[petsc-users] PETSc (3.9.0) GAMG weak scaling test issue
"Alberto F. Martín"
amartin at cimne.upc.edu
Thu Nov 8 05:41:24 CST 2018
Dear Mark,
thanks for your quick and comprehensive reply.
Before moving to the results of the experiments that u suggested, let me
clarify two points
on my original e-mail and your answer:
(1) The raw timings and #iters. provided in my first e-mail were actually
obtained with "-pc_gamg_square_graph 1" (and not 0); sorry about
that, my mistake.
(the logs, though, were consistent with the solver configuration
provided).
The raw figures with "-pc_gamg_square_graph 0" are actually as
follows:
(load3): [0.25074561, 0.3650926566, 0.6251466936, 0.8709517661,
15.52180776]
(load3): [0.148803731, 0.325266364, 0.5538515123, 0.7537377281,
1.475100923]
(load3): [8, 9, 11, 12, 12]
Bottom line: significant improvement of absolute times for the
first 4x problems, marginal improvement for
the largest problem (compared to
"-pc_gamg_square_graph 1")
(2) <</The PC setup times are large (I see 48 seconds at 16K but you
report 16). //
// -pc_gamg_square_graph 10 should help that./>>
This disagreement is justified by the following note on my
original e-mail:
<</Please note that within each run, I execute these two
stages up-to//
// three times, and this influences absolute timings given
in -log_view./>>
I tried new configurations based on your suggestions. Find attached the
results.
(legends indicate changes with respect to the solver configuration provided
in my first e-mail).
Bottom lines: (1) the configuration provided in my original e-mail leads
to fastest execution
and less number of iteration for the first 4x problems. (2) *The (new)
parameter-value combinations**
**suggested seem to have almost no impact into the preconditioner set up
time of the last problem.**
*I also tried HYPRE-BoomerAMG as suggested, with two different
configurations.
*** SYMMETRIC CONFIGURATION ***
-ksp_type cg
-ksp_monitor
-ksp_rtol 1.0e-6
-ksp_converged_reason
-ksp_max_it 500
-ksp_norm_type unpreconditioned
-ksp_view
-log_view
-pc_type hypre
-pc_hypre_type boomeramg
-pc_hypre_boomeramg_print_statistics 1
-pc_hypre_boomeramg_strong_threshold 0.25
-pc_hypre_boomeramg_coarsen_type HMIS
-pc_hypre_boomeramg_relax_type_down symmetric-SOR/Jacobi
-pc_hypre_boomeramg_relax_type_up symmetric-SOR/Jacobi
-pc_hypre_boomeramg_relax_type_coarse Gaussian-elimination
*** UNSYMMETRIC CONFIGURATION ***
-ksp_type gmres
-ksp_gmres_restart 500
-ksp_monitor
-ksp_rtol 1.0e-6
-ksp_converged_reason
-ksp_max_it 500
-ksp_pc_side right
-ksp_norm_type unpreconditioned
-pc_type hypre
-pc_hypre_type boomeramg
-pc_hypre_boomeramg_print_statistics 1
-pc_hypre_boomeramg_strong_threshold 0.25
-pc_hypre_boomeramg_coarsen_type HMIS
-pc_hypre_boomeramg_relax_type_down SOR/Jacobi
-pc_hypre_boomeramg_relax_type_up SOR/Jacobi
-pc_hypre_boomeramg_relax_type_coarse Gaussian-elimination
The raw results were:
*** SYMMETRIC CONFIGURATION ***
(load3): [0.1828534687, 0.3055133289, 0.3582984209, 0.4280304033,
1.343549139]
(load3): [0.2102472978, 0.4572948301, 0.7153297188, 0.9989531627, N/A]
(load3): [19, 23, 26, 28, 'DIVERGED_INDEFINITE_PC']
*** UNSYMMETRIC CONFIGURATION ***
(load3): [0.1841227429, 0.3082743008, 0.3652294828, 0.4654760892,
1.331299786]
(load3): [0.1194557019, 0.2830136018, 0.5046830242, 1.363314636, N/A]
(load3): [15, 19, 24, 48, DIVERGED_ITS]
Thus, the largest problem also seems to cause (even more severe) issues
to HYPRE, in particular,
INDEFINITE PRECONDITIONER with CG, and not convergence within 500
iterations for GMRES.
