[petsc-users] GAMG scalability for serendipity 20 nodes hexahedra

[Mark Adams]

You can get a plain aggregation (PA) method in GAMG with -pc_gamg_nsmooths
0, but Notay is a much different PA algorithm. It is much more involved
than what GAMG does. PA is not usually better for the Laplacian but it is
simple and cheap and can be good if smoothed aggregation is not working
well.

Also, I am finding (today) that -pc_gamg_square_graph 0 is faster
than -pc_gamg_square_graph 1, what I usually use, for Q2 elements.

On Thu, Jun 27, 2019 at 3:06 AM TARDIEU Nicolas <nicolas.tardieu at edf.fr>
wrote:

>
> Thank you very much for your answer, Mark.
> Do you think it is worth it, to play around with aggregation variants?
> Plain aggregation "à la Notay" for instance.
>
> Nicolas
>
>
>
>
>
>
> De : mfadams at lbl.gov <mfadams at lbl.gov>
> Envoyé : mercredi 26 juin 2019 22:37
> À : TARDIEU Nicolas
> Cc : PETSc users list
> Objet : Re: [petsc-users] GAMG scalability for serendipity 20 nodes
> hexahedra
>
>
> I get growth with Q2 elements also. I've never seen anyone report scaling
> of high order elements with generic AMG.
>
>
> First, discretizations are very important for AMG solver. All optimal
> solvers really. I've never looked at serendipity elements. It might be a
> good idea to try Q2 as well.
>
>
> SNES ex56 is 3D elasticity on a cube with tensor elements. Below are
> parameters that I have been using. I see some evidence that more smoothing
> steps (-mg_levels_ksp_max_it N) helps "scaling" but not necessarily solve
> time.
>
>
> An example of what I see, running ex56 with -cells 8,12,16  -max_conv_its
> 5 and the below params I get these iteration counts: 19, 20, 31, 31, 38.
>
>
> My guess is that you need higher order interpolation for higher order
> elements and when you add a new level you get an increase in condition
> number (ie, it is not an optimal MG method). But, the original smoothed
> aggregation paper did have high order discretizations  their theory said it
> was still optimal, as I recall.
>
>
> Mark
>
>
> -log_view
> -max_conv_its 5
> -petscspace_degree 2
> -snes_max_it 2
> -ksp_max_it 100
> -ksp_type cg
> -ksp_rtol 1.e-11
> -ksp_atol 1.e-71
> -ksp_norm_type unpreconditioned
> -snes_rtol 1.e-10
> -pc_type gamg
> -pc_gamg_type agg
> -pc_gamg_agg_nsmooths 1
> -pc_gamg_coarse_eq_limit 1000
> -pc_gamg_process_eq_limit 200
> -pc_gamg_reuse_interpolation true
> -ksp_converged_reason
> -snes_monitor_short
> -ksp_monitor_short
> -snes_converged_reason
> -use_mat_nearnullspace true
> -mg_levels_ksp_max_it 4
> -mg_levels_ksp_type chebyshev
> -mg_levels_esteig_ksp_type cg
> -gamg_est_ksp_type cg
> -gamg_est_ksp_max_it 10
> -mg_levels_esteig_ksp_max_it 10
> -mg_levels_ksp_chebyshev_esteig 0,0.05,0,1.05
> -mg_levels_pc_type jacobi
> -petscpartitioner_type simple
> -mat_block_size 3
> -matptap_via scalable
> -run_type 1
> -pc_gamg_repartition false
> -pc_gamg_threshold 0.0
> -pc_gamg_threshold_scale .25
> -pc_gamg_square_graph 1
> -check_pointer_intensity 0
> -snes_type ksponly
> -ex56_dm_view
> -options_left
>
>
>
>
>
>
>
> On Wed, Jun 26, 2019 at 8:21 AM TARDIEU Nicolas via petsc-users <
> petsc-users at mcs.anl.gov> wrote:
>
> Dear PETSc team,
>
>
> I have run a simple weak scalability test based on canonical 3D elasticity
> problem : a cube, meshed with 8 nodes hexaedra, clamped on one of its face
> and submited to a pressure load on the opposite face.
> I am using the FGMRES ksp with GAMG as preconditioner. I have set the
> rigid body modes using MatNullSpaceCreateRigidBody and its works like a
> charm. The solver exhibit a perfect scalability until 800 cores (I haven't
> tested with more cores). The ksp always  converges in 11 or 12 iterations.
> Let me emphasize that I use GAMG default options.
>
>
>
> Nevertheless, if I switch to a quadratic mesh with 20 nodes serendipity
> hexaedra, the weak scalability deteriorates. For instance the number of
> iteration for the ksp increases from 20 iterations for the smallest problem
> to 30 for the biggest.
> Here is my question : I wonder what is the right tuning for GAMG to
> recover the same weak scalability as in the linear case? I apologize if
> this is a stupid question...
>
>
>
>
>
>
> I  look forward to reading you,
> Nicolas
>
>
>
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