[petsc-dev] MatMult on Summit
Mark Adams
mfadams at lbl.gov
Sat Sep 21 18:37:38 CDT 2019
On Sat, Sep 21, 2019 at 12:48 AM Smith, Barry F. via petsc-dev <
petsc-dev at mcs.anl.gov> wrote:
>
> Junchao,
>
> Very interesting. For completeness please run also 24 and 42 CPUs
> without the GPUs. Note that the default layout for CPU cores is not good.
> You will want 3 cores on each socket then 12 on each.
>
His parms are balanced. see:
https://jsrunvisualizer.olcf.ornl.gov/?s1f0o01n6c4g1r14d1b21l0=
>
> Thanks
>
> Barry
>
> Since Tim is one of our reviewers next week this is a very good test
> matrix :-)
>
>
> > On Sep 20, 2019, at 11:39 PM, Zhang, Junchao via petsc-dev <
> petsc-dev at mcs.anl.gov> wrote:
> >
> > Click the links to visualize it.
> >
> > 6 ranks
> > https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c1g1r11d1b21l0=
> > jsrun -n 6 -a 1 -c 1 -g 1 -r 6 --latency_priority GPU-GPU
> --launch_distribution packed --bind packed:1 js_task_info ./ex900 -f
> HV15R.aij -mat_type aijcusparse -vec_type cuda -n 100 -log_view
> >
> > 24 ranks
> > https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c4g1r14d1b21l0=
> > jsrun -n 6 -a 4 -c 4 -g 1 -r 6 --latency_priority GPU-GPU
> --launch_distribution packed --bind packed:1 js_task_info ./ex900 -f
> HV15R.aij -mat_type aijcusparse -vec_type cuda -n 100 -log_view
> >
> > --Junchao Zhang
> >
> >
> > On Fri, Sep 20, 2019 at 11:34 PM Mills, Richard Tran via petsc-dev <
> petsc-dev at mcs.anl.gov> wrote:
> > Junchao,
> >
> > Can you share your 'jsrun' command so that we can see how you are
> mapping things to resource sets?
> >
> > --Richard
> >
> > On 9/20/19 11:22 PM, Zhang, Junchao via petsc-dev wrote:
> >> I downloaded a sparse matrix (HV15R) from Florida Sparse Matrix
> Collection. Its size is about 2M x 2M. Then I ran the same MatMult 100
> times on one node of Summit with -mat_type aijcusparse -vec_type cuda. I
> found MatMult was almost dominated by VecScatter in this simple test. Using
> 6 MPI ranks + 6 GPUs, I found CUDA aware SF could improve performance. But
> if I enabled Multi-Process Service on Summit and used 24 ranks + 6 GPUs, I
> found CUDA aware SF hurt performance. I don't know why and have to profile
> it. I will also collect data with multiple nodes. Are the matrix and tests
> proper?
