[petsc-users] Euclid or Boomeramg vs ILU: questions.
Sanjay Govindjee
s_g at berkeley.edu
Fri Aug 20 13:32:13 CDT 2021
Mark's suggestion will definitely help a lot. Remove the displacement
bc equations or include them in the matrix by zeroing out the row and
putting a 1 on the diagonal. The Lagrange multiplier will cause grief.
On 8/20/21 11:21 AM, Mark Adams wrote:
> Constraints are a pain with scalable/iterative solvers. If you order
> the constraints last then ILU should work as well as it can work, but
> AMG gets confused by the constraint equations.
> You could look at PETSc's Stokes solvers, but it would be best if you
> could remove the constrained equations from your system if they are
> just simple point wise BC's.
> Mark
>
> On Fri, Aug 20, 2021 at 8:53 AM Наздрачёв Виктор
> <numbersixvs at gmail.com <mailto:numbersixvs at gmail.com>> wrote:
>
> *Hello, dear PETSc team!*
>
> I have a 3D elasticity with heterogeneous properties problem.
> There is unstructured grid with aspect ratio varied from 4 to 25.
> Dirichlet BCs (bottom zero displacements) are imposed via linear
> constraint equations using Lagrange multipliers. Also, Neumann
> (traction) BCs are imposed on side edges of mesh. Gravity load is
> also accounted for.
>
> I can solve this problem with *dgmres solver* and *ILU* as a
> *preconditioner*. But ILU doesn`t support parallel computing, so I
> decided to use Euclid or Boomeramg as a preconditioner. The issue
> is in slow convergence and high memory consumption, much higher,
> than for ILU.
>
> E.g., for source matrix size 2.14 GB with *ILU-0
> preconditioning* memory consumption is about 5.9 GB, and the
> process converges due to 767 iterations, and with *Euclid-0
> preconditioning* memory consumption is about 8.7 GB, and the
> process converges due to 1732 iterations.
>
> One of the following preconditioners is currently in use: *ILU-0,
> ILU-1, Hypre (Euclid), Hypre (boomeramg)*.
>
> As a result of computations */(logs and memory logs are
> attached)/*, the following is established for preconditioners:
>
> 1. *ILU-0*: does not always provide convergence (or provides, but
> slow); uses an acceptable amount of RAM; does not support parallel
> computing.
>
> 2. *ILU-1*: stable; memory consumption is much higher than that of
> ILU-0; does not support parallel computing.
>
> 3. *Euclid*: provides very slow convergence, calculations are
> performed several times slower than for ILU-0; memory consumption
> greatly exceeds both ILU-0 and ILU-1; supports parallel computing.
> Also “drop tolerance” doesn’t provide enough accuracy in some
> cells, so I don’t use it.
>
> 4. *Boomeramg*: provides very slow convergence, calculations are
> performed several times slower than for ILU-0; memory consumption
> greatly exceeds both ILU-0 and ILU-1; supports parallel computing.
>
> In this regard, the following questions arose:
>
> 1. Is this behavior expected for HYPRE in computations with 1 MPI
> process? If not, is that problem can be related to *PETSc* or *HYPRE*?
>
> 2. Hypre (Euclid) has much fewer parameters than ILU. Among them
> is the factorization level *"-pc_hypre_euclid_level <now -2:
> formerly -2>: Factorization levels (None)"* and its default value
> looks very strange, moreover, it doesn’t matter what factor is
> chosen -2, -1 or 0. Could it be that the parameter is confused
> with Column pivot tolerance in ILU - *"-pc_factor_column_pivot
> <-2.: -2.>: Column pivot tolerance (used only for some
> factorization) (PCFactorSetColumnPivot)"*?
>
> 3. What preconditioner would you recommend to: optimize
> *convergence*, *memory* consumption, add *parallel computing*?
>
> 4. How can we theoretically estimate memory costs with *ILU,
> Euclid, Boomeramg*?
>
> 5. At what stage are memory leaks most likely?
>
> In any case, thank you so much for your attention! Will be
> grateful for any response.
>
> Kind regards,
> Viktor Nazdrachev
> R&D senior researcher
> Geosteering Technologies LLC
>
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