Hi Dan, Looking this over, I think the problem is definitely that the ghost zone generation is currently quite stupid in 3.0. We're revving up for a 3.1 release, but I think for 3.2 or even within the 3.1 cycle we can aim to have this fixed; I've implemented some simple caching that should speed it up, but which has some issues presently that keep it from being fully functional and effective. One issue that we've (well, I say "we" but I'm the most guilty) had lately is keeping track of things and ensuring that blockers and important new features are added with reasonable time. This has come up repeatedly in the user survey that's currently out. You've been in IRC lately and I wonder if maybe you could help us to keep track of this, by reminding me once in a while if it slips off the radar? It seems the bug tracker is not the best place, and making sure that we have the underscoring that comes from mentioning and communicating can really help to prioritize things. -Matt On Thu, Nov 27, 2014 at 10:26 AM, Daniel Fenn <dsfenn@gmail.com> wrote:
Hi Nathan,
I've attached the html output of pyprof2html. I did it for two cases--a large file on a fast disk, and a somewhat smaller file on a slower disk. In both cases, I only used one processor. The output is not real meaningful to me, since I'm not familiar with the code, and I'm not much of a python programmer.
I'll work on doing this with one of the yt datasets, and let you know how it goes.
Thanks for your help.
Dan
2014-11-27 0:08 GMT-05:00 Nathan Goldbaum <nathan12343@gmail.com>:
On Wed, Nov 26, 2014 at 8:09 PM, Daniel Fenn <dsfenn@gmail.com> wrote:
Hi everyone. Is it normal for slices to take substantially longer to be created if they're made from a derived field rather than an intrinsic one?
Specifically, I'm having an issue creating an axis-aligned slice using the divergence of the velocity field. It's taking around 6.5 minutes just to make the slice, whereas if I use temperature or density, it takes around 10 seconds or so for the same dataset.
Velocity divergence is particularly expensive because it needs ghost zones, which must be filled in using cascading interpolation. Depending on the AMR structure in your data, this might be quite expensive.
That said, 6.5 minutes for a slice is pretty bad.
Is there any chance you can profile the execution of your script? If you run your script like so:
$ python -m cProfile myscript.py
you should get a summary printed to stdout of where the interpreter is spending most of its time. You can also visualize this in more depth if you dump the profile data to a file, and then use pyprof2html or snakeviz to load and display the data, e.g:
$ python -m cProfile -o out.cprof myscript.py $ pyprof2html out.cprof
cProfile comes with pyhon, but pyp4orf2html and snakeviz must be installed separately using pip.
It would be great if you could file an issue about this. Even better if you can reproduce it using one of the test datasets on yt-project.org/data and attach a script that we can run ourselves.
I also notice that the amount of time it takes is not dependent on the number of processors I'm using. I've used 1, 12, and 24 processors, with identical results, even though I'm calling enable_parallelism(), and I can see that all the processes are running.
I read in the docs that slice operations aren't generally done in parallel, but in this case it seems that maybe it would be beneficial. A similar operation in VisIt completes much faster, so I'm wondering if I've misconfigured something, or if there is something I can do to speed things up.
I'd appreciate any thoughts anyone has on the subject.
Thanks,
Dan
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