Hi, Paraview is a great and handy tool. I am now trying to scale it to large data sets and have a question related to the parallel capabilities of the Paraview-XDMF-Reader:
We use a PIC-Code that devides its grid into blocks distributed over the ranks.
For the output every rank writes exactly one HDF5, collecting all local blocks
and all time steps.
One XDMF per rank was written describing the data, as well as a central XDMF
including all the individual ones.
The hierarchy is
central-xdmf: spatial_collection->includes_for_rank_xdmf_files
rank_xdmf_files:
temporal_collection->spatial_collectio_within_ranks->grids
The size of the simulation is about 1024 ranks and 256 time steps but should be
increased.
For these parameters we see (via top) a memory consumption of 16GB per pvserver
instance.
Directly after opening of the file, so even before "apply".
I guess that this is because all the pvserver instances read and parse the XDMF
file?
One time the paths to the HDF5 files were wrong, the memory consumption was the
same.
After "apply" there was than an error.
I tried "PV_USE_TRANSMIT=1" and also changed the grid hierarchy to only have:
temporal_collection->spatial_collection->grids
This directly in one file, that was finally 1GB on disk and around 16GB in
memory with lxml2 via python.
But it was to no effort, still every instance was consuming around 16GB
Is there any chance that pvserver can parallelize on the top-level so every
pvserver instance only reads some of the "include" files.
Or is there a different approach to store the grid-patches (all the same
resolution right now) in HDF5?
All suggestions are highly appreciated :-)
Thank you all very much for any support,
Best regards
--
Dipl.-Phys. Karl-Ulrich Bamberg
Ludwig-Maximilians-Universität München
Arnold-Sommerfeld-Center (ASC)
Computational & Plasma Physics
Theresienstr. 37, D-80333 München
phone: +49 (0)89 2180 4577
fax: +49 (0)89 2180 99 4577
e-mail: [email protected]
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