The preconditioner set up stage time, though, scales reasonably well
with the same data distribution
that we used to feed GAMG (although the preconditioner computed for the
largest problem seems to be
totally useless).
I have logs for all these results if required.
Thanks for your help!
Best regards,
Alberto.
On 07/11/18 19:46, Mark Adams wrote:
> First I would add -gamg_est_ksp_type cg
>
> You seem to be converging well so I assume you are setting the null
> space for GAMG.
>
> Note, you should test hypre also.
>
> You probably want a bigger "-pc_gamg_process_eq_limit 50". 200 at
> least but you test your machine with a range on the largest problem.
> This is a parameter for reducing the number of active processors (on
> coarse grids).
>
> I would only worry about "load3". This has 16K equations per process,
> which is where you start noticing "strong scaling" problems, depending
> on the machine.
>
> An important parameter is "-pc_gamg_square_graph 0". I would probably
> start with infinity (eg, 10).
>
> Now, I'm not sure about your domain, problem sizes, and thus the weak
> scaling design. You seem to be scaling on the background mesh, but
> that may not be a good proxy for complexity.
>
> You can look at the number of flops and scale it appropriately by the
> number of solver iterations to get a relative size of the problem. I
> would recommend scaling the number of processors with this. For
> instance here the MatMult line for the 4 proc and 16K proc run:
>
> ------------------------------------------------------------------------------------------------------------------------
> Event Count Time (sec) Flop --- Global --- --- Stage ---
> Total
> Max Ratio Max Ratio Max Ratio Mess Avg len Reduct %T %F
> %M %L %R %T %F %M %L %R Mflop/s
> ------------------------------------------------------------------------------------------------------------------------
> MatMult 636 1.0 1.9035e-01 1.0 3.12e+08 1.1 7.6e+03 3.0e+03 0.0e+00 0
> 47 62 44 0 0 47 62 44 0 6275 [2 procs]
> MatMult 1416 1.0 1.9601e+002744.6 4.82e+08 0.0 4.3e+08 7.2e+02
> 0.0e+00 0 48 50 48 0 0 48 50 48 0 2757975 [16K procs]
>
> Now, you have empty processors. See the massive load imbalance on time
> and the zero on Flops. The "Ratio" is max/min and cleary min=0 so
> PETSc reports a ratio of 0 (it is infinity really).
>
> Also, weak scaling on a thin body (I don't know your domain) is a
> little funny because as the problem scales up the mesh becomes more 3D
> and this causes the cost per equation to go up. That is why I prefer
> to use the number of non-zeros as the processor scaling function but
> number of equations is easier ...
>
> The PC setup times are large (I see 48 seconds at 16K bu you report
> 16). -pc_gamg_square_graph 10 should help that.
>
> The max number of flops per processor in MatMult goes up by 50% and
> the max time goes up by 10x and the number of iterations goes up by
> 13/8. If I put all of this together I get that 75% of the time at 16K
> is in communication at 16K. I think that and the absolute time can be
> improved some by optimizing parameters as I've suggested.
>
> Mark
>
>
>
>
>
> On Wed, Nov 7, 2018 at 11:03 AM "Alberto F. Martín" via petsc-users
> <petsc-users at mcs.anl.gov <mailto:petsc-users at mcs.anl.gov>> wrote:
>
> Dear All,
>
> we are performing a weak scaling test of the PETSc (v3.9.0) GAMG
> preconditioner when applied to the linear system arising
> from the *conforming unfitted FE discretization *(using Q1
> Lagrangian FEs) of a 3D PDE Poisson problem, where
> the boundary of the domain (a popcorn flake) is described as a
> zero-level-set embedded within a uniform background
> (Cartesian-like) hexahedral mesh. Details underlying the FEM
> formulation can be made available on demand if you
> believe that this might be helpful, but let me just point out that
> it is designed such that it addresses the well-known
> ill-conditioning issues of unfitted FE discretizations due to the
> small cut cell problem.
