I appear to be running into the same problem with my programmable
filter. Since the Transform filter cannot scale rectilinear data, I
wrote the following programmable filter to do it:
zscale = 0.001
pdi = self.GetInput()
pdo = self.GetOutput()
pdo.ShallowCopy(pdi)
zsi = pdi.GetZCoordinates()
zso = vtk.vtkDoubleArray()
zso.DeepCopy(zsi)
zss = zso.GetSize()
for i in xrange(zss):
zso.SetValue(i, zsi.GetValue(i)*zscale)
pdo.SetZCoordinates(zso)
Obviously, I need to update it to use the newer input/output names and
numpy arrays for speed, but it does work in serial. However, it appears
to duplicate every point on every processor in parallel. I've been
poring over the docs and experimenting, but I've yet to find a way to
use UPDATE_EXTENT properly in parallel with rectilinear data. Any ideas?
Thanks,
Sean
On 08/11/11 10:33, David E DeMarle wrote:
You should end up with one multiblock dataset on each processor, all
of those should have eight children. On any given processor 7 of those
children will be NULL and the remaining one will be unique to that
processor. Use UPDATE_PIECE and possibly localprocessid to figure out
which of the eight children the processor should fill in. The rest of
the vtkCompositeDataPipeline that ParaView uses expects and knows how
to handle that structure and filters downstream should have no problem
handling it.
And no these aren't stupid questions. They are described fairly well
in the most recent kitware books and courses but otherwise the
information is widely scattered around the paraview wiki, kitware
source magazine and the mailing list archives.
David E DeMarle
Kitware, Inc.
R&D Engineer
28 Corporate Drive
Clifton Park, NY 12065-8662
Phone: 518-371-3971 x109
On Thu, Aug 11, 2011 at 11:11 AM, Tim Gallagher
<[email protected]> wrote:
David,
Thanks for your response. It's much clearer how it all works, but I'm still
unsure how it fits together.
I don't actually need to know the interprocess links -- I have a list of blocks
to read and that list needs to be split over the processors. So each processor
needs to identify itself and the total number of procs, but that's all. So I
can definitely do that with the mpi4py, I was unaware that would work inside
the filter and I didn't know the paraview.vtk.parallel existed.
I'm not actually splitting the structured data; I'm splitting the
vtkMultiBlockDataSet. So each processor is responsible for populating a portion
of the dataset. For instance, in serial when the file (say, with 8 blocks) is
read, we end up with one vtkMultiBlockDataset with 8 vtkStructuredData's inside
it. If I have a parallel reader (with 8 processes), I have a hunch I'll end up
with 8 vtkMultiBlockDataSet's with one vtkStructuredData under each. Is this
correct? Will this cause problems for other filters downstream? If for fun, I
wanted to merge it such that each processor still only retains it's block, but
they share a common parent vtkMultiBlockDataset, is that possible?
I appreciate your help with this. Maybe these are stupid questions answered
somewhere else, but I can't seem to find them!
Tim
----- Original Message -----
From: "David E DeMarle"<[email protected]>
To: [email protected]
Cc: "ParaView list"<[email protected]>
Sent: Thursday, August 11, 2011 9:54:24 AM
Subject: Re: [Paraview] Programmable filter in parallel
ParaView tries to do no aggregation other than rendering onto the same
screen. Each processor is told what portion it is responsible for via
the UPDATE_EXTENT or UPDATE_PIECE/UPDATE_NUMBER_OF_PIECES keys and are
supposed to only produce what it is asked for. (See
http://paraview.org/Wiki/Writing_ParaView_Readers for more of the
story.)
Filters that need cross communication to work properly (beyond what
they can get from ghost cells) do so by accessing the
vtkMultiProcessController that connects all of the nodes in the server
(or sometimes via MPI directly but that isn't recommended).
