[petsc-users] Proper GPU usage in PETSc

Zhang, Chonglin zhangc20 at rpi.edu
Thu Sep 24 14:08:56 CDT 2020


Hi Matt,

I will quickly summarize what I found with “CreateMesh" for running ex12 here: https://gitlab.com/petsc/petsc/-/blob/master/src/snes/tutorials/ex12.c. If this is not a proper threads to discuss this, I can open a new one.

Commands used (relevant to mesh creation) to run ex12 (quad core desktop computer with CPU only, 4 MPI ranks):
mpirun -np 4 -cells 100, 100, 0 -options_left -log_view
I built PETSc commit: 2bbfc05, dated Sep 23, 2020, with debug=no.

Mesh size       CreateMesh (seconds)  DMPlexDistribute (seconds)
 100 *100             0.14                               0.081
 500 *500             2.28                               1.33
 1000*1000          10.1                               5.10
 2000*1000          24.6                              10.96
 2000*2000          73.7                              22.72

Is the performance reasonable for the “CreateMesh” functionality?

Anything I am not doing correctly with DMPlex running ex12?

Thanks!
Chonglin

On Sep 24, 2020, at 2:06 PM, Matthew Knepley <knepley at gmail.com<mailto:knepley at gmail.com>> wrote:

On Thu, Sep 24, 2020 at 2:04 PM Mark Adams <mfadams at lbl.gov<mailto:mfadams at lbl.gov>> wrote:
On Thu, Sep 24, 2020 at 1:38 PM Matthew Knepley <knepley at gmail.com<mailto:knepley at gmail.com>> wrote:
On Thu, Sep 24, 2020 at 12:48 PM Zhang, Chonglin <zhangc20 at rpi.edu<mailto:zhangc20 at rpi.edu>> wrote:
Thanks Mark and Barry!

A quick try of using “-pc_type jacobi” did reduce the number of count for “CpuToGpu” and “GpuToCpu”, although using “-pc_type gamg” (the counts did not decrease in this case) solves the problem faster (may not be of any meaning since the problem size is too small; the function “DMPlexCreateFromCellListParallelPetsc()" is slow for large problem size preventing running larger problems, separate issue).

It sounds like something is wrong then, or I do not understand what you mean by slow.

sor may be the default so you need to set the -mg_level_ksp[pc]_type chebyshev[jacobi]. chebyshev is the ksp default.

I meant for the mesh creation.

  Thanks,

     Matt

  Thanks,

     Matt

Would this “CpuToGpu” and “GpuToCpu” data transfer contribute a significant amount of time for a realistic sized problem, say for example a linear problem with ~1-2 million DOFs?

Also, is there any plan to have the SNES and DMPlex code run on GPU?

Thanks!
Chonglin

On Sep 24, 2020, at 12:17 PM, Barry Smith <bsmith at petsc.dev<mailto:bsmith at petsc.dev>> wrote:


   MatSOR() runs on the CPU, this causes copy to CPU for each application of MatSOR() and then a copy to GPU for the next step.

   You can try, for example -pc_type jacobi  better yet use PCGAMG if it amenable for your problem.

   Also the problem is way to small for a GPU.

  There will be copies between the GPU/CPU for each SNES iteration since the DMPLEX code does not run on GPUs.

   Barry



On Sep 24, 2020, at 10:08 AM, Zhang, Chonglin <zhangc20 at rpi.edu<mailto:zhangc20 at rpi.edu>> wrote:

Dear PETSc Users,

I have some questions regarding the proper GPU usage. I would like to know the proper way to:
(1) solve linear equation in SNES, using GPU in PETSc; what syntax/arguments should I be using;
(2) how to avoid/reduce the “CpuToGpu count” and “GpuToCpu count” data transfer showed in PETSc log file, when using CUDA aware MPI.


Details of what I am doing now and my observations are below:

System and compilers used:
(1) RPI’s AiMOS computer (node wise, it is the same as Summit);
(2) using GCC 7.4.0 and Spectrum-MPI 10.3.

I am doing the followings to solve the linear Poisson equation with SNES interface, under DMPlex:
(1) using DMPlex to set up the unstructured mesh;
(2) using DM to create vector and matrix;
(3) using SNES interface to solve the linear Poisson equation, with “-snes_type ksponly”;
(4) using “dm_vec_type cuda”, “dm_mat_type aijcusparse “ to use GPU vector and matrix, as suggested in this webpage: https://www.mcs.anl.gov/petsc/features/gpus.html
(5) using “use_gpu_aware_mpi” with PETSc, and using `mpirun -gpu` to enable GPU-Direct ( similar as "srun --smpiargs=“-gpu”" for Summit): https://secure.cci.rpi.edu/wiki/Slurm/#gpu-direct; https://www.olcf.ornl.gov/wp-content/uploads/2018/11/multi-gpu-workshop.pdf
(6) using “-options_left” to check and make sure all the arguments are accepted and used by PETSc.
(7) After problem setup, I am running the “SNESSolve()” multiple times to solve the linear problem and observe the log file with “-log_view"

I noticed that if I run “SNESSolve()” 500 times, instead of 50 times, the “CpuToGpu count” and/or “GpuToCpu count” increased roughly 10 times for some of the operations: SNESSolve, MatSOR, VecMDot, VecCUDACopyTo, VecCUDACopyFrom, MatCUSPARSCopyTo. See below for a truncated log corresponding to running SNESSolve() 500 times:


Event                Count      Time (sec)     Flop                              --- Global ---  --- Stage ----  Total   GPU    - CpuToGpu -   - GpuToCpu - GPU
                   Max Ratio  Max     Ratio   Max  Ratio  Mess   AvgLen  Reduct  %T %F %M %L %R  %T %F %M %L %R Mflop/s Mflop/s Count   Size   Count   Size  %F
---------------------------------------------------------------------------------------------------------------------------------------------------------------

