Maybe it's running low on memory in the parallel case where you use all 8 processors on a node. Why don't you try doing it with 7 or 6 ppn and see if that changes things. Britton suggested experimenting with the parameters in his previous email, so why not try? You can always ssh to the node where your job is running and look at the memory consumption to see if it's running low. Above all, experiment on your own to try to figure these things out. Cameron Agarwal, Shankar wrote:
Hi,
I ran yt's HaloFinder routine on 1 processor, and then on 8 processors. Both give identical output : 40,000 halos starting from 512^3 particles.
1 processor : took 1 hour 45 minutes. 8 processors : took 2 hours.
My script is hop_yt.py....
from yt.mods import * fn = "RedshiftOutput0002" pf = load(fn) halos = HaloFinder(pf,40,padding=0.02) halos.write_out("HopAnalysis.out")
commands I used....
mpirun -np 1 python hop_yt.py --parallel mpirun -np 8 python hop_yt.py --parallel
Any idea why 8 cpus would take longer ?
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-- Cameron Hummels PhD Candidate, Astronomy Department of Columbia University Public Outreach Director, Astronomy Department of Columbia University NASA IYA New York State Student Ambassador http://outreach.astro.columbia.edu