It sounds like M is equal to the number of processors (pipelines) and
M >> N. Is that correct?

-berk

On Fri, Dec 11, 2009 at 10:40 AM, Biddiscombe, John A. <[email protected]> wrote:
> Berk
>
> The data will be UnstructuredGrid for now. Multiblock, but actually, I don't 
> really care what each block is, only that I accept one block on each of N 
> processes, split it into more pieces, and the next filter accepts one (or 
> more if the numbers don't match up nicely) blocks and process them. The 
> redistribution shouldn't care what data types, only how many blocks in and 
> out.
>
> Looking at RedistributePolyData makes me realize my initial idea is no good. 
> In my mind I had a pipeline where multiblock datasets are passed down the 
> pipeline and simply the number of pieces is manipulated to achieve what I 
> wanted - but I see now that if I have M pieces downstream mapped upstream to 
> N pieces, what will happen is the readers will be effectively duplicated and 
> M/N readers will read the same pieces. I don't want this to happen as IO will 
> be a big problem if readers read the same blocks M/N times.
> I was hoping there was a way of simply instructing the pipeline to manage the 
> pieces, but I see now that this won't work, as there needs to be a specific 
> Send from each N to their M/N receivers (because the data is physically in 
> another process, so the pipeline can't see it). This is very annoying as 
> there must be a class which already does this (block redistribution, rather 
> than polygon level redistribution), and I would like it to be more 'pipeline 
> integrated' so that the user doesn't have to explicitly send each time an 
> algorithm needs it.
>
> I'll go through RedistributePolyData in depth and see what I can pull out of 
> it - please feel free to steer me towards another possibility :)
>
> JB
>
>
>> -----Original Message-----
>> From: Berk Geveci [mailto:[email protected]]
>> Sent: 11 December 2009 16:09
>> To: Biddiscombe, John A.
>> Cc: [email protected]
>> Subject: Re: [Paraview] Parallel Data Redistribution
>>
>> What is the data type? vtkRedistributePolyData and its subclasses do
>> this for polydata. It can do load balancing (where you can specify a
>> weight for each processor) as well.
>>
>> -berk
>>
>> On Fri, Dec 11, 2009 at 9:59 AM, Biddiscombe, John A. <[email protected]>
>> wrote:
>> > I have a filter pipeline which reads N blocks from disk, this works fine
>> on N processors.
>> >
>> > I now wish to subdivide those N blocks (using a custom filter) to produce
>> new data which will consist of M blocks - where M >> N.
>> >
>> > I wish to run the algorithm on M processors and have the piece information
>> transformed between the two filters (reader -> splitter), so that blocks are
>> distributed correctly. The reader will Read N blocks (leaving M-N processes
>> unoccupied), but the filter which splits them up needs to output a different
>> number of pieces and have the full M processes receiving data.
>> >
>> > I have a reasonably good idea of how to implement this, but I'm wondering
>> if any filters already do something similar. I will of course take apart the
>> D3 filter for ideas, but I don't need to do a parallel spatial decomposition
>> since my blocks are already discrete - I just want to redistribute the
>> blocks around and more importantly change the numbers of them between
>> filters.
>> >
>> > If anyone can suggest examples which do this already, please do
>> >
>> > Thanks
>> >
>> > JB
>> >
>> > --
>> > John Biddiscombe,                            email:biddisco @ cscs.ch
>> > http://www.cscs.ch/
>> > CSCS, Swiss National Supercomputing Centre  | Tel:  +41 (91) 610.82.07
>> > Via Cantonale, 6928 Manno, Switzerland      | Fax:  +41 (91) 610.82.82
>> >
>> >
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