dolfin team mailing list archive

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

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
>>     _______________________________________________
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>>
>>
>> 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


> we wait until we get all pieces in place. We currently have parallel
> parsing and partitioning working. Next step will be to get parallel
> assembly working, then the parallel solve (presumably simple since
> it's handled by PETSc or Epetra).
>
> --
> Anders