Hi Dave,
512^3, bunch of subgrids, with 64 cores totalling 12 gb (i.e., just
shy of 200mb / core) sounds okay to me. I would guess you are
seriously disk limited for the analysis itself, but you can verify
this by running:
python2.7 -m cProfile -o ytprof.cprof my_script.py
and then using either pyprof2html (requires Jinja2, sold separately)
or the stats module to examine it.
-Matt
On Wed, Mar 14, 2012 at 11:15 AM, david collins
64 cores, using 12GB on each? That's a problem. MassFraction it
12Gb total, but still... I'll grab some more failure info and get back to you. In serial it ran fine, and took about 1 hour for both [density,cellmass] and [density,cellvolume] within the default head-node allowance on nautilus, which is 12Gb. Timing wise longer than I want to deal with, but it does run.
d.
sounds like has a handful of dependencies -- CellMassMsun, which depends on Density and dx, but dx should be used as a single value not as a field. So that gives 3*512^3*64 bits = 3 gigabytes, but that's a far cry from 12 gb/core. Plus, the domain decomp for profiles is per-grid -- so only one of your cores should get assigned the root grid. This is very bizarre.
Would lazy_reader off change things?
Yes, but it would make them worse. :)
-Matt
Thanks, d.
Are you running in parallel? Which fields are you profiling? Do they have lots of dependencies, or require ghost zones? Is yt using lazy_reader? Does your data have many grids?
Matt
On Mar 14, 2012 12:37 AM, "david collins"
wrote: I should add that this was done on 64 cores-- in serial it works fine, just slow.
On Tue, Mar 13, 2012 at 9:35 PM, david collins
wrote: Hi, all--
I have an old dataset that I'm trying to make profiles on. It's a 512^3 root grid, but was written with ParallelRootGridIO off. I find that it's using strange amounts of memory, more than 12 Gb. Is this a known problem with a straight forward work-around?
d.
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