[yt-users] domain-decomposition for volume rendering huge brick?

Sam Skillman samskillman at gmail.com
Sun Nov 23 12:55:27 PST 2014 

Hi Stuart,

I just tried to do the same thing on a fake 8GB file and am seeing similar
things. After killing the job, it exited while doing this:
^CTraceback (most recent call last):
  File "test_load.py", line 11, in <module>
    ds = yt.load_uniform_grid(data, (1024, 1024, 1024))
  File
"/home/skillman/local/yt-x86_64/src/yt-3.0/yt/frontends/stream/data_structures.py",
line 605, in load_uniform_grid
    field_units, data = unitify_data(data)
  File
"/home/skillman/local/yt-x86_64/src/yt-3.0/yt/frontends/stream/data_structures.py",
line 494, in unitify_data
    data = dict((field, np.array(val)) for field, val in data.iteritems())
  File
"/home/skillman/local/yt-x86_64/src/yt-3.0/yt/frontends/stream/data_structures.py",
line 494, in <genexpr>
    data = dict((field, np.array(val)) for field, val in data.iteritems())
KeyboardInterrupt

Looking at line 605 in yt/frontends/stream/data_structures.py, it was
choking when trying to "unitify_data". However, if you go in and change
line 494 from:
 data = dict((field, np.array(val)) for field, val in data.iteritems())
to
 data = dict((field, val) for field, val in data.iteritems())



On Fri Nov 21 2014 at 3:07:49 PM Stuart Levy <salevy at illinois.edu> wrote:

>  OK, so I have experimented, though not much.
>
> First, sheepishly: I was off by a decimal place in the original file
> size.   It's a unigrid with 1.5e10, not 1.5e11 voxels - big enough to be a
> nuisance but not heroic.
>
> Second: load_uniform_grid() on a big numpy.memmap()'ed file, even a modest
> 8GB fraction of the full grid, takes a long time - many tens of minutes?
> I ran out of time slice before it finished even doing that.   Note this was
> just calling load_uniform_grid(), not any attempt at calculation yet.
>
> Speculation: something sweeps through the memory, causes a page fault,
> sweeps a bit more, another page fault, etc.  So there'd be many small I/O
> calls triggered sequentially, wasting lots of time.   Could that be?  If
> so, then I'm wondering if it could be possible to discover which portions
> of the array will be in each node's domain, and prefetch those in bulk
> first, using a few very efficient huge I/O calls (maybe via madvise()).
>
> Either that, or if I can do my own domain decomposition up front and
> *tell* the AMRKDTree which nodes own which slabs of grid, then I could just
> read() them in - also efficiently - and let yt do any further
> decomposition, maybe.
>
> Does either route make sense?   Is there code I should look at?
>
> Thanks as ever
>
>
>     Stuart
>
>
> On 11/7/14 1:33 PM, Sam Skillman wrote:
>
> Yep, the volume rendering should build the AMRKDTree itself, and *should*
> automatically decompose the giant brick into Np pieces. As for memory, you
> may need to (eek) allow for yt casting to 64-bit floats for the data, but
> you'll have to just experiment a bit.
>
>  Sam
>
> On Fri Nov 07 2014 at 11:15:13 AM Stuart Levy <salevy at illinois.edu> wrote:
>
>>  Thank you, Sam!   I think this makes sense.   Except, in case (1), do I
>> need to do something to bring the AMRKDTree into the picture?   Or are you
>> telling me that it is automatically constructed whenever you
>> load_uniform_grid(), or volume-render it?
>>
>> I think the available nodes have 64GB, so to load the whole ~600GB might
>> take at least 32 nodes or 1024 cores.
>>
>> Will let you know how it goes!
>>
>>
>> On 11/7/14 11:08 AM, Sam Skillman wrote:
>>
>> Ack, my calculation of 256-512 cores is probably low... feel free to push
>> up much higher.
>>
>> On Fri Nov 07 2014 at 9:03:51 AM Sam Skillman <samskillman at gmail.com>
>> wrote:
>>
>>> Hi Stuart,
>>>
>>>  On Thu Nov 06 2014 at 8:36:28 AM Stuart Levy <salevy at illinois.edu>
>>> wrote:
>>>
>>>> Hello all,
>>>>
>>>> We're hoping to use yt parallel volume rendering on a very large generic
>>>> brick - it's a simple rectangular unigrid slab, but containing something
>>>> like 1.5e11 points, so much too large for load_uniform_grid() to load
>>>> into memory in a single machine.
>>>>
>>>
>>>    Are you loading directly using something like numpy.fromfile?  If
>>> so, I think the easiest method would be to replace that with a np.memmap (
>>> http://docs.scipy.org/doc/numpy/reference/generated/numpy.memmap.html).
>>> Once that is loaded, you should be able to use load_uniform_grid.
>>>
>>>  At that point, there are two possible routes that both may or may not
>>> work well.
>>>
>>>  1) Just try rendering with ~256-512 cores, and the AMRKDTree should
>>> try to geometrically split the grid before performing and I/O.
>>> or
>>> 2) Use load_uniform_grid with the keyword nprocs=N ( for this size
>>> simulation, you probably need something like 256-1024 processors depending
>>> on the memory per core). This should do the equivalent thing to (1), but it
>>> may hit the I/O here instead of in the kd-tree.
>>>
>>>  I *think* (1) should be your best option, but I haven't tried
>>> rendering this large of a single-grid output.
>>>
>>>  When you build the camera option, definitely start out using the
>>> keyword "no_ghost=True", as this will extrapolate rather than interpolate
>>> from boundary grids to the vertices. The rendering quality won't be quite
>>> as good but for unigrid simulations there isn't a tremendous difference.
>>>
>>>  Let us know how that goes!  I'd be very excited to see images from
>>> such a large sim...
>>>
>>>  Sam
>>>
>>>
>>>
>>>>
>>>> I imagine it wouldn't be hard to do the domain decomposition by hand,
>>>> loading a different chunk of grid into each MPI process.   But then
>>>> what?   What would it take to invoke the volume renderer on each piece
>>>> and composite them together?   Would it help if the chunks were stored
>>>> in a KDTree?   Is there some example (one of the existing data loaders?)
>>>> which I could follow?
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>>
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