On Fri Nov 07 2014 at 9:03:51 AM Sam Skillman <samskillman@gmail.com> wrote:

Hi Stuart,

On Thu Nov 06 2014 at 8:36:28 AM Stuart Levy <salevy@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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