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<div><font face="monospace">42 cores have better performance.</font></div>
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<div><font face="monospace">36 MPI ranks</font></div>
<div><font face="monospace">MatMult 100 1.0 2.2435e+00 1.0 1.75e+09 1.3 2.9e+04 4.5e+04 0.0e+00 6 99 97 28 0 100100100100 0 25145 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterBegin 100 1.0 2.1869e-02 3.3 0.00e+00 0.0 2.9e+04 4.5e+04 0.0e+00 0 0 97 28 0 1 0100100 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterEnd 100 1.0 7.9205e-0152.6 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 22 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0</font></div>
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<div dir="ltr">--Junchao Zhang</div>
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<div dir="ltr" class="gmail_attr">On Sat, Sep 21, 2019 at 9:41 PM Smith, Barry F. <<a href="mailto:bsmith@mcs.anl.gov">bsmith@mcs.anl.gov</a>> wrote:<br>
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<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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Junchao,<br>
<br>
Mark has a good point; could you also try for completeness the CPU with 36 cores and see if it is any better than the 42 core case?<br>
<br>
Barry<br>
<br>
So extrapolating about 20 nodes of the CPUs is equivalent to 1 node of the GPUs for the multiply for this problem size.<br>
<br>
> On Sep 21, 2019, at 6:40 PM, Mark Adams <<a href="mailto:mfadams@lbl.gov" target="_blank">mfadams@lbl.gov</a>> wrote:<br>
> <br>
> I came up with 36 cores/node for CPU GAMG runs. The memory bus is pretty saturated at that point.<br>
> <br>
> On Sat, Sep 21, 2019 at 1:44 AM Zhang, Junchao via petsc-dev <<a href="mailto:petsc-dev@mcs.anl.gov" target="_blank">petsc-dev@mcs.anl.gov</a>> wrote:<br>
> Here are CPU version results on one node with 24 cores, 42 cores. Click the links for core layout.<br>
> <br>
> 24 MPI ranks, <a href="https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c4g1r14d1b21l0=" rel="noreferrer" target="_blank">
https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c4g1r14d1b21l0=</a><br>
> MatMult 100 1.0 3.1431e+00 1.0 2.63e+09 1.2 1.9e+04 5.9e+04 0.0e+00 8 99 97 25 0 100100100100 0 17948 0 0 0.00e+00 0 0.00e+00 0<br>
> VecScatterBegin 100 1.0 2.0583e-02 2.3 0.00e+00 0.0 1.9e+04 5.9e+04 0.0e+00 0 0 97 25 0 0 0100100 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
> VecScatterEnd 100 1.0 1.0639e+0050.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 2 0 0 0 0 19 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
> <br>
> 42 MPI ranks, <a href="https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c7g1r17d1b21l0=" rel="noreferrer" target="_blank">
https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c7g1r17d1b21l0=</a><br>
> MatMult 100 1.0 2.0519e+00 1.0 1.52e+09 1.3 3.5e+04 4.1e+04 0.0e+00 23 99 97 30 0 100100100100 0 27493 0 0 0.00e+00 0 0.00e+00 0<br>
> VecScatterBegin 100 1.0 2.0971e-02 3.4 0.00e+00 0.0 3.5e+04 4.1e+04 0.0e+00 0 0 97 30 0 1 0100100 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
> VecScatterEnd 100 1.0 8.5184e-0162.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 6 0 0 0 0 24 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
> <br>
> --Junchao Zhang<br>
> <br>
> <br>
> On Fri, Sep 20, 2019 at 11:48 PM Smith, Barry F. <<a href="mailto:bsmith@mcs.anl.gov" target="_blank">bsmith@mcs.anl.gov</a>> wrote:<br>
> <br>
> Junchao,<br>
> <br>
> 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.<br>
> <br>
> Thanks<br>
> <br>
> Barry<br>
> <br>
> Since Tim is one of our reviewers next week this is a very good test matrix :-)<br>
> <br>
> <br>
> > On Sep 20, 2019, at 11:39 PM, Zhang, Junchao via petsc-dev <<a href="mailto:petsc-dev@mcs.anl.gov" target="_blank">petsc-dev@mcs.anl.gov</a>> wrote:<br>
> > <br>
> > Click the links to visualize it.<br>
> > <br>
> > 6 ranks<br>
> > <a href="https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c1g1r11d1b21l0=" rel="noreferrer" target="_blank">
https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c1g1r11d1b21l0=</a><br>
> > 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<br>
> > <br>
> > 24 ranks<br>
> > <a href="https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c4g1r14d1b21l0=" rel="noreferrer" target="_blank">
https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c4g1r14d1b21l0=</a><br>
> > 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<br>
> > <br>
> > --Junchao Zhang<br>
> > <br>
> > <br>
> > On Fri, Sep 20, 2019 at 11:34 PM Mills, Richard Tran via petsc-dev <<a href="mailto:petsc-dev@mcs.anl.gov" target="_blank">petsc-dev@mcs.anl.gov</a>> wrote:<br>
> > Junchao,<br>
> > <br>
> > Can you share your 'jsrun' command so that we can see how you are mapping things to resource sets?<br>
> > <br>
> > --Richard<br>
> > <br>
> > On 9/20/19 11:22 PM, Zhang, Junchao via petsc-dev wrote:<br>
> >> 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?<br>
> >> <br>
> >> ------------------------------------------------------------------------------------------------------------------------<br>
> >> Event Count Time (sec) Flop --- Global --- --- Stage ---- Total GPU - CpuToGpu - - GpuToCpu - GPU<br>
> >> 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<br>
> >> ---------------------------------------------------------------------------------------------------------------------------------------------------------------<br>
> >> 6 MPI ranks (CPU version)<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> <br>
> >> 6 MPI ranks + 6 GPUs + regular SF<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> <br>
> >> 6 MPI ranks + 6 GPUs + CUDA-aware SF<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> <br>
> >> 24 MPI ranks + 6 GPUs + regular SF<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> <br>
> >> 24 MPI ranks + 6 GPUs + CUDA-aware SF<br>
> >> 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<br>
> >> 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<br>
> >> 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<br>
> >> <br>
> >> <br>
> >> --Junchao Zhang<br>
> > <br>
> <br>
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