[mesa-users] OpenMP vs. MPI

Wed Sep 24 17:57:49 EDT 2014

Thanks very much, everyone. I'm going to consult with the IT folks
here, and see what they recommend. I might ping the list again for
further advice once I understand our situation a bit better here.

Brian

On Wed, Sep 24, 2014 at 3:54 PM, Ehsan Moravveji <e.moravveji at gmail.com> wrote:
> Hi all,
> I practically calculate grids most of the time, and take up what Rob and
> Dave already suggested. One job per each core, and that's the most efficient
> way to go.
>
> But if you have a separate job for each core, then there is a chance to do
> better if the swapping between jobs is very efficient.
>
>
> With SLURM and/or PBS/Torque, the job queueing is pretty efficient, and the
> overhead is minimal, specifically if one makes use their "job array"
> feature.
> If you use either SLURM or PBS, I can already help setting you up.
>
> Best
> Ehsan.
>
>
>
>
>
> On Sep 24, 2014, at 2:16 PM, Dave Spiegel wrote:
>
> I did some experimenting on this a few months ago.  Just to reiterate what
> others are saying:
> The most efficient way to run N MESA jobs on N cores is to run each on its
> own core.  If you have fewer than N jobs and N cores, then you can take
> advantage of OpenMP by running your jobs in parallel, but probably the most
> efficient thing for you to do is just run each on its own core.
>
> On Wed, Sep 24, 2014 at 5:02 PM, Brian Jackson <decaelus at gmail.com> wrote:
>>
>> Many thanks for the responses.
>>
>> Brian
>>
>> On Wed, Sep 24, 2014 at 2:58 PM, Robert FARMER <rjfarmer at asu.edu> wrote:
>> > Hi,
>> > There right that an individual job will be no faster on the cluster than
>> > on
>> > your local machine, in fact maybe slower depending on how many cores you
>> > can
>> > request for each run, if you can get all 16 cores on the same machine it
>> > should be faster than your desktop. Although i don't know how well mesa
>> > scales past 10 cores so being limited to at most 16 may not be an issue.
>> >
>> > But what you could do is run many mesa jobs simultaneously, each taking
>> > 1
>> > core each, so each job takes longer overall but a grid of mesa models
>> > will
>> > be quicker (depending on how busy your cluster is)
>> >
>> > There are guides here
>> > http://mesastar.org/tools-utilities/mesa-on-clusters
>> > from people running mesa on clusters
>> >
>> > Rob
>> >
>> > On Wed, Sep 24, 2014 at 1:55 PM, Bill Paxton <paxton at kitp.ucsb.edu>
>> > wrote:
>> >>
>> >> Hi Brian,
>> >>
>> >> Your IT folks got it right.  MESA is designed to make use of OpenMP
>> >> shared-memory parallelism.  The only value of the cluster will come if
>> >> you
>> >> want to run lots of MESA's doing separate jobs, such as in a population
>> >> study or a study of the effects of different parameter values.  The
>> >> cluster's many nodes aren't going to help on individual MESA runs.  For
>> >> that, multicores help, at least for up to 8 cores or so (I have one
>> >> workstation in my office with 12 cores and another with 20 -- the 1st
>> >> an
>> >> aged mac, the 2nd a new workstation from Microway).  So you might
>> >> consider
>> >> trading in your iMac for something with at least 4 cores and at least 8
>> >> MB
>> >> of ram.  To anticipate your next question, GPU's aren't helpful. ;D
>> >> They
>> >> are great for some things, but not for full stellar evolution, at least
>> >> not
>> >> in the way it is done in MESA.
>> >>
>> >> Hope that helps,
>> >> Bill
>> >>
>> >>
>> >>
>> >> On Sep 24, 2014, at 1:43 PM, Brian Jackson wrote:
>> >>
>> >> > Dear MESA users,
>> >> > I'm planning to run a grid of MESA calculations, and I was hoping to
>> >> > use the cluster at my home institution to speed things up. However,
>> >> > after getting MESA installed on the cluster, the IT folks here sent
>> >> > me
>> >> > the following message (copy-pasted since I don't really understand
>> >> > the
>> >> > issues involved):
>> >> >
>> >> > "Spent some more time looking at how MESA functioned and it seems
>> >> > that
>> >> > it utilizes the OpenMP library, rather than an MPI library. The issue
>> >> > with this is programs that utilize OpenMP run on a shared memory
>> >> > model, which means your MESA jobs would be limited to one node (16
>> >> > cores). Because our cluster is focused on quantity of processors, the
>> >> > speed of each individual processor is most likely not as high as
>> >> > those
>> >> > on your workstation over there (2.0 GHz). This means you would likely
>> >> > experience better results running MESA locally (and potentially
>> >> > investing in a more powerful workstation if need be)."
>> >> >
>> >> > So it sounds like running MESA on my iMac with a 3.4 GHz processor
>> >> > might be faster than on the cluster. Any suggestions/work-arounds?
>> >> >
>> >> > Thanks,
>> >> > Brian
>> >> >
>> >> > ---------------------------------------------------
>> >> > Brian Jackson, Assistant Professor
>> >> > Dept. of Physics
>> >> > Boise State University
>> >> > 1910 University Drive
>> >> > Boise ID 83725-1570
>> >> >
>> >> > E-mail: bjackson at boisestate.edu
>> >> > URL: http://www.astrojack.com
>> >> > Phone: (208) 426-3723
>> >> > ---------------------------------------------------
>> >> >
>> >> >
>> >> >
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> --
> Dave Spiegel, Ph.D.
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