[petsc-users] petsc4py help with parallel execution
Ivan
ivan.voznyuk.work at gmail.com
Thu Nov 15 11:43:33 CST 2018
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!
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 <mailto: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 <mailto: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 <mailto:knepley at gmail.com>> wrote:
>
> On Thu, Nov 15, 2018 at 8:07 AM Ivan Voznyuk
> <ivan.voznyuk.work at gmail.com
> <mailto: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 <mailto: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
> <mailto: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/>
>
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