, Ph.D.* > > Senior Research Scientist > Edward L. Ginzton Laboratory, Rm. 202 > Stanford University > > 348 Via Pueblo Mall > 94305 Stanford, CA > > Tel. (650) 723-0464 > Fax (650) 723-2666 > > Ginzton Lab Shipping Address: > James and Anna Marie Spilker Engineering and Applied Sciences Building > 04-040 > 348 Via Pueblo Mall > 94305 Stanford, CA > _____________________________________ > > On Jul 24, 2018, at 6:11 AM, Guyer, Jonathan E. Dr. (Fed) < > jonathan.gu...@nist.gov> wrote: > > FiPy still does not support remeshing. > > As Dario said, choice of solver can make a big difference. I've not used > PyAMG much, but PySparse is dramatically faster than SciPy. PyTrilinos is > slower than PySparse, but enables you to solve in parallel. > > I've also found that 2D problems solve much better than the 1D performance > would lead you to believe. There's just a lot of overhead in setting up the > problem and the Python communication with the lower-level libraries. > > > On Jul 23, 2018, at 6:44 PM, Carsten Langrock <langr...@stanford.edu> > wrote: > > Hi, > > Thanks for the help with getting FiPy running under Linux! I am trying to > re-create a 1D nonlinear diffusion problem for which we have C++ code that > uses the implicit Thomas algorithm based on > > J. Weickert, B. Romerny, M. Viergever, "Efficient and Reliable Schemes > for Nonlinear Diffusion Filtering”, IEEE transactions on Image Processing, > vol.7, N03, page 398, March 1998 > > I have been able to get results in FiPy that match this code very closely > which was a great start. Our C++ code uses a fixed number of spatial points > and a fixed time step, but re-meshes space to most efficiently use the size > of the array; it increases the spatial step size by 2 whenever the > concentration at a particular point reaches a set threshold. I tried > implementing this in FiPy as well, but haven’t had much luck so far. I saw > an old mailing-list entry from 2011 where a user was told that FiPy wasn’t > meant to do remeshing. Is that still the case? > > I’d imagine one would somehow need to update the Grid1D object with the > new ‘dx’, but since the CellVariable that holds the solution was > initialized with that mesh object, I am not sure that such a change would > propagate in a sensible fashion. I think I know how to map the value of the > CellVariable to account for the change in ‘dx’ by > > array_size = 2000 > phi.value = numpy.concatenate((phi.value[1:array_size/2:2], > numpy.zeros(1500))) > > for the case when the initial variable holds 2000 spatial points. Maybe > there’s a more elegant way, but I think this works in principle. > > Another question would be execution speed. Right now, even when not > plotting the intermediate solutions, it takes many seconds on a very > powerful computer to run a simple diffusion problem. I am probably doing > something really wrong. I wasn’t expecting the code to perform as well as > the C++ code, but I had hoped to come within an order of magnitude. Are > there ways to optimize the performance? Maybe select a particularly clever > solver? If someone could point me into the right direction that’d be great. > In the end, I would like to expand the code into 2D, but given the poor 1D > performance, I don’t think that this would be feasible at this point. > > Thanks, > Carsten > > _____________________________________ > Dipl.-Phys. Carsten Langrock, Ph.D. > > Senior Research Scientist > Edward L. Ginzton Laboratory, Rm. 202 > Stanford University > > 348 Via Pueblo Mall > 94305 Stanford, CA > > Tel. (650) 723-0464 > Fax (650) 723-2666 > > Ginzton Lab Shipping Address: > James and Anna Marie Spilker Engineering and Applied Sciences Building > 04-040 > 348 Via Pueblo Mall > 94305 Stanford, CA > _____________________________________ > _______________________________________________ > fipy mailing list > fipy@nist.gov > http://www.ctcms.nist.gov/fipy > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] > > > > > _______________________________________________ > fipy mailing list > fipy@nist.gov > http://www.ctcms.nist.gov/fipy > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] > > _______________________________________________ > fipy mailing list > fipy@nist.gov > http://www.ctcms.nist.gov/fipy > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] >
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