[mesa-users] OpenMP vs. MPI

Wed Sep 24 18:03:01 EDT 2014

Also single jobs per core also helps with the job scheduling. Single cores
jobs can be fitted in to the gaps in the scheduler as it will be easier to
find machines with single cores free than many cores free. Thus your jobs
start sooner. I've had clusters where you have to wait a day or more to run
your code as its so over subscribed, thus being able to start asap (even if
it will take longer to run as you have less cores) gets you a result sooner.

Rob

On Wed, Sep 24, 2014 at 2:54 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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