[petsc-users] Proper GPU usage in PETSc
Zhang, Chonglin
zhangc20 at rpi.edu
Thu Sep 24 11:48:07 CDT 2020
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).
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
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