>
> The weak scaling test is set up as follows. We start from a single
> cube background mesh, and refine it uniformly several
> steps, until we have approximately either 10**3 (load1), 20**3
> (load2), or 40**3 (load3) hexahedra/MPI task when
> distributing it over 4 MPI tasks. The benchmark is scaled such
> that the next larger scale problem to be tested is obtained
> by uniformly refining the mesh from the previous scale and running
> it on 8x times the number of MPI tasks that we used
> in the previous scale. As a result, we obtain three weak scaling
> curves for each of the three fixed loads per MPI task
> above, on the following total number of MPI tasks: 4, 32, 262,
> 2097, 16777. The underlying mesh is not partitioned among
> MPI tasks using ParMETIS (unstructured multilevel graph
> partitioning) nor optimally by hand, but following the so-called
> z-shape space-filling curves provided by an underlying octree-like
> mesh handler (i.e., p4est library).
>
> I configured the preconditioned linear solver as follows:
>
> -ksp_type cg
> -ksp_monitor
> -ksp_rtol 1.0e-6
> -ksp_converged_reason
> -ksp_max_it 500
> -ksp_norm_type unpreconditioned
> -ksp_view
> -log_view
>
> -pc_type gamg
> -pc_gamg_type agg
> -mg_levels_esteig_ksp_type cg
> -mg_coarse_sub_pc_type cholesky
> -mg_coarse_sub_pc_factor_mat_ordering_type nd
> -pc_gamg_process_eq_limit 50
> -pc_gamg_square_graph 0
> -pc_gamg_agg_nsmooths 1
>
> Raw timings (in seconds) of the preconditioner set up and PCG
> iterative solution stage, and number of iterations are as follows:
>
> **preconditioner set up**
> (load1): [0.02542160451, 0.05169247743, 0.09266782179,
> 0.2426272957, 13.64161944]
> (load2): [0.1239175797 , 0.1885528499 , 0.2719282564 ,
> 0.4783878336, 13.37947339]
> (load3): [0.6565349903 , 0.9435049873 , 1.299908397 ,
> 1.916243652 , 16.02904088]
>
> **PCG stage**
> (load1): [0.003287350759, 0.008163803257, 0.03565631993,
> 0.08343045413, 0.6937994603]
> (load2): [0.0205939794 , 0.03594723623 , 0.07593298424,
> 0.1212046621 , 0.6780373845]
> (load3): [0.1310882876 , 0.3214917686 , 0.5532023879 ,
> 0.766881627 , 1.485446003]
>
> **number of PCG iterations**
> (load1): [5, 8, 11, 13, 13]
> (load2): [7, 10, 12, 13, 13]
> (load3): [8, 10, 12, 13, 13]
>
> It can be observed that both the number of linear solver
> iterations and the PCG stage timings (weakly)
> scale remarkably, but t*here is a significant time increase when
> scaling the problem from 2097 to 16777 MPI tasks **
> **for the preconditioner setup stage* (e.g., 1.916243652 vs
> 16.02904088 sec. with 40**3 cells per MPI task).
> I gathered the combined output of -ksp_view and -log_view (only)
> for all the points involving the load3 weak scaling
> test (find them attached to this message). Please note that within
> each run, I execute the these two stages up-to
> three times, and this influences absolute timings given in -log_view.
>
> Looking at the output of -log_view, it is very strange to me,
> e.g., that the stage labelled as "Graph"
> does not scale properly as it is just a call to MatDuplicate if
> the block size of the matrix is 1 (our case), and
> I guess that it is just a local operation that does not require
> any communication.
> What I am missing here? The load does not seem to be unbalanced
> looking at the "Ratio" column.
>
> I wonder whether the observed behaviour is as expected, or this a
> miss-configuration of the solver from our side.
> I played (quite a lot) with several parameter-value combinations,
> and the configuration above is the one that led to fastest
> execution (from the ones tested, that might be incomplete, I can
> also provide further feedback if helpful).
