Hi Dave,

Thanks for the insight and great citation, thanks for pointing it out.

Burlen

David E DeMarle wrote:
This is what I was thinking of:
*Parallel Hierarchical Visualization of Large Time-varying 3D Vector Fields*

*Hongfeng Yu, Chaoli Wang, Kwan-Liu Ma

*David E DeMarle
Kitware, Inc.
R&D Engineer
28 Corporate Drive
Clifton Park, NY 12065-8662
Phone: 518-371-3971 x109


On Tue, Aug 25, 2009 at 9:46 AM, David E DeMarle <[email protected] <mailto:[email protected]>> wrote:

    There was a paper at Super Computing last year (or maybe the year
    before) about improved partitioning for stream tracing that would
    probably help alot. Think of making a filter like D3 which moves
    data around to repartition, such that the partitions take into
    account the principle flow directions. That way particles stay
    resident much more often. The upfront cost might be high to do the
    repartition, but afterwards stream tracing would be faster.

    David E DeMarle
    Kitware, Inc.
    R&D Engineer
    28 Corporate Drive
    Clifton Park, NY 12065-8662
    Phone: 518-371-3971 x109


    On Tue, Aug 25, 2009 at 4:42 AM, John Biddiscombe
    <[email protected] <mailto:[email protected]>> wrote:

        Burlen

        I have had performance issues with the Distributed Stream
        tracer, but in fact I found that in general, the problem of it
        not being very well optimized for parallel operation was not
        the main trouble. If you are using Unstructured Grids, and
        they are large (in my case 20million cells in a block), then
        the main time was taken by the building of cell links which
        are used to FindCEll inwhich an integration point lies. I
        modified the stream tracer interpolation to use a BSP tree (or
        CellLocator) and found a huge improvement in execution time.
        (minutes instead of hours).

        Secondly. the parallelization of the stream tracer is an
        inherent problem. One cannot integrate the streamline in block
        2, until it has reached a boundary in block 1 - one must wait
        until the streamling traverses one block before passing it to
        the next. In actuality, the implementation could be improved
        with more intelligent seeding and rending/receiving of
        streamline seeds etc between iterations.

        The Particle tracer code could be modifed to produce
        streamlines in a serial or distributed manner and ought to
        give a 'reasonably' optimal solution to the problem - but in
        fact the chaps at kitware are at the moment (they tell me) in
        the process of revamping the streamline code to make use of
        CellLocators - and for this reason I recently committed my BSP
        tree code.

        Here's how to check your bottleneck.
        Find a large StructuredGrid dataset which is loaded in
        parallel. Generate streamlines. Time it. Convert the grdi to
        UnstructuredGrid and do the same. If test 1 takes 1 minute and
        test 2 1 hour, then it isn't the parallization that's the real
        issue, but the grid being used.

        JB





            We've been using the distributed stream tracer to generate
            100s-1000s of stream lines per time step. It's very slow,
            and it doesn't scale at all.  The class comments say as
            much. I'm sure there is a reason why this implementation
            was chosen. Is there something that generally prevents
            real parallel implementation? Is there a better
            implementation available out there?

            There is this post a while back
            http://www.paraview.org/pipermail/paraview/2009-July/012959.html

            What's the status?

            Thanks
            Burlen







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