[petsc-users] PETSc GPU MatMatMult performance question

Barry Smith bsmith at petsc.dev
Thu Feb 3 15:25:20 CST 2022


  I suspect the new matrix has a very different parallel nonzero structure that results in MOST of the calculations taking place on the CPU (since the "off-diagonal" part of the matrix dominates the non-zero pattern). PETSc is not designed for this type of nonzero structure and will give a bad performance (CPU or GPU); it is not a "PDE-ish" type of nonzero structure.



> On Feb 3, 2022, at 2:59 PM, Rohan Yadav <rohany at alumni.cmu.edu> wrote:
> 
> I'm sorry, I did a little switch here. The original log view I sent for 2 runs was on a different input matrix. Based on Barry's request I switched to a different matrix as the original one did not fit on 1 GPU.
> 
> >  In the previously sent runs it was about 98% on GPU. 
> 
> Re 98% on the GPU though, my first email had a similar ratio in the log though:
> ```
> MatMatMultNum         30 1.0 4.2967e+01 1.0 6.34e+11 1.1 6.0e+01 9.4e+07 0.0e+00 37100 86110  0  37100 86110  0 28598   920026      2 6.71e+03   30 8.73e+04 98
> ```
> The follow up log might be slightly different as well because I pushed a new log stage as requested by Stefano.
> 
> Rohan
> 
> On Thu, Feb 3, 2022 at 11:50 AM Barry Smith <bsmith at petsc.dev <mailto:bsmith at petsc.dev>> wrote:
> 
>   Mark,
> 
>     Good eye. Something is definitely very different between this run and the previous (options, code change?). In the previously sent runs it was about 98% on GPU. 
> 
>   Barry
> 
> 
>> On Feb 3, 2022, at 12:29 PM, Rohan Yadav <rohany at alumni.cmu.edu <mailto:rohany at alumni.cmu.edu>> wrote:
>> 
>> >    Please send the code that builds the sparse B matrix and the setMatToConstant() routine.
>> 
>> Setting to a constant:
>> ```
>> void setMatToConstant(Mat mat, PetscScalar c) {
>>   PetscInt rStart, rEnd, m, n;
>>   MatGetSize(mat, &m, &n);
>>   MatGetOwnershipRange(mat, &rStart, &rEnd);
>>   for (int i = rStart; i < rEnd; i++) {
>>     for (int j = 0; j < n; j++) {
>>       MatSetValue(mat, i, j, c, INSERT_VALUES);
>>     }
>>   }
>>   MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY);
>>   MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY);
>> }
>> ```
>> 
>> Loading sparse matrix from disk:
>> ```
>> int loadMatrixFromFile(Mat* A, char* filename) {
>>   auto ierr = MatCreate(PETSC_COMM_WORLD, A); CHKERRQ(ierr);
>>   MatSetFromOptions(*A);
>>   PetscViewer viewer;
>>   PetscViewerCreate(PETSC_COMM_WORLD, &viewer);
>>   PetscViewerSetType(viewer, PETSCVIEWERBINARY);
>>   PetscViewerFileSetMode(viewer, FILE_MODE_READ);
>>   PetscViewerFileSetName(viewer, filename);
>>   MatLoad(*A, viewer);
>>   return 0;
>> }
>> ```
>> These are only called once and should not affect the computation in a loop though.
>> > But first please verify that if you run with one MPI rank the "on GPU" and the overall flop rates for the MatMatMult() are almost the same and there is no copy from the GPU for each multiply?
>> 
>> Yes, with 1 mpi rank / GPU there are no extra copies done. As soon as I move to 2 ranks I see this behavior.
>> 
>> Here are updated logs with a new stage for 2 ranks. I've staged the logs into "MyComputation".