> >>
> >>
> ------------------------------------------------------------------------------------------------------------------------
> >> 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
> >>
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------
> >> 6 MPI ranks (CPU version)
> >> MatMult 100 1.0 1.1895e+01 1.0 9.63e+09 1.1 2.8e+03
> 2.2e+05 0.0e+00 24 99 97 18 0 100100100100 0 4743 0 0
> 0.00e+00 0 0.00e+00 0
> >> VecScatterBegin 100 1.0 4.9145e-02 3.0 0.00e+00 0.0 2.8e+03
> 2.2e+05 0.0e+00 0 0 97 18 0 0 0100100 0 0 0 0
> 0.00e+00 0 0.00e+00 0
> >> VecScatterEnd 100 1.0 2.9441e+00133 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 3 0 0 0 0 13 0 0 0 0 0 0 0
> 0.00e+00 0 0.00e+00 0
> >>
> >> 6 MPI ranks + 6 GPUs + regular SF
> >> MatMult 100 1.0 1.7800e-01 1.0 9.66e+09 1.1 2.8e+03
> 2.2e+05 0.0e+00 0 99 97 18 0 100100100100 0 318057 3084009 100
> 1.02e+02 100 2.69e+02 100
> >> VecScatterBegin 100 1.0 1.2786e-01 1.3 0.00e+00 0.0 2.8e+03
> 2.2e+05 0.0e+00 0 0 97 18 0 64 0100100 0 0 0 0
> 0.00e+00 100 2.69e+02 0
> >> VecScatterEnd 100 1.0 6.2196e-02 3.0 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 0 0 0 0 0 22 0 0 0 0 0 0 0
> 0.00e+00 0 0.00e+00 0
> >> VecCUDACopyTo 100 1.0 1.0850e-02 2.3 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 0 0 0 0 0 5 0 0 0 0 0 0 100
> 1.02e+02 0 0.00e+00 0
> >> VecCopyFromSome 100 1.0 1.0263e-01 1.2 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 0 0 0 0 0 54 0 0 0 0 0 0 0
> 0.00e+00 100 2.69e+02 0
> >>
> >> 6 MPI ranks + 6 GPUs + CUDA-aware SF
> >> MatMult 100 1.0 1.1112e-01 1.0 9.66e+09 1.1 2.8e+03
> 2.2e+05 0.0e+00 1 99 97 18 0 100100100100 0 509496 3133521 0
> 0.00e+00 0 0.00e+00 100
> >> VecScatterBegin 100 1.0 7.9461e-02 1.1 0.00e+00 0.0 2.8e+03
> 2.2e+05 0.0e+00 1 0 97 18 0 70 0100100 0 0 0 0
> 0.00e+00 0 0.00e+00 0
> >> VecScatterEnd 100 1.0 2.2805e-02 1.5 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 0 0 0 0 0 17 0 0 0 0 0 0 0
> 0.00e+00 0 0.00e+00 0
> >>
> >> 24 MPI ranks + 6 GPUs + regular SF
> >> MatMult 100 1.0 1.1094e-01 1.0 2.63e+09 1.2 1.9e+04
> 5.9e+04 0.0e+00 1 99 97 25 0 100100100100 0 510337 951558 100
> 4.61e+01 100 6.72e+01 100
> >> VecScatterBegin 100 1.0 4.8966e-02 1.8 0.00e+00 0.0 1.9e+04
> 5.9e+04 0.0e+00 0 0 97 25 0 34 0100100 0 0 0 0
> 0.00e+00 100 6.72e+01 0
> >> VecScatterEnd 100 1.0 7.2969e-02 4.9 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 1 0 0 0 0 42 0 0 0 0 0 0 0
> 0.00e+00 0 0.00e+00 0
> >> VecCUDACopyTo 100 1.0 4.4487e-03 1.8 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 0 0 0 0 0 3 0 0 0 0 0 0 100
> 4.61e+01 0 0.00e+00 0
> >> VecCopyFromSome 100 1.0 4.3315e-02 1.9 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 0 0 0 0 0 29 0 0 0 0 0 0 0
> 0.00e+00 100 6.72e+01 0
> >>
> >> 24 MPI ranks + 6 GPUs + CUDA-aware SF
> >> MatMult 100 1.0 1.4597e-01 1.2 2.63e+09 1.2 1.9e+04
> 5.9e+04 0.0e+00 1 99 97 25 0 100100100100 0 387864 973391 0
> 0.00e+00 0 0.00e+00 100
> >> VecScatterBegin 100 1.0 6.4899e-02 2.9 0.00e+00 0.0 1.9e+04
> 5.9e+04 0.0e+00 1 0 97 25 0 35 0100100 0 0 0 0
> 0.00e+00 0 0.00e+00 0
> >> VecScatterEnd 100 1.0 1.1179e-01 4.1 0.00e+00 0.0 0.0e+00
> 0.0e+00 0.0e+00 1 0 0 0 0 48 0 0 0 0 0 0 0
> 0.00e+00 0 0.00e+00 0
> >>
> >>
> >> --Junchao Zhang
> >
>
>
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