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 : [email protected] <[email protected]>
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
<[email protected]> 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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