>> 
>> ```
>> ---------------------------------------------- PETSc Performance Summary: ----------------------------------------------
>> 
>> /g/g15/yadav2/taco/petsc/bin/benchmark on a  named lassen572 with 2 processors, by yadav2 Thu Feb  3 09:27:30 2022
>> Using Petsc Release Version 3.16.3, unknown
>> 
>>                          Max       Max/Min     Avg       Total
>> Time (sec):           2.091e+02     1.001   2.090e+02
>> Objects:              4.800e+01     1.000   4.800e+01
>> Flop:                 4.344e+11     1.019   4.303e+11  8.606e+11
>> Flop/sec:             2.077e+09     1.018   2.059e+09  4.118e+09
>> MPI Messages:         3.500e+01     1.000   3.500e+01  7.000e+01
>> MPI Message Lengths:  6.316e+10     1.000   1.805e+09  1.263e+11
>> MPI Reductions:       8.100e+01     1.000
>> 
>> Flop counting convention: 1 flop = 1 real number operation of type (multiply/divide/add/subtract)
>>                             e.g., VecAXPY() for real vectors of length N --> 2N flop
>>                             and VecAXPY() for complex vectors of length N --> 8N flop
>> 
>> Summary of Stages:   ----- Time ------  ----- Flop ------  --- Messages ---  -- Message Lengths --  -- Reductions --
>>                         Avg     %Total     Avg     %Total    Count   %Total     Avg         %Total    Count   %Total
>>  0:      Main Stage: 1.0555e+02  50.5%  2.8686e+11  33.3%  3.000e+01  42.9%  1.466e+09       34.8%  4.300e+01  53.1%
>>  1:   MyComputation: 1.0345e+02  49.5%  5.7373e+11  66.7%  4.000e+01  57.1%  2.058e+09       65.2%  2.000e+01  24.7%
>> 
>> ------------------------------------------------------------------------------------------------------------------------
>> See the 'Profiling' chapter of the users' manual for details on interpreting output.
>> Phase summary info:
>>    Count: number of times phase was executed
>>    Time and Flop: Max - maximum over all processors
>>                   Ratio - ratio of maximum to minimum over all processors
>>    Mess: number of messages sent
>>    AvgLen: average message length (bytes)
>>    Reduct: number of global reductions
>>    Global: entire computation
>>    Stage: stages of a computation. Set stages with PetscLogStagePush() and PetscLogStagePop().
>>       %T - percent time in this phase         %F - percent flop in this phase
>>       %M - percent messages in this phase     %L - percent message lengths in this phase
>>       %R - percent reductions in this phase
>>    Total Mflop/s: 10e-6 * (sum of flop over all processors)/(max time over all processors)
>>    GPU Mflop/s: 10e-6 * (sum of flop on GPU over all processors)/(max GPU time over all processors)
>>    CpuToGpu Count: total number of CPU to GPU copies per processor
>>    CpuToGpu Size (Mbytes): 10e-6 * (total size of CPU to GPU copies per processor)
>>    GpuToCpu Count: total number of GPU to CPU copies per processor
>>    GpuToCpu Size (Mbytes): 10e-6 * (total size of GPU to CPU copies per processor)
>>    GPU %F: percent flops on GPU in this event
>> ------------------------------------------------------------------------------------------------------------------------
>> 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          2 1.0 4.0085e-0136.3 0.00e+00 0.0 2.0e+00 4.0e+00 2.0e+00  0  0  3  0  2   0  0  7  0  5     0       0      0 0.00e+00    0 0.00e+00  0
>> BuildTwoSidedF         1 1.0 4.0080e-0113602.0 0.00e+00 0.0 0.0e+00 0.0e+00 1.0e+00  0  0  0  0  1   0  0  0  0  2     0       0      0 0.00e+00    0 0.00e+00  0
>> MatAssemblyBegin      12 1.0 4.0084e-017217.1 0.00e+00 0.0 0.0e+00 0.0e+00 1.0e+00  0  0  0  0  1   0  0  0  0  2     0       0      0 0.00e+00    0 0.00e+00  0
>> MatAssemblyEnd        12 1.0 3.4970e+00 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 6.0e+00  2  0  0  0  7   3  0  0  0 14     0       0      0 0.00e+00    0 0.00e+00  0
>> MatZeroEntries         1 1.0 2.4093e-03 1.0 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    0 0.00e+00  0
