[petsc-dev] MatMult on Summit
Smith, Barry F.
bsmith at mcs.anl.gov
Sat Sep 21 22:19:43 CDT 2019
Thanks
> On Sep 21, 2019, at 10:17 PM, Zhang, Junchao <jczhang at mcs.anl.gov> wrote:
>
> 42 cores have better performance.
>
> 36 MPI ranks
> 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
> 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
> 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
>
> --Junchao Zhang
>
>
> On Sat, Sep 21, 2019 at 9:41 PM Smith, Barry F. <bsmith at mcs.anl.gov> wrote:
>
> Junchao,
>
> 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?
>
> Barry
>
> So extrapolating about 20 nodes of the CPUs is equivalent to 1 node of the GPUs for the multiply for this problem size.
>
> > On Sep 21, 2019, at 6:40 PM, Mark Adams <mfadams at lbl.gov> wrote:
> >
> > I came up with 36 cores/node for CPU GAMG runs. The memory bus is pretty saturated at that point.
> >
> > On Sat, Sep 21, 2019 at 1:44 AM Zhang, Junchao via petsc-dev <petsc-dev at mcs.anl.gov> wrote:
> > Here are CPU version results on one node with 24 cores, 42 cores. Click the links for core layout.
> >
> > 24 MPI ranks, https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c4g1r14d1b21l0=
> > 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
> > 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
> > 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
> >
> > 42 MPI ranks, https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n6c7g1r17d1b21l0=
> > 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
> > 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
> > 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
> >
> > --Junchao Zhang
> >
> >
> > On Fri, Sep 20, 2019 at 11:48 PM Smith, Barry F. <bsmith 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.
> >
> > 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
> > >
> >
>
More information about the petsc-dev
mailing list