[petsc-users] petsc4py help with parallel execution
Ivan Voznyuk
ivan.voznyuk.work at gmail.com
Fri Nov 16 12:02:44 CST 2018
Hi Satish,
Thanks for your reply.
Bad news... I tested 2 solutions that you proposed, none has worked.
1. --with-blaslapack-dir=/opt/intel/mkl
--with-mkl_pardiso-dir=/opt/intel/mkl installed well, without any problems.
However, the code is still turning in sequential way.
2. When I changed -lmkl_sequential to -lmkl_intel_thread -liomp, he at
first did not find the liomp, so I had to create a symbolic link of libiomp5.so
to /lib.
At the launching of the .py code I had to go with:
export
LD_PRELOAD=/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_sequential.so
and
export LD_LIBRARY_PATH=/opt/petsc/petsc1/arch-linux2-c-debug/lib/
But still it does not solve the given problem and code is still running
sequentially...
May be you have some other ideas?
Thanks,
Ivan
On Fri, Nov 16, 2018 at 6:11 PM Balay, Satish <balay at mcs.anl.gov> wrote:
> Yes PETSc prefers sequential MKL - as MPI handles parallelism.
>
> One way to trick petsc configure to use threaded MKL is to enable pardiso.
> i.e:
>
> --with-blaslapack-dir=/opt/intel/mkl --with-mkl_pardiso-dir=/opt/intel/mkl
>
>
> http://ftp.mcs.anl.gov/pub/petsc/nightlylogs/archive/2018/11/15/configure_master_arch-pardiso_grind.log
>
> BLAS/LAPACK: -Wl,-rpath,/soft/com/packages/intel/16/u3/mkl/lib/intel64
> -L/soft/com/packages/intel/16/u3/mkl/lib/intel64 -lmkl_intel_lp64
> -lmkl_core -lmkl_intel_thread -liomp5 -ldl -lpthread
>
> Or you can manually specify the correct MKL library list [with
> threading] via --with-blaslapack-lib option.
>
> Satish
>
> On Fri, 16 Nov 2018, Ivan Voznyuk via petsc-users wrote:
>
> > Hi,
> > You were totally right: no miracle, parallelization does come from
> > multithreading. We checked Option 1/: played with OMP_NUM_THREADS=1 it
> > changed computational time.
> >
> > So, I reinstalled everything (starting with Ubuntu ending with petsc) and
> > configured the following things:
> >
> > - installed system's ompenmpi
> > - installed Intel MKL Blas / Lapack
> > - configured PETSC as ./configure --with-cc=mpicc --with-fc=mpif90
> > --with-cxx=mpicxx --with-blas-lapack-dir=/opt/intel/mkl/lib/intel64
> > --download-scalapack --download-mumps --with-hwloc --with-shared
> > --with-openmp=1 --with-pthread=1 --with-scalar-type=complex
> > hoping that it would take into account blas multithreading
> > - installed petsc4py
> >
> > However, I do not get any parallelization...
> > What I tried to do so far unsuccessfully :
> > - play with OMP_NUM_THREADS
> > - reinstall the system
> > - ldd PETSc.cpython-35m-x86_64-linux-gnu.so yields lld_result.txt (here
> > attached)
> > I noted that libmkl_sequential.so library there. Do you think this is
> > normal?
> > - I found a similar problem reported here:
> > https://lists.mcs.anl.gov/pipermail/petsc-users/2016-March/028803.html
> To
> > solve this problem, developers recommended to replace -lmkl_sequential to
> > -lmkl_intel_thread options in PETSC_ARCH/lib/conf/petscvariables.
> However,
> > I did not find something that would be named like this (it might be a
> > change of version)
> > - Anyway, I replaced lmkl_sequential to lmkl_intel_thread in every file
> of
> > PETSC, but it changed nothing.
> >
> > As a result, in the new make.log (here attached ) I have a parameter
> > #define PETSC_HAVE_LIBMKL_SEQUENTIAL 1 and option -lmkl_sequential
> >
> > Do you have any idea of what I should change in the initial options in
> > order to obtain the blas multithreding parallelization?
> >
> > Thanks a lot for your help!