>> MatLoad                1 1.0 1.3756e+01 1.0 0.00e+00 0.0 6.0e+00 4.6e+08 2.1e+01  7  0  9  2 26  13  0 20  6 49     0       0      0 0.00e+00    0 0.00e+00  0
>> MatMatMultSym         20 1.0 4.7919e+00 2.4 0.00e+00 0.0 4.0e+00 1.6e+07 1.2e+01  2  0  6  0 15   3  0 13  0 28     0       0      0 0.00e+00    0 0.00e+00  0
>> MatMatMultNum         10 1.0 4.9853e+01 1.1 1.45e+11 1.0 2.0e+01 2.1e+09 0.0e+00 23 33 29 33  0  46100 67 94  0  5754   182686      2 2.23e+03   10 2.08e+04  5
>> MatCUSPARSCopyTo       1 1.0 2.2646e-02 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      1 1.55e+02    0 0.00e+00  0
>> MatDenseCopyTo         1 1.0 1.6636e-01 1.0 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      1 2.08e+03    0 0.00e+00  0
>> MatDenseCopyFrom      11 1.0 3.0463e+00 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  1  0  0  0  0   3  0  0  0  0     0       0      0 0.00e+00   11 2.29e+04  0
>> VecSet                 3 1.0 5.0035e-04 1.0 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    0 0.00e+00  0
>> SFSetGraph             1 1.0 4.4294e-03 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    0 0.00e+00  0
>> SFSetUp                1 1.0 1.3982e-01 1.0 0.00e+00 0.0 4.0e+00 1.6e+07 1.0e+00  0  0  6  0  1   0  0 13  0  2     0       0      0 0.00e+00    0 0.00e+00  0
>> 
>> --- Event Stage 1: MyComputation
>> 
>> MatAssemblyBegin      20 1.0 1.6894e-05 2.7 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    0 0.00e+00  0
>> MatAssemblyEnd        20 1.0 1.5575e-05 1.5 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    0 0.00e+00  0
>> MatMatMultSym         40 1.0 1.0096e+01 2.6 0.00e+00 0.0 0.0e+00 0.0e+00 2.0e+01  3  0  0  0 25   7  0  0  0100     0       0      0 0.00e+00    0 0.00e+00  0
>> MatMatMultNum         20 1.0 9.9320e+01 1.1 2.90e+11 1.0 4.0e+01 2.1e+09 0.0e+00 46 67 57 65  0  93100100100  0  5777   182577      0 0.00e+00   20 4.16e+04  5
>> MatDenseCopyFrom      20 1.0 5.5380e+00 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  3  0  0  0  0   5  0  0  0  0     0       0      0 0.00e+00   20 4.16e+04  0
>> ---------------------------------------------------------------------------------------------------------------------------------------------------------------
>> 
>> Memory usage is given in bytes:
>> 
>> Object Type          Creations   Destructions     Memory  Descendants' Mem.
>> Reports information only for process 0.
>> 
>> --- Event Stage 0: Main Stage
>> 
>>               Matrix    17             10  20381695840     0.
>>               Viewer     2              0            0     0.
>>               Vector     4              1         1792     0.
>>            Index Set     2              2     31848152     0.
>>    Star Forest Graph     3              0            0     0.
>> 
>> --- Event Stage 1: MyComputation
>> 
>>               Matrix    20             20  40763391680     0.
>> ========================================================================================================================
>> Average time to get PetscTime(): 3.96e-08
>> Average time for MPI_Barrier(): 8.184e-07
>> Average time for zero size MPI_Send(): 2.8165e-06
>> #PETSc Option Table entries:
>> -bench spmm
>> -enable_gpu
>> -log_view
>> -mat_type aijcusparse
>> -matload_block_size 1
>> -matrix /p/gpfs1/yadav2/tensors/petsc/nlpkkt200.petsc
>> -n 20
>> -vec_type cuda
>> -warmup 10
>> #End of PETSc Option Table entries
>> Compiled without FORTRAN kernels
>> Compiled with full precision matrices (default)
>> sizeof(short) 2 sizeof(int) 4 sizeof(long) 8 sizeof(void*) 8 sizeof(PetscScalar) 8 sizeof(PetscInt) 4
