Sorry, I forgot to add the download link for the matrix files: https://transfer.pcloud.com/download.html?code=5ZViHIZI96yPIODHYSZ7y1HZMloBfcyhAHunjQVMpWUJIykLt76k
Thanks On Sat, Apr 1, 2017 at 12:01 AM Toon Weyens <[email protected]> wrote: > Dear jose, > > I have saved the matrices in Matlab format and am sending them to you > using pCloud. If you want another format, please tell me. Please also note > that they are about 1.4GB each. > > I also attach a typical output of eps_view and log_view in output.txt, for > 8 processes. > > Thanks so much for helping me out! I think Petsc and Slepc are amazing > inventions that really have saved me many months of work! > > Regards > > On Fri, Mar 31, 2017 at 5:12 PM Jose E. Roman <[email protected]> wrote: > > In order to answer about GD I would need to know all the settings you are > using. Also if you could send me the matrix I could do some tests. > GD and JD are preconditioned eigensolvers, which need a reasonably good > preconditioner. But MUMPS is a direct solver, not a preconditioner, and > that is often counterproductive in this kind of methods. > Jose > > > > El 31 mar 2017, a las 16:45, Toon Weyens <[email protected]> > escribió: > > > > Dear both, > > > > I have recompiled slepc and petsc without debugging, as well as with the > recommended --with-fortran-kernels=1. In the attachment I show the scaling > for a typical "large" simulation with about 120 000 unkowns, using > Krylov-Schur. > > > > There are two sets of datapoints there, as I do two EPS solves in one > simulations. The second solve is faster as it results from a grid > refinement of the first solve, and takes the solution of the first solve as > a first, good guess. Note that there are two pages in the PDF and in the > second page I show the time · n_procs. > > > > As you can see, the scaling is better than before, especially up to 8 > processes (which means about 15,000 unknowns per process, which is, as I > recall, cited as a good minimum on the website. > > > > I am currently trying to run make streams NPMAX=8, but the cluster is > extraordinarily crowded today and it does not like my interactive jobs. I > will try to run them asap. > > > > The main issue now, however, is again the first issue: the Generalizeid > Davidson method does not converge to the physically correct negative > eigenvalue (it should be about -0.05 as Krylov-Schur gives me). In stead it > stays stuck at some small positive eigenvalue of about +0.0002. It looks as > if the solver really does not like passing the eigenvalue = 0 barrier, a > behavior I also see in smaller simulations, where the convergence is > greatly slowed down when crossing this. > > > > However, this time, for this big simulation, just increasing NCV does > not do the trick, at least not until NCV=2048. > > > > Also, I tried to use target magnitude without success either. > > > > I started implementing the capability to start with Krylov-Schur and > then switch to GD with EPSSetInitialSpace when a certain precision has been > reached, but then realized it might be a bit of overkill as the SLEPC > solution phase in my code is generally not more than 15% of the time. There > are probably other places where I can gain more than a few percents. > > > > However, if there is another trick that can make GD to work, it would > certainly be appreciated, as in my experience it is really about 5 times > faster than Krylov-Schur! > > > > Thanks! > > > > Toon > >
