Nicolas wrote:
Carsten Kutzner a écrit :
On Jan 10, 2009, at 8:32 PM, Nicolas wrote:
Berk Hess a écrit :
Hi,
Setting -npme 2 is ridicolous.
mdrun estimates the number of PME nodes by itself when you do not
specify -npme.
In most cases you need 1/3 or 1/4 of the nodes doing pme.
The default -npme guess of mdrun is usually not bad,
but might need to tuned a bit.
At the end of the md.log file you find the relative PP/PME load
so you can see in which direction you might need to change -npme,
if necessary.
Actually, I have tested npme ranging from 0 to 5, but 2 is well
representative of what happens. For example with 5 cores for the PME,
the perfs reach a plateau at 14-15 cores. So, setting npme to 0
systematically gives the best results. I have also tested -1. With,
npme set to -1, the performances are the same than for 0 until 8
cores. Above that, the guess is not so efficient.
Hi Nicolas,
as Berk mentioned, you should expect a different optimal number of PME
nodes for
each number of total nodes you test on. So the way to go is to fix the
number of total
nodes and vary the number of PME nodes until you find the best
performance for that
number of nodes. Then move on to another number of total nodes. I have
written
a small tool that does a part of this job for you by finding out the
optimum number
of PME nodes for a given number of total nodes. If you want to give it
a try, I can
send it to you. Typically the optimum number of PME nodes should not
be too far
off the mdrun estimate. If it is far off, this could point out some
network or MPI
problem. Note that separate PME nodes can only work if the MPI ranks
are not scattered
among the nodes, i.e. on 4-core nodes the ranks 0-3 should be on the
same node
as well as ranks 4-7 and so on. This is printed at the very start of a
parallel
simulation.
Carsten
If you could send me that script, that would be great. I've got a couple
of clusters to benchmark.
By the way, I probably didn't explain my case well enough. I'm trying to
benchmark Gromacs 4 as a function of the number of corse AND the number
of cores dedicated to the PME in a same time. My point was that I didn't
obtain better performance with npme >= 1 compared to npme=0, whatever
the cores and PME cores is. So, I was not sure whether the result was
related to the cluster itself or from a wrong usage of Gromacs.
I don't know if this has been mentioned yet or not, but it was pointed out to me
some months ago, and it helped me in a similar issue. I was empirically trying
to figure out the best combinations, as you seem to be doing.
A nice little feature of grompp is that it prints out the estimated PME load,
and I believe the optimum is about 25%, so you would have 3:1 PP:PME ratio. The
load will be based on your input parameters (grid spacing, cutoff's, etc).
Maybe your PME load is way off the optimum and that is why you are seeing
bizarre behavior in terms of performance.
-Justin
Nicolas
--
Dr. Carsten Kutzner
Max Planck Institute for Biophysical Chemistry
Theoretical and Computational Biophysics
Am Fassberg 11
37077 Goettingen, Germany
Tel. +49-551-2012313, Fax: +49-551-2012302
http://www.mpibpc.mpg.de/home/grubmueller/ihp/ckutzne
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--
========================================
Justin A. Lemkul
Graduate Research Assistant
Department of Biochemistry
Virginia Tech
Blacksburg, VA
jalemkul[at]vt.edu | (540) 231-9080
http://www.bevanlab.biochem.vt.edu/Pages/Personal/justin
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