--- Event Stage 0: Main Stage

BuildTwoSided        510 1.0 4.9205e-03 1.1 0.00e+00 0.0 3.5e+01 4.0e+00 1.0e+03  0  0  0  0  0   0  0 21  0  0     0       0      0 0.00e+00    0 0.00e+00  0
BuildTwoSidedF       501 1.0 1.0199e-02 1.4 0.00e+00 0.0 0.0e+00 0.0e+00 1.0e+03  0  0  0  0  0   0  0  0  0  0     0       0      0 0.00e+00    0 0.00e+00  0
SNESSolve            500 1.0 3.2570e+02 1.0 1.18e+10 1.0 0.0e+00 0.0e+00 8.7e+05100100  0  0100 100100  0  0100   144     202   31947 7.82e+02 63363 1.44e+03 82
SNESSetUp              1 1.0 6.0082e-04 1.7 0.00e+00 0.0 0.0e+00 0.0e+00 1.0e+00  0  0  0  0  0   0  0  0  0  0     0       0      0 0.00e+00    0 0.00e+00  0
SNESFunctionEval     500 1.0 3.9826e+01 1.0 3.60e+08 1.0 0.0e+00 0.0e+00 5.0e+02 12  3  0  0  0  12  3  0  0  0    36      13      0 0.00e+00 1000 2.48e+01  0
SNESJacobianEval     500 1.0 4.8200e+01 1.0 5.97e+08 1.0 0.0e+00 0.0e+00 2.0e+03 15  5  0  0  0  15  5  0  0  0    50       0   1000 7.77e+01  500 1.24e+01  0
DMPlexResidualFE     500 1.0 3.6923e+01 1.1 3.56e+08 1.0 0.0e+00 0.0e+00 0.0e+00 10  3  0  0  0  10  3  0  0  0    39       0      0 0.00e+00  500 1.24e+01  0
DMPlexJacobianFE     500 1.0 4.6013e+01 1.0 5.95e+08 1.0 0.0e+00 0.0e+00 2.0e+03 14  5  0  0  0  14  5  0  0  0    52       0   1000 7.77e+01    0 0.00e+00  0
MatSOR             30947 1.0 3.1254e+00 1.1 1.21e+09 1.0 0.0e+00 0.0e+00 0.0e+00  1 10  0  0  0   1 10  0  0  0  1542       0      0 0.00e+00 61863 1.41e+03  0
MatAssemblyBegin     511 1.0 5.3428e+00256.4 0.00e+00 0.0 0.0e+00 0.0e+00 2.0e+03  1  0  0  0  0   1  0  0  0  0     0       0      0 0.00e+00    0 0.00e+00  0
MatAssemblyEnd       511 1.0 4.3440e-02 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 2.1e+01  0  0  0  0  0   0  0  0  0  0     0       0   1002 7.80e+01    0 0.00e+00  0
MatCUSPARSCopyTo    1002 1.0 3.6557e-02 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   0  0  0  0  0     0       0   1002 7.80e+01    0 0.00e+00  0
VecMDot            29930 1.0 3.7843e+01 1.0 2.62e+09 1.0 0.0e+00 0.0e+00 6.0e+04 12 22  0  0  7  12 22  0  0  7   277    3236   29930 6.81e+02    0 0.00e+00 100
VecNorm            31447 1.0 2.1164e+01 1.4 1.79e+08 1.0 0.0e+00 0.0e+00 6.3e+04  5  2  0  0  7   5  2  0  0  7    34      55   1017 2.31e+01    0 0.00e+00 100
VecNormalize       30947 1.0 2.3957e+01 1.1 2.65e+08 1.0 0.0e+00 0.0e+00 6.2e+04  7  2  0  0  7   7  2  0  0  7    44      51   1017 2.31e+01    0 0.00e+00 100
VecCUDACopyTo      30947 1.0 7.8866e+00 3.4 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  2  0  0  0  0   2  0  0  0  0     0       0   30947 7.04e+02    0 0.00e+00  0
VecCUDACopyFrom    63363 1.0 1.0873e+00 1.1 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   0  0  0  0  0     0       0      0 0.00e+00 63363 1.44e+03  0
KSPSetUp             500 1.0 2.2737e-03 1.1 0.00e+00 0.0 0.0e+00 0.0e+00 5.0e+00  0  0  0  0  0   0  0  0  0  0     0       0      0 0.00e+00    0 0.00e+00  0
KSPSolve             500 1.0 2.3687e+02 1.0 1.08e+10 1.0 0.0e+00 0.0e+00 8.6e+05 72 92  0  0 99  73 92  0  0 99   182     202   30947 7.04e+02 61863 1.41e+03 89
KSPGMRESOrthog     29930 1.0 1.8920e+02 1.0 7.87e+09 1.0 0.0e+00 0.0e+00 6.4e+05 58 67  0  0 74  58 67  0  0 74   166     209   29930 6.81e+02    0 0.00e+00 100
PCApply            30947 1.0 3.1555e+00 1.1 1.21e+09 1.0 0.0e+00 0.0e+00 0.0e+00  1 10  0  0  0   1 10  0  0  0  1527       0      0 0.00e+00 61863 1.41e+03  0


Thanks!
Chonglin




--
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

https://www.cse.buffalo.edu/~knepley/<http://www.cse.buffalo.edu/~knepley/>


--
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

https://www.cse.buffalo.edu/~knepley/<http://www.cse.buffalo.edu/~knepley/>

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