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

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 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 wrote:

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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 wrote:

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Hi Stuart,

On Thu Nov 06 2014 at 8:36:28 AM Stuart Levy wrote:

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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

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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? _______________________________________________ yt-users mailing list yt-users@lists.spacepope.org http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org

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