>> Configure options: --download-c2html=0 --download-hwloc=0 --download-sowing=0 --prefix=./petsc-install/ --with-64-bit-indices=0 --with-blaslapack-lib="/usr/tcetmp/packages/lapack/lapack-3.9.0-gcc-7.3.1/lib/liblapack.so /usr/tcetmp/packages/lapack/lapack-3.9.0-gcc-7.3.1/lib/libblas.so" --with-cc=/usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigcc --with-clanguage=C --with-cxx-dialect=C++17 --with-cxx=/usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpig++ --with-cuda=1 --with-debugging=0 --with-fc=/usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigfortran --with-fftw=0 --with-hdf5-dir=/usr/tcetmp/packages/petsc/build/3.13.0/spack/opt/spack/linux-rhel7-power9le/xl_r-16.1/hdf5-1.10.6-e7e7urb5k7va3ib7j4uro56grvzmcmd4 --with-hdf5=1 --with-mumps=0 --with-precision=double --with-scalapack=0 --with-scalar-type=real --with-shared-libraries=1 --with-ssl=0 --with-suitesparse=0 --with-trilinos=0 --with-valgrind=0 --with-x=0 --with-zlib-include=/usr/include --with-zlib-lib=/usr/lib64/libz.so --with-zlib=1 CFLAGS="-g -DNoChange" COPTFLAGS="-O3" CXXFLAGS="-O3" CXXOPTFLAGS="-O3" FFLAGS=-g CUDAFLAGS=-std=c++17 FOPTFLAGS= PETSC_ARCH=arch-linux-c-opt
>> -----------------------------------------
>> Libraries compiled on 2022-01-21 06:41:50 on lassen111
>> Machine characteristics: Linux-4.14.0-115.21.2.1chaos.ch6a.ppc64le-ppc64le-with-redhat-7.6-Maipo
>> Using PETSc directory: /g/g15/yadav2/taco/petsc/petsc/petsc-install
>> Using PETSc arch:
>> -----------------------------------------
>> 
>> Using C compiler: /usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigcc -g -DNoChange -fPIC "-O3"
>> Using Fortran compiler: /usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigfortran -g -fPIC
>> -----------------------------------------
>> 
>> Using include paths: -I/g/g15/yadav2/taco/petsc/petsc/petsc-install/include -I/usr/tcetmp/packages/petsc/build/3.13.0/spack/opt/spack/linux-rhel7-power9le/xl_r-16.1/hdf5-1.10.6-e7e7urb5k7va3ib7j4uro56grvzmcmd4/include -I/usr/include -I/usr/tce/packages/cuda/cuda-11.1.0/include
>> -----------------------------------------
>> 
>> Using C linker: /usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigcc
>> Using Fortran linker: /usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigfortran
>> Using libraries: -Wl,-rpath,/g/g15/yadav2/taco/petsc/petsc/petsc-install/lib -L/g/g15/yadav2/taco/petsc/petsc/petsc-install/lib -lpetsc -Wl,-rpath,/usr/tcetmp/packages/lapack/lapack-3.9.0-gcc-7.3.1/lib -L/usr/tcetmp/packages/lapack/lapack-3.9.0-gcc-7.3.1/lib -Wl,-rpath,/usr/tcetmp/packages/petsc/build/3.13.0/spack/opt/spack/linux-rhel7-power9le/xl_r-16.1/hdf5-1.10.6-e7e7urb5k7va3ib7j4uro56grvzmcmd4/lib -L/usr/tcetmp/packages/petsc/build/3.13.0/spack/opt/spack/linux-rhel7-power9le/xl_r-16.1/hdf5-1.10.6-e7e7urb5k7va3ib7j4uro56grvzmcmd4/lib -Wl,-rpath,/usr/tce/packages/cuda/cuda-11.1.0/lib64 -L/usr/tce/packages/cuda/cuda-11.1.0/lib64 -Wl,-rpath,/usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib -L/usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib -Wl,-rpath,/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib/gcc/ppc64le-redhat-linux/8 -L/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib/gcc/ppc64le-redhat-linux/8 -Wl,-rpath,/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib/gcc -L/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib/gcc -Wl,-rpath,/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib64 -L/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib64 -Wl,-rpath,/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib -L/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib -llapack -lblas -lhdf5_hl -lhdf5 -lm /usr/lib64/libz.so -lcuda -lcudart -lcufft -lcublas -lcusparse -lcusolver -lcurand -lstdc++ -ldl -lmpiprofilesupport -lmpi_ibm_usempi -lmpi_ibm_mpifh -lmpi_ibm -lgfortran -lm -lgfortran -lm -lgcc_s -lquadmath -lpthread -lquadmath -lstdc++ -ldl
>> -----------------------------------------
>> ```
>> 
>> On Wed, Feb 2, 2022 at 11:59 PM Stefano Zampini <stefano.zampini at gmail.com <mailto:stefano.zampini at gmail.com>> wrote:
>> 
>> 
>> 1) It uses MatMPIDenseScatter() to move to the other ranks their needed rows of the C matrix. That function has the call MatDenseGetArrayRead() normally would trigger a copy of C up to the CPU each time. But since C is not changing in your test run I guess it only triggers one copy.