> Any feedback that we can get from your experience in order to find
> the cause(s) of this issue and a mitigating solution
> will be of high added value.
>
> Thanks very much in advance!
> Best regards,
> Alberto.
>
> --
> Alberto F. Martín-Huertas
> Senior Researcher, PhD. Computational Science
> Centre Internacional de Mètodes Numèrics a l'Enginyeria (CIMNE)
> Parc Mediterrani de la Tecnologia, UPC
> Esteve Terradas 5, Building C3, Office 215,
> 08860 Castelldefels (Barcelona, Spain)
> Tel.: (+34) 9341 34223
> e-mail:amartin at cimne.upc.edu <mailto:e-mail:amartin at cimne.upc.edu>
>
> FEMPAR project co-founder
> web:http://www.fempar.org
>
> ________________
> IMPORTANT NOTICE
> All personal data contained on this mail will be processed confidentially and registered in a file property of CIMNE in
> order to manage corporate communications. You may exercise the rights of access, rectification, erasure and object by
> letter sent to Ed. C1 Campus Norte UPC. Gran Capitán s/n Barcelona.
>
--
Alberto F. Martín-Huertas
Senior Researcher, PhD. Computational Science
Centre Internacional de Mètodes Numèrics a l'Enginyeria (CIMNE)
Parc Mediterrani de la Tecnologia, UPC
Esteve Terradas 5, Building C3, Office 215,
08860 Castelldefels (Barcelona, Spain)
Tel.: (+34) 9341 34223
e-mail:amartin at cimne.upc.edu
FEMPAR project co-founder
web: http://www.fempar.org
________________
IMPORTANT NOTICE
All personal data contained on this mail will be processed confidentially and registered in a file property of CIMNE in
order to manage corporate communications. You may exercise the rights of access, rectification, erasure and object by
letter sent to Ed. C1 Campus Norte UPC. Gran Capitán s/n Barcelona.
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(A) ** Added NearNullSpace to matrix (i.e. the constant vector) **
(load3): [0.2512322953, 0.3657070249, 0.6209384622, 0.8898622398, 16.37409958]
(load3): [0.1474562958, 0.3245896269, 0.551462595 , 0.7768286369, 1.563904478]
(load3): [8, 9, 11, 12, 12]
(B) ** (A) + -gamg_est_ksp_type cg**
(load3): [0.2532081502, 0.3669248847, 0.6215682998, 0.9122101571, 15.82921874]
(load3): [0.1476225629, 0.3242742592, 0.5494060389, 0.793106758, 1.541510889]
(load3): [8, 9, 11, 12, 12]
(C) ** (B) + -pc_gamg_square_graph 10**
(load3): [0.7063658834, 1.045530763, 1.403756126, 1.903321964, 16.91176975]
(load3): [0.1308690757, 0.3190896986, 0.5635806862, 0.790503782, 1.528392129]
(load3): [8, 10, 12, 14, 15]
(D) ** (C) + -pc_gamg_process_eq_limit 200**
(load3): [0.7066891911, 1.041900044, 1.438325046, 2.154289208, 15.54656001]
(load3): [0.1325668963, 0.3205731977, 0.5486685866, 0.8334027417, 1.485407834]
(load3): [8, 10, 12, 14, 15]
(E) ** (C) + -pc_gamg_process_eq_limit 500**
(load3): [0.7349723065, 1.084142983, 1.562717193, 2.198781526, 16.83547859]
(load3): [0.1336050248, 0.3177526584, 0.5764533961, 0.8126104074, 1.661861523]
(load3): [8, 10, 12, 14, 15]
(F) ** (C) + -pc_gamg_process_eq_limit 1000**
(3, 'a0b0c0d0e0f0g0h0i0'): [0.739308523, 1.117045472, 1.54470065, 2.845281176, 16.66935678]
(3, 'a0b0c0d0e0f0g0h0i0'): [0.1373377964, 0.3255409142, 0.5619245535, 0.8124665194, 1.660140919]
(3, 'a0b0c0d0e0f0g0h0i0'): [8, 10, 12, 13, 15]
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