Hi all,
I recently did some volume renders of a 50 Mpc box unigrid simulation with
1024^3 grid cells on kraken. I used exactly 64 cores and did not have to
use less than the full number of cores available per node. I was making
1024^2 images that took roughly between 5-10 seconds to render. I tried
some 2048 that took around 30-40 seconds. I was rendering baryon
overdensity with a transfer function that had 2000 narrow gaussians. The
number was high because I am combining this with a movie in which I render
only one of those guassians at a time and build the box up from low
overdensity up to high. I didn't go to lower number of processors, so I'm
not exactly sure at what point this would have run out of ram. I consider
this an overwhelming success. I've attached some sample images, one with
the full transfer function and a sample frame from the movie where I do them
one at a time while spinning. Very very nice job!
Britton
On Wed, Nov 10, 2010 at 11:46 AM, Matthew Turk
Hi Sam,
Great work! I'm really happy to see this make it into the primary trunk.
I'd like to encourage people to try this out, particularly on large datasets, and write to the list or Sam if you run into problems. This is a big increase in functionality, and everyone wants to make sure it works out alright.
I've been using the volume rendering capabilities of yt quite extensively, in kind of an unconventional way, to calculate off-axis average values, and I'm very excited about the performance improvements that this new subsystem will bring.
Congrats, Sam!
-Matt
On Tue, Nov 9, 2010 at 5:12 PM, Sam Skillman
wrote: Hi all, I just wanted to announce that the new kd-Tree rendering framework is now in the 'yt' branch of the repository. There are a couple things I wanted to point to if you are interested. The changeset itself: http://yt.enzotools.org/changeset/c7947fef16ac/ A post on blog.enzotools.org highlighting some recent successes: http://blog.enzotools.org/amr-kd-tree-rendering-added-to-yt A simple script, where you should just have to change the parameter file name: http://paste.enzotools.org/show/1367/ A more advanced script that exposes a few new options: http://paste.enzotools.org/show/1368/ Both of these scripts should be able to be run in parallel (as long as N is a power of 2 for now) transparently as: mpirun -np N python script.py --parallel Parallel performance will depend on the structure of your data, but the docs for the Camera object have some suggestions. If you find any problems or have any thoughts, let me know! Best, Sam
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