> >
> > Ivan
> >
> >
> >
> >
> >
> >
> > On Fri, Nov 16, 2018 at 1:25 AM Dave May <dave.mayhem23 at gmail.com>
> wrote:
> >
> > >
> > >
> > > On Thu, 15 Nov 2018 at 17:44, Ivan via petsc-users <
> > > petsc-users at mcs.anl.gov> wrote:
> > >
> > >> Hi Stefano,
> > >>
> > >> In fact, yes, we look at the htop output (and the resulting
> computational
> > >> time ofc).
> > >>
> > >> In our code we use MUMPS, which indeed depends on blas / lapack. So I
> > >> think this might be it!
> > >>
> > >> I will definetely check it (I mean the difference between our MUMPS,
> > >> blas, lapack).
> > >>
> > >> If you have an idea of how we can verify on his PC that the source of
> his
> > >> parallelization does come from BLAS, please do not hesitate to tell
> me!
> > >>
> > >
> > > Option 1/
> > > * Set this environment variable
> > > export OMP_NUM_THREADS=1
> > > * Re-run your "parallel" test.
> > > * If the performance differs (job runs slower) compared with your
> previous
> > > run where you inferred parallelism was being employed, you can safely
> > > assume that the parallelism observed comes from threads
> > >
> > > Option 2/
> > > * Re-configure PETSc to use a known BLAS implementation which does not
> > > support threads
> > > * Re-compile PETSc
> > > * Re-run your parallel test
> > > * If the performance differs (job runs slower) compared with your
> previous
> > > run where you inferred parallelism was being employed, you can safely
> > > assume that the parallelism observed comes from threads
> > >
> > > Option 3/
> > > * Use a PC which does not depend on BLAS at all,
> > > e.g. -pc_type jacobi -pc_type bjacobi
> > > * If the performance differs (job runs slower) compared with your
> previous
> > > run where you inferred parallelism was being employed, you can safely
> > > assume that the parallelism observed comes from BLAS + threads
> > >
> > >
> > >
> > >> Thanks!
> > >>
> > >> Ivan
> > >> On 15/11/2018 18:24, Stefano Zampini wrote:
> > >>
> > >> If you say your program is parallel by just looking at the output from
> > >> the top command, you are probably linking against a multithreaded blas
> > >> library
> > >>
> > >> Il giorno Gio 15 Nov 2018, 20:09 Matthew Knepley via petsc-users <
> > >> petsc-users at mcs.anl.gov> ha scritto:
> > >>
> > >>> On Thu, Nov 15, 2018 at 11:59 AM Ivan Voznyuk <
> > >>> ivan.voznyuk.work at gmail.com> wrote:
> > >>>
> > >>>> Hi Matthew,
> > >>>>
> > >>>> Does it mean that by using just command python3 simple_code.py
> (without
> > >>>> mpiexec) you *cannot* obtain a parallel execution?
> > >>>>
> > >>>
> > >>> As I wrote before, its not impossible. You could be directly calling
> > >>> PMI, but I do not think you are doing that.
> > >>>
> > >>>
> > >>>> It s been 5 days we are trying to understand with my colleague how
> he
> > >>>> managed to do so.
> > >>>> It means that by using simply python3 simple_code.py he gets 8
> > >>>> processors workiing.
> > >>>> By the way, we wrote in his code few lines:
> > >>>> rank = PETSc.COMM_WORLD.Get_rank()
> > >>>> size = PETSc.COMM_WORLD.Get_size()
> > >>>> and we got rank = 0, size = 1
> > >>>>
> > >>>
> > >>> This is MPI telling you that you are only running on 1 processes.
> > >>>
> > >>>
> > >>>> However, we compilator arrives to KSP.solve(), somehow it turns on 8
> > >>>> processors.
> > >>>>
> > >>>
> > >>> Why do you think its running on 8 processes?
> > >>>
> > >>>
> > >>>> This problem is solved on his PC in 5-8 sec (in parallel, using
> *python3
> > >>>> simple_code.py*), on mine it takes 70-90 secs (in sequantial, but
> with
> > >>>> the same command *python3 simple_code.py*)
> > >>>>
> > >>>
> > >>> I think its much more likely that there are differences in the solver
> > >>> (use -ksp_view to see exactly what solver was used), then
> > >>> to think it is parallelism. Moreover, you would never ever ever see
> that
> > >>> much speedup on a laptop since all these computations
> > >>> are bandwidth limited.
