[mpich-devel] MPI_Comm_Split/Dup scalability on BGQ and K supercomputers
Bob Cernohous
bcernohous at cray.com
Mon Sep 22 13:18:47 CDT 2014
I thought there was a "memory" optimization that disabled PAMI on irregular communicators. However I don't know the current state of that code.
if(MPIDI_Process.optimized.memory && (comm->local_size & (comm->local_size-1)))
{
/* Don't create irregular geometries. Fallback to MPICH only collectives */
geom_init = 0;
comm->mpid.geometry = NULL;
}
> -----Original Message-----
> From: devel-bounces at mpich.org [mailto:devel-bounces at mpich.org] On
> Behalf Of Jeff Hammond
> Sent: Monday, September 22, 2014 12:16 PM
> To: devel at mpich.org
> Subject: Re: [mpich-devel] MPI_Comm_Split/Dup scalability on BGQ and K
> supercomputers
>
> PAMID_COLLECTIVES=0 is really bad for performance. IBM should figure out a
> way to disable it on a per-communicator basis when MPI_COMM_SPLIT is
> going to have issues. I recall they allow one to "unoptimize" a communicator
> but I thought that was only possible after it was created.
>
> Jeff
>
> On Mon, Sep 22, 2014 at 8:39 AM, Junchao Zhang <jczhang at mcs.anl.gov>
> wrote:
> > Sam,
> > I had some updates from IBM last week. They reproduced the problem
> > and found it only happens when the number of MPI ranks is
> > non-power-of-2. Their advice is that since the IBM BG/Q optimized
> > collectives themselves are mostly designed only to be helpful for
> > blocks with power-of-2 geometries, you can try in your program to see
> > if subsequent collective calls with
> > PAMID_COLLECTIVES=1 are actually faster than PAMID_COLLECTIVES=0 on
> > comms with a non-power-of-2 geometry. If the answer is no, then you
> > can just run with PAMID_COLLECTIVES=0 and avoid the dup/split
> performance issue.
> > Otherwise, IBM may prioritize this ticket.
> >
> > Thanks.
> > --Junchao Zhang
> >
> > On Thu, Jul 3, 2014 at 4:41 PM, Junchao Zhang <jczhang at mcs.anl.gov>
> wrote:
> >>
> >> Hi, Sam,
> >> I wrote micro-benchmarks for MPI_Comm_split/dup. My profiling
> >> results suggested the problem lies in a IBM PAMI library call,
> >> PAMI_Geometry_create_taskrange(). Unfortunately, I don't have access
> >> to the PAMI source code and don't know why. I reported it to IBM and
> >> hope IBM will fix it.
> >> Alternatively, you can set an environment variable
> >> PAMID_COLLECTIVES=0 to disables pami collectives. My tests showed it
> >> at least fixed the scalability problem of Comm_split and Comm_dup.
> >> Also through profiling, I found the qsort() called in MPICH code is
> >> actually using the merge sort algorithm in Mira's libc library.
> >>
> >>
> >>
> >> --Junchao Zhang
> >>
> >>
> >> On Sat, May 17, 2014 at 9:06 AM, Sam Williams <swwilliams at lbl.gov>
> wrote:
> >>>
> >>> I've been conducting scaling experiments on the Mira (Blue Gene/Q)
> >>> and K
> >>> (Sparc) supercomputers. I've noticed that the time required for
> >>> MPI_Comm_split and MPI_Comm_dup can grow quickly with scale
> (~P^2).
> >>> As such, its performance eventually becomes a bottleneck. That is,
> >>> although the benefit of using a subcommunicator is huge (multigrid
> >>> solves are weak-scalable), the penalty of creating one (multigrid
> >>> build time) is also huge.