Try the following for two means of getting a hold of the interprocess links.
import paraview.vtk.parallel
#print(dir(paraview.vtk.parallel))
#print(dir(paraview.vtk.parallel.vtkMultiProcessController))
controller =
paraview.vtk.parallel.vtkMultiProcessController.GetGlobalController()
print controller.GetLocalProcessId()
print controller.GetNumberOfProcesses()
from mpi4py import MPI
#print(dir(MPI))
#print(help(MPI))
print MPI.COMM_WORLD.Get_rank()
print MPI.COMM_WORLD.Get_size()
Note also that there is a "feature" in the python programmable filter
that comes into play with structured data. That feature says that
structured data is not split at all by default. If you want structured
data to actually be parallel you need to put this code in your python
programmable filter.
from paraview import util
self.GetExecutive().SetExtentTranslator(self.GetExecutive().GetOutputInformation(0),
vtk.vtkExtentTranslator())
David E DeMarle
Kitware, Inc.
R&D Engineer
28 Corporate Drive
Clifton Park, NY 12065-8662
Phone: 518-371-3971 x109
On Wed, Aug 3, 2011 at 11:09 AM, Tim Gallagher<[email protected]> wrote:
I guess I sort of answered my own question -- the entire script runs on each
processor, so I ended up with 8 copies of my data in memory (or I would have,
had I not filled the 12 GB of RAM and 20 GB of swap space and my system
crashed).
So is there some way to query the processor information? Probably something in
the RequestInformation script -- find out how many processors there are and
then the prog. filter determines based on processor ID and number of processors
what section of the data to load.
In that case, how does the aggregation of the data work? The exact pipeline is:
DataObjectGenerator("MB{}")
ProgrammableFilter
in serial, the PF appends blocks into the input and passes that through to the
output. In parallel, that same pipeline would create a MB{} on each CPU that
gets filled with that CPU's data, but at the end of this step I would want a
single MB{} object, not NCPU MB{}'s.
Hopefully that makes sense... I've never used PV in parallel, so I'm not sure
how it all works.
Tim
----- Original Message -----
From: "Tim Gallagher"<[email protected]>
To: "ParaView list"<[email protected]>
Sent: Wednesday, August 3, 2011 9:24:25 AM
Subject: [Paraview] Programmable filter in parallel
Hi,
I know many of the built-in readers/filters already work in parallel, but how
does one write a parallel programmable filter?
Our data files are XDMF and split into blocks of data. We have a single XDMF
file that we can read that reads all the blocks and generates a
vtkMultiBlockDataset (this works with the built in XDMF reader).
However, each block has some ghost cells around it that are needed to do the
CellDataToPointData interpolation. For large numbers of blocks, this creates
far too many grid points for our machines to load. So, I've written a
programmable filter that does:
start with empty vtkMultiBlockDataset
for each block in restart file
read block file with XDMFReader
CellDataToPointData
strip off the extra layers of cells
append to output vtkMultiBlockDataset
If I run this in parallel, what exactly is parallel? Is the reading and CD2PD
done in parallel on each block? Is none of it parallel? Ideally, I would have
the loop over blocks done in parallel, but I don't know how to indicate that in
the programmable filter (if it's possible).
Any advice would be great,
Tim
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Powered by www.kitware.com
Visit other Kitware open-source projects at
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Please keep messages on-topic and check the ParaView Wiki at:
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Follow this link to subscribe/unsubscribe:
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_______________________________________________
Powered by www.kitware.com
Visit other Kitware open-source projects at
http://www.kitware.com/opensource/opensource.html
Please keep messages on-topic and check the ParaView Wiki at:
http://paraview.org/Wiki/ParaView
Follow this link to subscribe/unsubscribe:
http://www.paraview.org/mailman/listinfo/paraview
_______________________________________________
Powered by www.kitware.com
Visit other Kitware open-source projects at
http://www.kitware.com/opensource/opensource.html
Please keep messages on-topic and check the ParaView Wiki at:
http://paraview.org/Wiki/ParaView
Follow this link to subscribe/unsubscribe:
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