> > >>>
> > >>> Thanks,
> > >>>
> > >>> Matt
> > >>>
> > >>>
> > >>>> So, conclusion is that on his computer this code works in the same
> way
> > >>>> as scipy: all the code is executed in sequantial mode, but when it
> comes to
> > >>>> solution of system of linear equations, it runs on all available
> > >>>> processors. All this with just running python3 my_code.py (without
> any
> > >>>> mpi-smth)
> > >>>>
> > >>>> Is it an exception / abnormal behavior? I mean, is it something
> > >>>> irregular that you, developers, have never seen?
> > >>>>
> > >>>> Thanks and have a good evening!
> > >>>> Ivan
> > >>>>
> > >>>> P.S. I don't think I know the answer regarding Scipy...
> > >>>>
> > >>>>
> > >>>> On Thu, Nov 15, 2018 at 2:39 PM Matthew Knepley <knepley at gmail.com>
> > >>>> wrote:
> > >>>>
> > >>>>> On Thu, Nov 15, 2018 at 8:07 AM Ivan Voznyuk <
> > >>>>> ivan.voznyuk.work at gmail.com> wrote:
> > >>>>>
> > >>>>>> Hi Matthew,
> > >>>>>> Thanks for your reply!
> > >>>>>>
> > >>>>>> Let me precise what I mean by defining few questions:
> > >>>>>>
> > >>>>>> 1. In order to obtain a parallel execution of simple_code.py, do I
> > >>>>>> need to go with mpiexec python3 simple_code.py, or I can just
> launch
> > >>>>>> python3 simple_code.py?
> > >>>>>>
> > >>>>>
> > >>>>> mpiexec -n 2 python3 simple_code.py
> > >>>>>
> > >>>>>
> > >>>>>> 2. This simple_code.py consists of 2 parts: a) preparation of
> matrix
> > >>>>>> b) solving the system of linear equations with PETSc. If I launch
> mpirun
> > >>>>>> (or mpiexec) -np 8 python3 simple_code.py, I suppose that I will
> basically
> > >>>>>> obtain 8 matrices and 8 systems to solve. However, I need to
> prepare only
> > >>>>>> one matrix, but launch this code in parallel on 8 processors.
> > >>>>>>
> > >>>>>
> > >>>>> When you create the Mat object, you give it a communicator (here
> > >>>>> PETSC_COMM_WORLD). That allows us to distribute the data. This is
> all
> > >>>>> covered extensively in the manual and the online tutorials, as
> well as the
> > >>>>> example code.
> > >>>>>
> > >>>>>
> > >>>>>> In fact, here attached you will find a similar code
> (scipy_code.py)
> > >>>>>> with only one difference: the system of linear equations is
> solved with
> > >>>>>> scipy. So when I solve it, I can clearly see that the solution is
> obtained
> > >>>>>> in a parallel way. However, I do not use the command mpirun (or
> mpiexec). I
> > >>>>>> just go with python3 scipy_code.py.
> > >>>>>>
> > >>>>>
> > >>>>> Why do you think its running in parallel?
> > >>>>>
> > >>>>> Thanks,
> > >>>>>
> > >>>>> Matt
> > >>>>>
> > >>>>>
> > >>>>>> In this case, the first part (creation of the sparse matrix) is
> not
> > >>>>>> parallel, whereas the solution of system is found in a parallel
> way.
> > >>>>>> So my question is, Do you think that it s possible to have the
> same
> > >>>>>> behavior with PETSC? And what do I need for this?
> > >>>>>>
> > >>>>>> I am asking this because for my colleague it worked! It means
> that he
> > >>>>>> launches the simple_code.py on his computer using the command
> python3
> > >>>>>> simple_code.py (and not mpi-smth python3 simple_code.py) and he
> obtains a
> > >>>>>> parallel execution of the same code.
> > >>>>>>
> > >>>>>> Thanks for your help!