> >>>
> >>> For example, when scaling from 1 to 46K nodes (= cubes of integers)
> >>> on Mira, the time (in seconds) required to build a MG solver
> >>> (including a
> >>> subcommunicator) scales as
> >>> 222335.output: Total time in MGBuild 0.056704
> >>> 222336.output: Total time in MGBuild 0.060834
> >>> 222348.output: Total time in MGBuild 0.064782
> >>> 222349.output: Total time in MGBuild 0.090229
> >>> 222350.output: Total time in MGBuild 0.075280
> >>> 222351.output: Total time in MGBuild 0.091852
> >>> 222352.output: Total time in MGBuild 0.137299
> >>> 222411.output: Total time in MGBuild 0.301552
> >>> 222413.output: Total time in MGBuild 0.606444
> >>> 222415.output: Total time in MGBuild 0.745272
> >>> 222417.output: Total time in MGBuild 0.779757
> >>> 222418.output: Total time in MGBuild 4.671838
> >>> 222419.output: Total time in MGBuild 15.123162
> >>> 222420.output: Total time in MGBuild 33.875626
> >>> 222421.output: Total time in MGBuild 49.494547
> >>> 222422.output: Total time in MGBuild 151.329026
> >>>
> >>> If I disable the call to MPI_Comm_Split, my time scales as
> >>> 224982.output: Total time in MGBuild 0.050143
> >>> 224983.output: Total time in MGBuild 0.052607
> >>> 224988.output: Total time in MGBuild 0.050697
> >>> 224989.output: Total time in MGBuild 0.078343
> >>> 224990.output: Total time in MGBuild 0.054634
> >>> 224991.output: Total time in MGBuild 0.052158
> >>> 224992.output: Total time in MGBuild 0.060286
> >>> 225008.output: Total time in MGBuild 0.062925
> >>> 225009.output: Total time in MGBuild 0.097357
> >>> 225010.output: Total time in MGBuild 0.061807
> >>> 225011.output: Total time in MGBuild 0.076617
> >>> 225012.output: Total time in MGBuild 0.099683
> >>> 225013.output: Total time in MGBuild 0.125580
> >>> 225014.output: Total time in MGBuild 0.190711
> >>> 225016.output: Total time in MGBuild 0.218329
> >>> 225017.output: Total time in MGBuild 0.282081
> >>>
> >>> Although I didn't directly measure it, this suggests the time for
> >>> MPI_Comm_Split is growing roughly quadratically with process
> concurrency.
> >>>
> >>>
> >>>
> >>>
> >>> I see the same effect on the K machine (8...64K nodes) where the
> >>> code uses comm_split/dup in conjunction:
> >>> run00008_7_1.sh.o2412931: Total time in MGBuild 0.026458
> seconds
> >>> run00064_7_1.sh.o2415876: Total time in MGBuild 0.039121
> seconds
> >>> run00512_7_1.sh.o2415877: Total time in MGBuild 0.086800
> seconds
> >>> run01000_7_1.sh.o2414496: Total time in MGBuild 0.129764
> seconds
> >>> run01728_7_1.sh.o2415878: Total time in MGBuild 0.224576
> seconds
> >>> run04096_7_1.sh.o2415880: Total time in MGBuild 0.738979
> seconds
> >>> run08000_7_1.sh.o2414504: Total time in MGBuild 2.123800
> seconds
> >>> run13824_7_1.sh.o2415881: Total time in MGBuild 6.276573
> seconds
> >>> run21952_7_1.sh.o2415882: Total time in MGBuild 13.634200
> seconds
> >>> run32768_7_1.sh.o2415884: Total time in MGBuild 36.508670
> seconds
> >>> run46656_7_1.sh.o2415874: Total time in MGBuild 58.668228
> seconds
> >>> run64000_7_1.sh.o2415875: Total time in MGBuild 117.322217
> seconds
> >>>
> >>>
> >>> A glance at the implementation on Mira (I don't know if the
> >>> implementation on K is stock) suggests it should be using qsort to
> >>> sort based on keys. Unfortunately, qsort is not performance robust
> >>> like heap/merge sort. If one were to be productive and call comm_split
> like...
> >>> MPI_Comm_split(...,mycolor,myrank,...)
> >>> then one runs the risk that the keys are presorted. This hits the
> >>> worst case computational complexity for qsort... O(P^2). Demanding
> >>> programmers avoid sending sorted keys seems unreasonable.
> >>>
> >>>
> >>> I should note, I see a similar lack of scaling with MPI_Comm_dup on
> >>> the K machine. Unfortunately, my BGQ data used an earlier version
> >>> of the code that did not use comm_dup. As such, I can’t
> >>> definitively say that it is a problem on that machine as well.
> >>>
> >>> Thus, I'm asking for scalable implementations of comm_split/dup
> >>> using merge/heap sort whose worst case complexity is still PlogP to
> >>> be prioritized in the next update.
> >>>
> >>>
> >>> thanks
> >>> _______________________________________________
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> >>
> >>
> >
> >
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>
>
> --
> Jeff Hammond
> jeff.science at gmail.com
> http://jeffhammond.github.io/
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