[petsc-users] Multigrid

Tue May 1 17:21:15 CDT 2012

ps: I tried it with and without the non-zero initial guess to check the results, BTW.

----- Original Message -----
From: "Karthik Duraisamy" <dkarthik at stanford.edu>
To: "PETSc users list" <petsc-users at mcs.anl.gov>
Sent: Tuesday, May 1, 2012 3:18:34 PM
Subject: Re: [petsc-users] Multigrid

Hello,

   Sorry (and thanks for the reply). I've attached the no multigrid case. I didn't include it because (at least to the untrained eye, everything looks the same).

Regards,
Karthik

KSP Object: 8 MPI processes
  type: gmres
    GMRES: restart=100, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement
    GMRES: happy breakdown tolerance 1e-30
  maximum iterations=1
  using preconditioner applied to right hand side for initial guess
  tolerances:  relative=1e-05, absolute=1e-50, divergence=1e+10
  left preconditioning
  using nonzero initial guess
  using PRECONDITIONED norm type for convergence test
PC Object: 8 MPI processes
  type: bjacobi
    block Jacobi: number of blocks = 8
    Local solve is same for all blocks, in the following KSP and PC objects:
  KSP Object:  (sub_)   1 MPI processes  
    type: preonly
    maximum iterations=10000, initial guess is zero
    tolerances:  relative=1e-05, absolute=1e-50, divergence=10000
    left preconditioning
    using NONE norm type for convergence test
  PC Object:  (sub_)   1 MPI processes  
    type: ilu
      ILU: out-of-place factorization
      0 levels of fill
      tolerance for zero pivot 1e-12
      using diagonal shift to prevent zero pivot
      matrix ordering: natural
      factor fill ratio given 1, needed 1
        Factored matrix follows:
          Matrix Object:           1 MPI processes          
            type: seqaij
            rows=9015, cols=9015
            package used to perform factorization: petsc
            total: nonzeros=517777, allocated nonzeros=517777
            total number of mallocs used during MatSetValues calls =0
              using I-node routines: found 3476 nodes, limit used is 5
    linear system matrix = precond matrix:
    Matrix Object:     1 MPI processes    
      type: seqaij
      rows=9015, cols=9015
      total: nonzeros=517777, allocated nonzeros=517777
      total number of mallocs used during MatSetValues calls =0
        using I-node routines: found 3476 nodes, limit used is 5
  linear system matrix = precond matrix:
  Matrix Object:   8 MPI processes  
    type: mpiaij
    rows=75000, cols=75000
    total: nonzeros=4427800, allocated nonzeros=4427800
    total number of mallocs used during MatSetValues calls =0
      using I-node (on process 0) routines: found 3476 nodes, limit used is 5

----- Original Message -----
From: "Matthew Knepley" <knepley at gmail.com>
To: "PETSc users list" <petsc-users at mcs.anl.gov>
Sent: Tuesday, May 1, 2012 3:15:14 PM
Subject: Re: [petsc-users] Multigrid

On Tue, May 1, 2012 at 6:12 PM, Karthik Duraisamy < dkarthik at stanford.edu > wrote: 

Hello Barry, 

Thank you for your super quick response. I have attached the output of ksp_view and it is practically the same as that when I don't use PCMG. The part I don't understand is how PCMG able to function at the zero grid level and still produce a much better convergence than when using the default PC. Is there any additional smoothing or interpolation going on? 

You only included one output, so I have no way of knowing what you used before. However, this is running GMRES/ILU. 

Also, for Algebraic Multigrid, would you recommend BoomerAMG or ML ? 

They are different algorithms. Its not possible to say generally that one is better. Try them both. 