> > >>>>>> Ivan
> > >>>>>>
> > >>>>>>
> > >>>>>> On Thu, Nov 15, 2018 at 11:54 AM Matthew Knepley <
> knepley at gmail.com>
> > >>>>>> wrote:
> > >>>>>>
> > >>>>>>> On Thu, Nov 15, 2018 at 4:53 AM Ivan Voznyuk via petsc-users <
> > >>>>>>> petsc-users at mcs.anl.gov> wrote:
> > >>>>>>>
> > >>>>>>>> Dear PETSC community,
> > >>>>>>>>
> > >>>>>>>> I have a question regarding the parallel execution of petsc4py.
> > >>>>>>>>
> > >>>>>>>> I have a simple code (here attached simple_code.py) which
> solves a
> > >>>>>>>> system of linear equations Ax=b using petsc4py. To execute it,
> I use the
> > >>>>>>>> command python3 simple_code.py which yields a sequential
> performance. With
> > >>>>>>>> a colleague of my, we launched this code on his computer, and
> this time the
> > >>>>>>>> execution was in parallel. Although, he used the same command
> python3
> > >>>>>>>> simple_code.py (without mpirun, neither mpiexec).
> > >>>>>>>>
> > >>>>>>> I am not sure what you mean. To run MPI programs in parallel, you
> > >>>>>>> need a launcher like mpiexec or mpirun. There are Python
> programs (like
> > >>>>>>> nemesis) that use the launcher API directly (called PMI), but
> that is not
> > >>>>>>> part of petsc4py.
> > >>>>>>>
> > >>>>>>> Thanks,
> > >>>>>>>
> > >>>>>>> Matt
> > >>>>>>>
> > >>>>>>>> My configuration: Ubuntu x86_64 Ubuntu 16.04, Intel Core i7,
> PETSc
> > >>>>>>>> 3.10.2, PETSC_ARCH=arch-linux2-c-debug, petsc4py 3.10.0 in
> virtualenv
> > >>>>>>>>
> > >>>>>>>> In order to parallelize it, I have already tried:
> > >>>>>>>> - use 2 different PCs
> > >>>>>>>> - use Ubuntu 16.04, 18.04
> > >>>>>>>> - use different architectures (arch-linux2-c-debug,
> > >>>>>>>> linux-gnu-c-debug, etc)
> > >>>>>>>> - ofc use different configurations (my present config can be
> found
> > >>>>>>>> in make.log that I attached here)
> > >>>>>>>> - mpi from mpich, openmpi
> > >>>>>>>>
> > >>>>>>>> Nothing worked.
> > >>>>>>>>
> > >>>>>>>> Do you have any ideas?
> > >>>>>>>>
> > >>>>>>>> Thanks and have a good day,
> > >>>>>>>> Ivan
> > >>>>>>>>
> > >>>>>>>> --
> > >>>>>>>> Ivan VOZNYUK
> > >>>>>>>> PhD in Computational Electromagnetics
> > >>>>>>>>
> > >>>>>>>
> > >>>>>>>
> > >>>>>>> --
> > >>>>>>> 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/>
> > >>>>>>>
> > >>>>>>
> > >>>>>>
> > >>>>>> --
> > >>>>>> Ivan VOZNYUK
> > >>>>>> PhD in Computational Electromagnetics
> > >>>>>> +33 (0)6.95.87.04.55
> > >>>>>> My webpage <https://ivanvoznyukwork.wixsite.com/webpage>
> > >>>>>> My LinkedIn <http://linkedin.com/in/ivan-voznyuk-b869b8106>
> > >>>>>>
> > >>>>>
> > >>>>>
> > >>>>> --
> > >>>>> 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/>
> > >>>>>
> > >>>>
> > >>>>
> > >>>> --
> > >>>> Ivan VOZNYUK
> > >>>> PhD in Computational Electromagnetics
> > >>>> +33 (0)6.95.87.04.55
> > >>>> My webpage <https://ivanvoznyukwork.wixsite.com/webpage>
> > >>>> My LinkedIn <http://linkedin.com/in/ivan-voznyuk-b869b8106>
> > >>>>
> > >>>
> > >>>
> > >>> --
> > >>> 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/>
> > >>>
> > >>
> >
> >
>
>
--
Ivan VOZNYUK
PhD in Computational Electromagnetics
+33 (0)6.95.87.04.55
My webpage <https://ivanvoznyukwork.wixsite.com/webpage>
My LinkedIn <http://linkedin.com/in/ivan-voznyuk-b869b8106>
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