dolfin team mailing list archive

["Martin Sandve Alnæs" <martinal@xxxxxxxxx>]

On Fri, Dec 19, 2008 at 9:08 PM, Anders Logg <logg@xxxxxxxxx> wrote:
> On Fri, Dec 19, 2008 at 01:01:50PM +0100, Martin Sandve Alnæs wrote:
>> On Fri, Dec 19, 2008 at 11:54 AM, Anders Logg <logg@xxxxxxxxx> wrote:
>> > 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.
>> >
>>
>> Basically, it looks like class MPI can be renamed to Communicator.
>> All its functions must be made non-static, and it should have a member
>> variable to hold the communicator ID. All occurences of MPI_COMM_WORLD
>> in MPI.cpp should be replaced with the communicator ID variable.
>> And all other places in the code where MPI_COMM_WORLD is needed,
>> a communicator must be used instead (i.e. distribute).
>>
>> The communicator design will also affect the linear algebra backend,
>> depending on how those libraries do this... The persons who design
>> this should probably take a look at how both PETSc and Epetra
>> handle this to get ideas.
>>
>> But I don't have time to really get involved with this now.
>> It's not like this is a simple thing to do on the side ;)
>>
>> Martin
>
> I don't understand the point. The functions in MPI are static by
> nature, how many processes, current process number etc.

Those functions depend on MPI_COMM_WORLD, which
is the group of all processes. If you want to run one part
of the application on a subset of the processes, you wouldn't
want to use the current MPI::gather etc, since it works with
all processes. Instead you can define a communicator for
that subset of processes, and "how many processes",
"current process number" etc is defined within that context.

> But we do need something extra (the communicator) which can be shared
> between objects that should share communication pattern.

A "communicator" is a MPI term, and doesn't really involve
anything I would associate with a "communication pattern",
it's just a named group of processes.

> We'll get to this in time, unless someone has time to think about it
> now, and provide the implementation... ;-)

Since current MPI ~ future Communicator the change
should probably be fairly easy to do later on.

Martin