On Mon, Jul 20, 2015 at 11:14 AM, [email protected] < [email protected]> wrote:
> I am roughly guessing that my sparse matrix will have 1000 non-zeros on > each row. As the matrix I symmetric I divide by half: > > > > 1000*1000000(rows)*8(bytes)*2(complex numbers)/2 = 8GB > 1) You have missed the memory to represent the columns, so multiply by 1.5 2) Are you using a symmetric format? 3) This is only the space to represent the matrix, not its factors. Matt > > However, it turns out to be much less: I create the matrices in another > program and the binary file holding my matrix K is only 800MB. M is a > lumped mass matrix… > > > > By the way, I tried using MUMPS and have the same problem there. > > > > Mahir > > > > > > *From:* Matthew Knepley [mailto:[email protected]] > *Sent:* den 20 juli 2015 18:05 > *To:* Ülker-Kaustell, Mahir > *Cc:* Hong; petsc-users > *Subject:* Re: [petsc-users] SuperLU MPI-problem > > > > On Mon, Jul 20, 2015 at 10:59 AM, [email protected] < > [email protected]> wrote: > > Hong: > > > > Previous experiences with this equation have shown that it is very > difficult to solve it iteratively. Hence the use of a direct solver. > > > > The large test problem I am trying to solve has slightly less than 10^6 > degrees of freedom. The matrices are derived from finite elements so they > are sparse. > > > > Estimated how? It is very difficult to estimate flll-in. > > > > Matt > > > > The machine I am working on has 128GB ram. I have estimated the memory > needed to less than 20GB, so if the solver needs twice or even three times > as much, it should still work well. Or have I completely misunderstood > something here? > > > > Mahir > > > > > > > > *From:* Hong [mailto:[email protected]] > *Sent:* den 20 juli 2015 17:39 > *To:* Ülker-Kaustell, Mahir > *Cc:* petsc-users > *Subject:* Re: [petsc-users] SuperLU MPI-problem > > > > Mahir: > > Direct solvers consume large amount of memory. Suggest to try followings: > > > > 1. A sparse iterative solver if [-omega^2M + K] is not too > ill-conditioned. You may test it using the small matrix. > > > > 2. Incrementally increase your matrix sizes. Try different matrix > orderings. > > Do you get memory crash in the 1st symbolic factorization? > > In your case, matrix data structure stays same when omega changes, so you > only need to do one matrix symbolic factorization and reuse it. > > > > 3. Use a machine that gives larger memory. > > > > Hong > > > > Dear Petsc-Users, > > > > I am trying to use PETSc to solve a set of linear equations arising from > Naviers equation (elastodynamics) in the frequency domain. > > The frequency dependency of the problem requires that the system > > > > [-omega^2M + K]u = F > > > > where M and K are constant, square, positive definite matrices (mass and > stiffness respectively) is solved for each frequency omega of interest. > > K is a complex matrix, including material damping. > > > > I have written a PETSc program which solves this problem for a small (1000 > degrees of freedom) test problem on one or several processors, but it keeps > crashing when I try it on my full scale (in the order of 10^6 degrees of > freedom) problem. > > > > The program crashes at KSPSetUp() and from what I can see in the error > messages, it appears as if it consumes too much memory. > > > > I would guess that similar problems have occurred in this mail-list, so I > am hoping that someone can push me in the right direction… > > > > Mahir > > > > > > > > > > > > > > > > -- > > What most experimenters take for granted before they begin their > experiments is infinitely more interesting than any results to which their > experiments lead. > -- Norbert Wiener > -- What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead. -- Norbert Wiener
