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 <[email protected]> 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 <[email protected]> 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.

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