[petsc-users] Proper GPU usage in PETSc
Mark Adams
mfadams at lbl.gov
Thu Sep 24 13:04:27 CDT 2020
On Thu, Sep 24, 2020 at 1:38 PM Matthew Knepley <knepley at gmail.com> wrote:
> On Thu, Sep 24, 2020 at 12:48 PM Zhang, Chonglin <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.
>
> 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> 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> 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/>
>
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