[yt-users] 14-hour load time for Enzo dataset with VR, vs. 30 minutes with ProjectionPlot?

[Cameron Hummels]

For reference, I've definitely noticed that doing VR or
off_axis_projections with moderately sized datasets take about 5-10x longer
than ProjectionPlots of the same dataset.  Last week I was playing with a
non-cosmological enzo dataset that is 1.6GB in total size, and it took
about 3-4 minutes to do an off_axis_projection, whereas it took about 20
seconds to do a ProjectionPlot.

On Thu, Mar 3, 2016 at 1:47 PM, Nathan Goldbaum <nathan12343 at gmail.com>
wrote:

> I don't think too many people have done a volume rendering this big, so
> you're likely hitting scaling issues that haven't been looked at closely.
>
> Have you tried doing any sort of parallel volume rendering? yt supports
> decomposing in the image plane in parallel using the MosaicCamera.
>
> -Nathan
>
> On Thu, Mar 3, 2016 at 3:38 PM, Stuart Levy <salevy at illinois.edu> wrote:
>
>> Hello yt people,
>>
>> We're trying to render imagery of a pretty large Enzo snapshot (~160GB,
>> in 330,000 grids in 512 HDF5 domains) with yt-3.3dev.
>>
>> On a reasonably fast Linux machine, we can do a ProjectionPlot of a few
>> variables in about 30 minutes, running single-threaded while it scans the
>> data (which is what takes most of the time).   Data access pattern: we see
>> it reading through each of the HDF5 files in numerical order (cpu0000,
>> cpu0001, ...), taking a few seconds each, and opening each file exactly
>> once.
>>
>> On the same machine and same dataset, using the volume rendering API, the
>> data-scanning process takes about* 14 hours* (not counting any rendering
>> time).   (On Blue Waters, Kalina using a similar dataset couldn't get it to
>> finish within a 24-hour wall-clock limit.)   Data access pattern: it opens
>> an HDF5 file many times in quick succession, then opens another, then opens
>> the previous file a bunch more times.  I'm guessing it grabs one AMR grid
>> from each HDF5 open:
>>
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0074",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0075",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0357",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0357",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0357",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0357",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0357",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0357",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0074",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0075",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0235",
>> O_RDONLY) = 3
>> open("/fe0/deslsst/renaissance/normal/RD0074/RedshiftOutput0074.cpu0357",
>> O_RDONLY) = 3
>>
>> This is trouble.  Is there anything we can do to make load times less
>> extravagant when using VR on Enzo?   What if we ran "ds.index" before
>>
>> I tried running cProfile on it, as in
>>    python -m cProfile myscript.py ...
>> Happy to point anyone at the dataset on our systems or BW, but at this
>> scale it's not a very portable problem.
>>
>>
>>
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>>
>>
>
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-- 
Cameron Hummels
NSF Postdoctoral Fellow
Department of Astronomy
California Institute of Technology
http://chummels.org
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