>> 
>> 2) If uses MatMatMultNumericAdd_SeqAIJ_SeqDense(aij->B,workB,cdense->A,PETSC_TRUE);CHKERRQ(ierr); to do the off diagonal part of the product but this triggers for each multiply a copy of the result matrix from the CPU to the GPU (hugely expensive)
>> 
>> For performance there needs to be a new routine MatMatMultNumeric_MPIAIJCUSPRSE_MPICUDADense() that is smarter about the needed MPI communication so it only moves exactly what it needs to the other ranks and it does the off-diagonal part of the product on the GPU so it does not need to copy the result up to the CPU. 
>> 
>> 
>> MPIAIJCUSPARSE uses MatProductSetFromOptions_MPIAIJBACKEND
>> 
>> Rohan
>> I would suggest to add PetscLogStage around your performance loop (do a warmup outside of it) and send the relevant portion of the log
>>  
>> Barry
>> 
>> 
>> 
>> 
>> 
>> 
>>> ---------------------------------------------- PETSc Performance Summary: ----------------------------------------------
>>> 
>>> /g/g15/yadav2/taco/petsc/bin/benchmark on a  named lassen457 with 2 processors, by yadav2 Wed Feb  2 17:23:19 2022
>>> Using Petsc Release Version 3.16.3, unknown
>>> 
>>>                          Max       Max/Min     Avg       Total
>>> Time (sec):           1.163e+02     1.000   1.163e+02
>>> Objects:              4.800e+01     1.000   4.800e+01
>>> Flop:                 6.338e+11     1.065   6.144e+11  1.229e+12
>>> Flop/sec:             5.451e+09     1.065   5.284e+09  1.057e+10
>>> MPI Messages:         3.500e+01     1.000   3.500e+01  7.000e+01
>>> MPI Message Lengths:  2.544e+09     1.000   7.267e+07  5.087e+09
>>> MPI Reductions:       8.100e+01     1.000
>>> 
>>> Flop counting convention: 1 flop = 1 real number operation of type (multiply/divide/add/subtract)
>>>                             e.g., VecAXPY() for real vectors of length N --> 2N flop
>>>                             and VecAXPY() for complex vectors of length N --> 8N flop
>>> 
>>> Summary of Stages:   ----- Time ------  ----- Flop ------  --- Messages ---  -- Message Lengths --  -- Reductions --
>>>                         Avg     %Total     Avg     %Total    Count   %Total     Avg         %Total    Count   %Total
>>>  0:      Main Stage: 1.1628e+02 100.0%  1.2288e+12 100.0%  7.000e+01 100.0%  7.267e+07      100.0%  6.300e+01  77.8%
>>> 
>>> ------------------------------------------------------------------------------------------------------------------------
>>> See the 'Profiling' chapter of the users' manual for details on interpreting output.
>>> Phase summary info:
>>>    Count: number of times phase was executed
>>>    Time and Flop: Max - maximum over all processors
>>>                   Ratio - ratio of maximum to minimum over all processors
>>>    Mess: number of messages sent
>>>    AvgLen: average message length (bytes)
>>>    Reduct: number of global reductions
>>>    Global: entire computation
>>>    Stage: stages of a computation. Set stages with PetscLogStagePush() and PetscLogStagePop().
