[petsc-dev] MatMult on Summit
Matthew Knepley
knepley at gmail.com
Mon Sep 23 11:10:12 CDT 2019
Thanks Junchao. That is very clear and helpful.
Matt
On Mon, Sep 23, 2019 at 12:01 PM Zhang, Junchao via petsc-dev <
petsc-dev at mcs.anl.gov> wrote:
> I also did OpenMP stream test and then I found mismatch between OpenMPI
> and MPI. That reminded me a subtle issue on summit: pair of cores share L2
> cache. One has to place MPI ranks to different pairs to get best
> bandwidth. See different bindings
> https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n2c21g3r12d1b21l0= and
> https://jsrunvisualizer.olcf.ornl.gov/?s4f1o01n2c21g3r12d1b22l0=. Note
> each node has 21 cores. I assume that means 11 pairs. The new results are
> below. They match with we what I got from OpenMPI. The bandwidth is almost
> doubled from 1 to 2 cores per socket. IBM document also says each socket
> has two memory controllers. I could not find the core-memory controller
> affinity info. I tried different bindings but did not find huge difference.
>
> #Ranks Rate (MB/s) Ratio over 2 ranks
> 1 29229.8 -
> 2 59091.0 1.0
> 4 112260.7 1.9
> 6 159852.8 2.7
> 8 194351.7 3.3
> 10 215841.0 3.7
> 12 232316.6 3.9
> 14 244615.7 4.1
> 16 254450.8 4.3
> 18 262185.7 4.4
> 20 267181.0 4.5
> 22 270290.4 4.6
> 24 221944.9 3.8
> 26 238302.8 4.0
>
>
> --Junchao Zhang
>
>
> On Sun, Sep 22, 2019 at 6:04 PM Smith, Barry F. <bsmith at mcs.anl.gov>
> wrote:
>
>>
>> Junchao,
>>
>> For completeness could you please run with a single core? But leave
>> the ratio as you have with over 2 ranks since that is the correct model.
>>
>> Thanks
>>
>> Barry
>>
>>
>> > On Sep 22, 2019, at 11:14 AM, Zhang, Junchao <jczhang at mcs.anl.gov>
>> wrote:
>> >
>> > I did stream test on Summit. I used the MPI version from petsc, but
>> largely increased the array size N since one socket of Summit has 120MB L3
>> cache. I used MPI version since it was easy for me to distribute ranks
>> evenly to the two sockets.
>> > The result matches with data released by OLCF (see attached figure) and
>> data given by Jed. We can see the bandwidth saturates around 24 ranks.
>> >
>> > #Ranks Rate (MB/s) Ratio over 2 ranks
>> > ------------------------------------------
>> > 2 59012.2834 1.00
>> > 4 70959.1475 1.20
>> > 6 106639.9837 1.81
>> > 8 138638.6929 2.35
>> > 10 171125.0873 2.90
>> > 12 196162.5197 3.32
>> > 14 215272.7810 3.65
>> > 16 229562.4040 3.89
>> > 18 242587.4913 4.11
>> > 20 251057.1731 4.25
>> > 22 258569.7794 4.38
>> > 24 265443.2924 4.50
>> > 26 266562.7872 4.52
>> > 28 267043.6367 4.53
>> > 30 266833.7212 4.52
>> > 32 267183.8474 4.53
>> >
>> > On Sat, Sep 21, 2019 at 11:24 PM Smith, Barry F. <bsmith at mcs.anl.gov>
>> wrote:
>> >
>> > Junchao could try the PETSc (and non-PETSc) streams tests on the
>> machine.
>> >
>> > There are a few differences, compiler, the reported results are with
>> OpenMP, different number of cores but yes the performance is a bit low. For
>> DOE that is great, makes GPUs look better :-)
>> >
>> >
>> > > On Sep 21, 2019, at 11:11 PM, Jed Brown <jed at jedbrown.org> wrote:
>> > >
>> > > For an AIJ matrix with 32-bit integers, this is 1 flops/6 bytes, or
>> 165
>> > > GB/s for the node for the best case (42 ranks).
>> > >
>> > > My understanding is that these systems have 8 channels of DDR4-2666
>> per
>> > > socket, which is ~340 GB/s of theoretical bandwidth on a 2-socket
>> > > system, and 270 GB/s STREAM Triad according to this post
>> > >
>> > >
>> https://openpowerblog.wordpress.com/2018/07/19/epyc-skylake-vs-power9-stream-memory-bandwidth-comparison-via-zaius-barreleye-g2/
>> > >
>> > > Is this 60% of Triad the best we can get for SpMV?
>> > >
>> > > "Zhang, Junchao via petsc-dev" <petsc-dev at mcs.anl.gov> writes:
>> > >
>> > >> 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
>> <mailto: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<mailto:
>> 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<mailto: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<mailto: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<mailto: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<mailto: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
>> > >>>>
>> > >>>
>> >
>> > <SummitNode.png>
>>
>>
--
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which their
experiments lead.
-- Norbert Wiener
https://www.cse.buffalo.edu/~knepley/ <http://www.cse.buffalo.edu/~knepley/>
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