[petsc-users] KSP on GPU

Tue Nov 1 10:42:08 CDT 2022

Thanks for the tips!

The suggested settings for GAMG did not yield better results,
but hypre worked well right away, giving very good convergence!

A follow-up question then (I hope that's ok; and it could be related to GAMG
not working, I'll check that). Once everything was running I discovered that my gradient vector
dfdx which I populate via an array df obtained from VecGetArray(dfdx, &df) doesn't get
filled properly; it always contains only zeros. This is not the case when I run on the CPU,
and df gets filled as it should even on the GPU, suggesting that either I'm not using
VecGetArray properly, or I shouldn't use it at all for GPU computations?

Kind regards,
Carl-Johan

From: Mark Adams <mfadams at lbl.gov>
Sent: den 31 oktober 2022 13:30
To: Carl-Johan Thore <carl-johan.thore at liu.se>
Cc: Matthew Knepley <knepley at gmail.com>; Barry Smith <bsmith at petsc.dev>; petsc-users at mcs.anl.gov
Subject: Re: [petsc-users] KSP on GPU

* You could try hypre or another preconditioner that you can afford, like LU or ASM, that works.
* If this matrix is SPD, you want to use -fieldsplit_0_pc_gamg_esteig_ksp_type cg -fieldsplit_0_pc_gamg_esteig_ksp_max_it 10
 These will give better eigen estimates, and that is important.
The differences between these steimates is not too bad.
There is a safety factor (1.05 is the default) that you could increase with: -fieldsplit_0_mg_levels_ksp_chebyshev_esteig 0,0.05,0,1.1
* Finally you could try -fieldsplit_0_pc_gamg_reuse_interpolation 1, if GAMG is still not working.

Use -fieldsplit_0_ksp_converged_reason and check the iteration count.
And it is a good idea to check with hypre to make sure something is not going badly in terms of performance anyway. AMG is hard and hypre is a good solver.

Mark

On Mon, Oct 31, 2022 at 1:56 AM Carl-Johan Thore via petsc-users <petsc-users at mcs.anl.gov<mailto:petsc-users at mcs.anl.gov>> wrote:
The GPU supports double precision and I didn't explicitly tell PETSc to use float when compiling, so
I guess it uses double? What's the easiest way to check?

Barry, running -ksp_view shows that the solver options are the same for CPU and GPU. The only
difference is the coarse grid solver for gamg ("the package used to perform factorization:") which
is petsc for CPU and cusparse for GPU. I tried forcing the GPU to use petsc via
-fieldsplit_0_mg_coarse_sub_pc_factor_mat_solver_type, but then ksp failed to converge
even on the first topology optimization iteration.

-ksp_view also shows differences in the eigenvalues from the Chebyshev smoother. For example,

GPU:
   Down solver (pre-smoother) on level 2 -------------------------------
          KSP Object: (fieldsplit_0_mg_levels_2_) 1 MPI process
            type: chebyshev
              eigenvalue targets used: min 0.109245, max 1.2017
              eigenvalues provided (min 0.889134, max 1.09245) with

CPU:
              eigenvalue targets used: min 0.112623, max 1.23886
              eigenvalues provided (min 0.879582, max 1.12623)

But I guess such differences are expected?

/Carl-Johan

From: Matthew Knepley <knepley at gmail.com<mailto:knepley at gmail.com>>
Sent: den 30 oktober 2022 22:00
To: Barry Smith <bsmith at petsc.dev<mailto:bsmith at petsc.dev>>
Cc: Carl-Johan Thore <carl-johan.thore at liu.se<mailto:carl-johan.thore at liu.se>>; petsc-users at mcs.anl.gov<mailto:petsc-users at mcs.anl.gov>
Subject: Re: [petsc-users] KSP on GPU

On Sun, Oct 30, 2022 at 3:52 PM Barry Smith <bsmith at petsc.dev<mailto:bsmith at petsc.dev>> wrote:

   In general you should expect similar but not identical conference behavior.

    I suggest running with all the monitoring you can. -ksp_monitor_true_residual -fieldsplit_0_monitor_true_residual -fieldsplit_1_monitor_true_residual and compare the various convergence between the CPU and GPU. Also run with -ksp_view and check that the various solver options are the same (they should be).

Is the GPU using float or double?

   Matt

  Barry

On Oct 30, 2022, at 11:02 AM, Carl-Johan Thore via petsc-users <petsc-users at mcs.anl.gov<mailto:petsc-users at mcs.anl.gov>> wrote:

Hi,

I'm solving a topology optimization problem with Stokes flow discretized by a stabilized Q1-Q0 finite element method
and using BiCGStab with the fieldsplit preconditioner to solve the linear systems. The implementation
is based on DMStag, runs on Ubuntu via WSL2, and works fine with PETSc-3.18.1 on multiple CPU cores and the following
options for the preconditioner:

-fieldsplit_0_ksp_type preonly \
-fieldsplit_0_pc_type gamg \
-fieldsplit_0_pc_gamg_reuse_interpolation 0 \
-fieldsplit_1_ksp_type preonly \
-fieldsplit_1_pc_type jacobi

However, when I enable GPU computations by adding two options -

...
-dm_vec_type cuda \
-dm_mat_type aijcusparse \
-fieldsplit_0_ksp_type preonly \
-fieldsplit_0_pc_type gamg \
-fieldsplit_0_pc_gamg_reuse_interpolation 0 \
-fieldsplit_1_ksp_type preonly \
-fieldsplit_1_pc_type jacobi

- KSP still works fine the first couple of topology optimization iterations but then
stops with "Linear solve did not converge due to DIVERGED_DTOL ..".

My question is whether I should expect the GPU versions of the linear solvers and pre-conditioners
to function exactly as their CPU counterparts (I got this impression from the documentation),
in which case I've probably made some mistake in my own code, or whether there are other/additional
settings or modifications I should use to run on the GPU (an NVIDIA Quadro T2000)?

Kind regards,

Carl-Johan

--
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

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