>>>       %T - percent time in this phase         %F - percent flop in this phase
>>>       %M - percent messages in this phase     %L - percent message lengths in this phase
>>>       %R - percent reductions in this phase
>>>    Total Mflop/s: 10e-6 * (sum of flop over all processors)/(max time over all processors)
>>>    GPU Mflop/s: 10e-6 * (sum of flop on GPU over all processors)/(max GPU time over all processors)
>>>    CpuToGpu Count: total number of CPU to GPU copies per processor
>>>    CpuToGpu Size (Mbytes): 10e-6 * (total size of CPU to GPU copies per processor)
>>>    GpuToCpu Count: total number of GPU to CPU copies per processor
>>>    GpuToCpu Size (Mbytes): 10e-6 * (total size of GPU to CPU copies per processor)
>>>    GPU %F: percent flops on GPU in this event
>>> ------------------------------------------------------------------------------------------------------------------------
>>> 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          2 1.0 4.4400e-01567.5 0.00e+00 0.0 2.0e+00 4.0e+00 2.0e+00  0  0  3  0  2   0  0  3  0  3     0       0      0 0.00e+00    0 0.00e+00  0
>>> BuildTwoSidedF         1 1.0 4.4395e-0115659.1 0.00e+00 0.0 0.0e+00 0.0e+00 1.0e+00  0  0  0  0  1   0  0  0  0  2     0       0      0 0.00e+00    0 0.00e+00  0
>>> MatAssemblyBegin      32 1.0 4.4400e-017378.9 0.00e+00 0.0 0.0e+00 0.0e+00 1.0e+00  0  0  0  0  1   0  0  0  0  2     0       0      0 0.00e+00    0 0.00e+00  0
>>> MatAssemblyEnd        32 1.0 1.8511e+00 2.2 0.00e+00 0.0 0.0e+00 0.0e+00 6.0e+00  1  0  0  0  7   1  0  0  0 10     0       0      0 0.00e+00    0 0.00e+00  0
>>> MatZeroEntries         1 1.0 3.3306e-03 1.0 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    0 0.00e+00  0
>>> MatLoad                1 1.0 1.7220e+01 1.0 0.00e+00 0.0 6.0e+00 -8.8e+07 2.1e+01 15  0  9-10 26  15  0  9-10 33     0       0      0 0.00e+00    0 0.00e+00  0
>>> MatMatMultSym         60 1.0 9.2215e-01 2.6 0.00e+00 0.0 4.0e+00 7.3e+05 3.2e+01  1  0  6  0 40   1  0  6  0 51     0       0      0 0.00e+00    0 0.00e+00  0
>>> MatMatMultNum         30 1.0 4.2967e+01 1.0 6.34e+11 1.1 6.0e+01 9.4e+07 0.0e+00 37100 86110  0  37100 86110  0 28598   920026      2 6.71e+03   30 8.73e+04 98
>>> MatCUSPARSCopyTo       1 1.0 4.4761e-01 1.0 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      1 3.80e+03    0 0.00e+00  0
>>> MatDenseCopyTo         1 1.0 2.2742e-01 1.0 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      1 2.91e+03    0 0.00e+00  0
>>> MatDenseCopyFrom      31 1.0 1.2006e+01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 10  0  0  0  0  10  0  0  0  0     0       0      0 0.00e+00   31 9.02e+04  0
>>> VecSet                 3 1.0 4.1917e-04 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    0 0.00e+00  0
>>> SFSetGraph             1 1.0 1.9180e-04 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    0 0.00e+00  0
>>> SFSetUp                1 1.0 1.3672e-02 1.1 0.00e+00 0.0 4.0e+00 7.3e+05 1.0e+00  0  0  6  0  1   0  0  6  0  2     0       0      0 0.00e+00    0 0.00e+00  0
>>> ---------------------------------------------------------------------------------------------------------------------------------------------------------------
>>> 
>>> Memory usage is given in bytes:
>>> 
>>> Object Type          Creations   Destructions     Memory  Descendants' Mem.
>>> Reports information only for process 0.
>>> 
>>> --- Event Stage 0: Main Stage
>>> 
>>>               Matrix    37             30   2867511840     0.
>>>               Viewer     2              0            0     0.
>>>               Vector     4              1         1792     0.
>>>            Index Set     2              2      1495248     0.
>>>    Star Forest Graph     3              0            0     0.
>>> ========================================================================================================================
>>> Average time to get PetscTime(): 3.83e-08
>>> Average time for MPI_Barrier(): 7.874e-07
>>> Average time for zero size MPI_Send(): 3.4035e-06
>>> #PETSc Option Table entries:
>>> -bench spmm
>>> -enable_gpu
>>> -log_view
>>> -mat_type aijcusparse
>>> -matload_block_size 1
>>> -matrix /p/gpfs1/yadav2/tensors/petsc/arabic-2005.petsc
>>> -n 20
>>> -vec_type cuda
>>> -warmup 10
>>> ```
>>> 
>>> Thanks,
>>> 
>>> Rohan Yadav
>>> 
>> 
>> 
>> 
>> -- 
>> Stefano
> 

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