[Users] Using Stampede2 SKX
roland.haas at physics.gatech.edu
Fri Jan 26 09:26:26 CST 2018
thank you very much for giving this a spin.
> Hi Erik, Roland, all,
> After our discussion on last week's telecon, I followed Roland's instructions on how to get the branch which has changes to how Carpet handles prolongation with respect to OpenMP. I reran my simple scaling test on Stampede Skylake nodes using this branch of Carpet (rhaas/openmp-tasks) to test the scalability.
> Attached is a plot showing the speeds for a variety of number of nodes and how the 48 threads are distributed on the nodes between MPI processes and OpenMP threads. I did this for three versions of the ETK. 1. Fresh checkout of ET_2017_06. 2. The ET_2017_06 with Carpet switched to the rhaas/openmp-tasks (labelled "Test On") 3. Again with the checkout from #2, but without the parameters to enable the new prolongation code (labelled "Test Off"). The run speeds used were grabbed at iteration 256 from Carpet::physical_time_per_hour. No IO or regridding.
> For 4 and 8 nodes (ie 192 and 384 cores), there wasn't much difference between the 3 trials. However, for 16 and 24 nodes (768 and 1152 cores), we see some improvement in run speed (10-15%) for many choices of distribution of threads, again with a slight preference for 8 ranks/node.
> I also ran the previous test (not using the openmp-tasks branch) on comet, and found similar results as before.
> On 01/21/2018 01:07 PM, Erik Schnetter wrote:
> > James
> > I looked at OpenMP performance in the Einstein Toolkit a few months > ago, and I found that Carpet's prolongation operators are not well > parallelized. There is a branch in Carpet (and a few related thorns) > that apply a different OpenMP parallelization strategy, which seems to > be more efficient. We are currently looking into cherry-picking the > relevant changes from this branch (there are also many unrelated > changes, since I experimented a lot) and putting them back into the > master branch.
> > These changes only help with prolongation, which seems to be a major > contributor to non-OpenMP-scalability. I experimented with other > changes as well. My findings (unfortunately without good solutions so > far) are:
> > - The standard OpenMP parallelization of loops over grid functions is > not good for data cache locality. I experimented with padding arrays, > ensuring that loop boundaries align with cache line boundaries, etc., > but this never worked quite satisfactorily -- MPI parallelization is > still faster than OpenMP. In effect, the only reason one would use > OpenMP is once one encounters MPI's scalability limits, so that > OpenMP's non-scalability is less worse.
> > - We could overlap calculations with communication. To do so, I have > experimental changes that break loops over grid functions into tiles. > Outer tiles need to wait for communication (synchronization or > parallelization) to finish, while inner tiles can be calculated right > away. Unfortunately, OpenMP does not support open-ended threads like > this, so I'm using Qthreads <https://github.com/Qthreads/qthreads> and > FunHPC <https://bitbucket.org/eschnett/funhpc.cxx> for this. The > respective changes to Carpet, the scheduler, and thorns are > significant, and I couldn't prove any performance improvements yet. > However, once we removed other, more prominent non-scalability causes, > I hope that this will become interesting.
> > I haven't been attending the ET phone calls recently because Monday > mornings aren't good for me schedule-wise. If you are interested, then > we can ensure that we both attend at the same time and then discuss > this. We need to make sure the Roland Haas is then also attending.
> > -erik
> > On Sat, Jan 20, 2018 at 10:21 AM, James Healy <jchsma at rit.edu > <mailto:jchsma at rit.edu>> wrote:
> > Hello all,
> > I am trying to run on the new skylake processors on Stampede2 and
> > while the run speeds we are obtaining are very good, we are
> > concerned that we aren't optimizing properly when it comes to
> > OpenMP. For instance, we see the best speeds when we use 8 MPI
> > processors per node (with 6 threads each for a total of 48 total
> > threads/node). Based on the architecture, we were expecting to
> > see the best speeds with 2 MPI/node. Here is what I have tried:
> > 1. Using the simfactory files for stampede2-skx (config file, run
> > and submit scripts, and modules loaded) I compiled a version
> > of ET_2017_06 using LazEv (RIT's evolution thorn) and
> > McLachlan and submitted a series of runs that change both the
> > number of nodes used, and how I distribute the 48 threads/node
> > between MPI processes.
> > 2. I use a standard low resolution grid, with no IO or
> > regridding. Parameter file attached.
> > 3. Run speeds are measured from Carpet::physical_time_per_hour at
> > iteration 256.
> > 4. I tried both with and without hwloc/SystemTopology.
> > 5. For both McLachlan and LazEv, I see similar results, with 2
> > MPI/node giving the worst results (see attached plot for
> > McLachlan) and a slight preferences for 8 MPI/node.
> > So my questions are:
> > 1. Has there been any tests run by any other users on stampede2 skx?
> > 2. Should we expect 2 MPI/node to be the optimal choice?
> > 3. If so, are there any other configurations we can try that
> > could help optimize?
> > Thanks in advance!
> > Jim Healy
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> > -- > Erik Schnetter <schnetter at cct.lsu.edu <mailto:schnetter at cct.lsu.edu>>
> > http://www.perimeterinstitute.ca/personal/eschnetter/
My email is as private as my paper mail. I therefore support encrypting
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