Matt 

Best regards, 
Karthik. 

type: mg 
MG: type is MULTIPLICATIVE, levels=1 cycles=v 
Cycles per PCApply=1 
Not using Galerkin computed coarse grid matrices 
Coarse grid solver -- level ------------------------------- 
KSP Object: (mg_levels_0_) 8 MPI processes 
type: gmres 
GMRES: restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement 
GMRES: happy breakdown tolerance 1e-30 
maximum iterations=1, initial guess is zero 
tolerances: relative=1e-05, absolute=1e-50, divergence=10000 
left preconditioning 
using PRECONDITIONED norm type for convergence test 
PC Object: (mg_levels_0_) 8 MPI processes 
type: bjacobi 
block Jacobi: number of blocks = 8 
Local solve is same for all blocks, in the following KSP and PC objects: 
KSP Object: (mg_levels_0_sub_) 1 MPI processes 
type: preonly 
maximum iterations=10000, initial guess is zero 
tolerances: relative=1e-05, absolute=1e-50, divergence=10000 
left preconditioning 
using NONE norm type for convergence test 
PC Object: (mg_levels_0_sub_) 1 MPI processes 
type: ilu 
ILU: out-of-place factorization 
0 levels of fill 
tolerance for zero pivot 1e-12 
using diagonal shift to prevent zero pivot 
matrix ordering: natural 
factor fill ratio given 1, needed 1 
Factored matrix follows: 
Matrix Object: 1 MPI processes 
type: seqaij 
rows=9015, cols=9015 
package used to perform factorization: petsc 
total: nonzeros=517777, allocated nonzeros=517777 
total number of mallocs used during MatSetValues calls =0 
using I-node routines: found 3476 nodes, limit used is 5 
linear system matrix = precond matrix: 
Matrix Object: 1 MPI processes 
type: seqaij 
rows=9015, cols=9015 
total: nonzeros=517777, allocated nonzeros=517777 
total number of mallocs used during MatSetValues calls =0 
using I-node routines: found 3476 nodes, limit used is 5 
linear system matrix = precond matrix: 
Matrix Object: 8 MPI processes 
type: mpiaij 
rows=75000, cols=75000 
total: nonzeros=4427800, allocated nonzeros=4427800 
total number of mallocs used during MatSetValues calls =0 
using I-node (on process 0) routines: found 3476 nodes, limit used is 5 
linear system matrix = precond matrix: 
Matrix Object: 8 MPI processes 
type: mpiaij 
rows=75000, cols=75000 
total: nonzeros=4427800, allocated nonzeros=4427800 
total number of mallocs used during MatSetValues calls =0 
using I-node (on process 0) routines: found 3476 nodes, limit used is 5 

----- Original Message ----- 
From: "Barry Smith" < bsmith at mcs.anl.gov > 
To: "PETSc users list" < petsc-users at mcs.anl.gov > 
Sent: Tuesday, May 1, 2012 1:39:26 PM 
Subject: Re: [petsc-users] Multigrid 

On May 1, 2012, at 3:37 PM, Karthik Duraisamy wrote: 

> Hello, 
> 
> I have been using PETSc for a couple of years with good success, but lately as my linear problems have become stiffer (condition numbers of the order of 1.e20), I am looking to use better preconditioners. I tried using PCMG with all the default options (i.e., I just specified my preconditioner as PCMG and did not add any options to it) and I am immediately seeing better convergence. 
> 
> What I am not sure of is why? I would like to know more about the default parameters (the manual is not very explicit) and more importantly, want to know why it is working even when I haven't specified any grid levels and coarse grid operators. Any 
> help in this regard will be appreciated. 

First run with -ksp_view to see what solver it is actually using. 

Barry 

> 
> Also, ultimately I want to use algebraic multigrid so is PCML a better option than BoomerAMG? I tried BoomerAMG with mixed results. 
> 
> Thanks, 
> Karthik 
> 
> 
> 
> -- 
> 
> ======================================= 
> Karthik Duraisamy 
> Assistant Professor (Consulting) 
> Durand Building Rm 357 
> Dept of Aeronautics and Astronautics 
> Stanford University 
> Stanford CA 94305 
> 
> Phone: 650-721-2835 
> Web: www.stanford.edu/~dkarthik 
> ======================================= 

-- 
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 