dolfin team mailing list archive

[Anders Logg <logg@xxxxxxxxx>]

On Fri, Dec 19, 2008 at 09:23:43AM +0100, Martin Sandve Alnæs wrote:
> On Fri, Dec 19, 2008 at 9:12 AM, Anders Logg <logg@xxxxxxxxx> wrote:
> > On Fri, Dec 19, 2008 at 09:06:43AM +0100, Martin Sandve Alnæs wrote:
> >> On Fri, Dec 19, 2008 at 8:54 AM, Anders Logg <logg@xxxxxxxxx> wrote:
> >> > On Fri, Dec 19, 2008 at 12:09:50PM +0900, Evan Lezar wrote:
> >> >>
> >> >>
> >> >> On Thu, Dec 18, 2008 at 4:25 AM, Johan Hake <hake@xxxxxxxxx> wrote:
> >> >>
> >> >>     On Wednesday 17 December 2008 20:19:52 Anders Logg wrote:
> >> >>     > On Wed, Dec 17, 2008 at 08:13:03PM +0100, Johan Hake wrote:
> >> >>     > > On Wednesday 17 December 2008 19:20:11 Anders Logg wrote:
> >> >>     > > > Ola and I have now finished up the first round of getting DOLFIN to
> >> >>     > > > run in parallel. In short, we can now parse meshes from file in
> >> >>     > > > parallel and partition meshes in parallel (using ParMETIS).
> >> >>     > > >
> >> >>     > > > We reused some good ideas that Niclas Jansson had implemented in
> >> >>     > > > PXMLMesh before, but have also made some significant changes as
> >> >>     > > > follows:
> >> >>     > > >
> >> >>     > > > 1. The XML reader does not handle any partitioning.
> >> >>     > > >
> >> >>     > > > 2. The XML reader just reads in a chunk of the mesh data on each
> >> >>     > > > processor (in parallel) and stores that into a LocalMeshData object
> >> >>     > > > (one for each processor). The data is just partitioned in blocks so
> >> >>     > > > the vertices and cells may be completely unrelated.
> >> >>     > > >
> >> >>     > > > 3. The partitioning takes place in MeshPartitioning::partition,
> >> >>     > > > which gets a LocalMeshData object on each processor. It then calls
> >> >>     > > > ParMETIS to compute a partition (in parallel) and then redistributes
> >> >>     > > > the data accordingly. Finally, a mesh is built on each processor
> >> >>     using
> >> >>     > > > the local data.
> >> >>     > > >
> >> >>     > > > 4. All direct MPI calls (except one which should be removed) have
> >> >>     been
> >> >>     > > > removed from the code. Instead, we mostly rely on
> >> >>     > > > dolfin::MPI::distribute which handles most cases of parallel
> >> >>     > > > communication and works with STL data structures.
> >> >>     > > >
> >> >>     > > > 5. There is just one ParMETIS call (no initial geometric
> >> >>     > > > partitioning). It seemed like an unnecessary step, or are there good
> >> >>     > > > reasons to perform the partitioning in two steps?
> >> >>     > > >
> >> >>     > > > For testing, go to sandbox/passembly, build and then run
> >> >>     > > >
> >> >>     > > >   mpirun -n 4 ./demo
> >> >>     > > >   ./plot_partitions 4
> >> >>     > >
> >> >>     > > Looks beautiful!
> >> >>     > >
> >> >>     > > I threw a 3D mesh of 160K vertices onto it, and it was partitioned
> >> >>     nicely
> >> >>     > > in some 10 s, on my 2 core laptop.
> >> >>     > >
> >> >>     > > Johan
> >> >>     >
> >> >>     > Nice, in particular since we haven't run any 3D test cases ourselves,
> >> >>     > just a tiny mesh of the unit square... :-)
> >> >>
> >> >>     Yes I thought so too ;)
> >> >>
> >> >>     Johan
> >> >>     _______________________________________________
> >> >>     DOLFIN-dev mailing list
> >> >>     DOLFIN-dev@xxxxxxxxxx
> >> >>     http://www.fenics.org/mailman/listinfo/dolfin-dev
> >> >>
> >> >>
> >> >> While we are on the topic of parallelisation - I have some comments.
> >> >>
> >> >> A couple of months ago I was trying to parallelise some of my own code and ran
> >> >> into some problems with the communicators used for PETSc and SLEPc problems - I
> >> >> sort of got them to work, but never commited my changes because they were a
> >> >> little ungainly and I felt I needed to spend some more time on them.
> >> >>
> >> >> One thing that I did notice is that the user does not have much control over
> >> >> which parts of the process run in parallel - with the number of MPI processes
> >> >> deciding whether or not a parallel implementation should be used.  In my case
> >> >> what I wanted to do was perform a frequency sweep and for each frequency point
> >> >> perform the same calculation (an eigenvalue problem) for that frequency.  My
> >> >> intention was to distribute the frequency sweep over a number of processors and
> >> >> then handle the assembly and solution of each system separately.  This is not
> >> >> possible as the code is now since the assembly and eigenvalue solvers all try
> >> >> to run in parallel as soon as the applicaion is run as an mpi program.
> >> >>
> >> >> I know that this is not very descriptive, but does anyone else have thoughts on
> >> >> the matter?  I will put together something a little more concrete as soon as I
> >> >> have a chance (I am travelling around quite a bit at the moment so it is
> >> >> difficult for me to focus).
> >> >
> >> > We're just getting started. Everything needs to be configurable. At
> >> > the moment, we assume that all parallel computation should be split in
> >> > MPI::num_processes().
> >> >
> >> > We can either add optional parameters to all parallel calls or add
> >> > global options to control how many processes are used. But I suggest
> >>
> >> Global options wouldn't solve anything (the point here is variable
> >> number of processes during the application lifetime), and it's probably
> >> enough with a single parameter, the communicator, perhaps permanently
> >> attached to each FunctionSpace (dofmap must be distributed over
> >> the processes in a communicator to be usable). Calls to MPI::num_processes()
> >> would be replaced by V.communicator().num_processes() or something.
> >> I get the waiting argument though. When stuff works, a search for MPI::*
> >> should reveal the places where "V.communicator()." should replace "MPI::",
> >> likely covering most places that need to be updated.
> >>
> >> Martin
> >
> > Sounds good.
> >
> > Speaking of the communicator, I'd like to add access to a default
> > communicator in the MPI class which could be accessed by
> > MPI::communicator() but couldn't figure out how to do this (I suspect
> > it's trivial).
> >
> > Then we could avoid including mpi.h in MeshPartitioning.cpp (look for
> > the two first FIXMEs in that file). If anyone knows how to do this,
> > let me know.
> 
> You mean something like
> 
> class MPI {
> public:
> 
>   static dolfin::Communicator & communicator()
>   { return *mainCommunicator; }
> 
> private:
>   static shared_ptr<Communicator> mainCommunicator;
> };
> 
> ?
> 
> This can then be initialized the first time MPI::communicator()
> is called, to be a communicator including all MPI processes.
> 
> Epetra has a "serial communicator", which is probably needed
> as well when MPI is not in use, instead of adding #ifdefs all
> places MPI is used.
> 
> Martin

Yes, something like that. But what would the Communicator class look
like? If you know how to do this, feel free to just add it.

-- 
Anders

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