Mark, I think your idea is good and submitted the jobs, but the jobs are in the queue for a whole day.
--Junchao Zhang On Mon, Jun 11, 2018 at 8:09 AM, Mark Adams <[email protected]> wrote: > > > On Mon, Jun 11, 2018 at 12:46 AM, Junchao Zhang <[email protected]> > wrote: > >> I used an LCRC machine named Bebop. I tested on its Intel Broadwell >> nodes. Each nodes has 2 CPUs and 36 cores in total. I collected data using >> 36 cores in a node or 18 cores in a node. As you can see, 18 cores/node >> gave much better performance, which is reasonable as routines like MatSOR, >> MatMult, MatMultAdd are all bandwidth bound. >> >> The code uses a DMDA 3D grid, 7-point stencil, and defines >> nodes(vertices) at the surface or second to the surface as boundary nodes. >> Boundary nodes only have a diagonal one in their row in the matrix. >> Interior nodes have 7 nonzeros in their row. Boundary processors in the >> processor grid has less nonzero. This is one source of load-imbalance. Will >> load-imbalance get severer at coarser grids of an MG level? >> > > Yes. > > You can use a simple Jacobi solver to see the basic performance of your > operator and machine. Do you see as much time spent in Vec Scatters? > VecAXPY? etc. > > >> >> I attach a trace view figure that show activity of each ranks along the >> time axis in one KSPSove. White color means MPI wait. You can see white >> takes a large space. >> >> I don't have a good explanation why at large scale (1728 cores), >> processors wait longer time, as the communication pattern is still 7-point >> stencil in a cubic processor gird. >> >> --Junchao Zhang >> >> On Sat, Jun 9, 2018 at 11:32 AM, Smith, Barry F. <[email protected]> >> wrote: >> >>> >>> Junchao, >>> >>> Thanks, the load balance of matrix entries is remarkably similar >>> for the two runs so it can't be a matter of worse work load imbalance for >>> SOR for the larger case explaining why the SOR takes more time. >>> >>> Here is my guess (and I know no way to confirm it). In the smaller >>> case the overlap of different processes on the same node running SOR at the >>> same time is lower than the larger case hence the larger case is slower >>> because there are more SOR processes fighting over the same memory >>> bandwidth at the same time than in the smaller case. Ahh, here is >>> something you can try, lets undersubscribe the memory bandwidth needs, run >>> on say 16 processes per node with 8 nodes and 16 processes per node with 64 >>> nodes and send the two -log_view output files. I assume this is an LCRC >>> machine and NOT a KNL system? >>> >>> Thanks >>> >>> >>> Barry >>> >>> >>> > On Jun 9, 2018, at 8:29 AM, Mark Adams <[email protected]> wrote: >>> > >>> > -pc_gamg_type classical >>> > >>> > FYI, we only support smoothed aggregation "agg" (the default). (This >>> thread started by saying you were using GAMG.) >>> > >>> > It is not clear how much this will make a difference for you, but you >>> don't want to use classical because we do not support it. It is meant as a >>> reference implementation for developers. >>> > >>> > First, how did you get the idea to use classical? If the documentation >>> lead you to believe this was a good thing to do then we need to fix that! >>> > >>> > Anyway, here is a generic input for GAMG: >>> > >>> > -pc_type gamg >>> > -pc_gamg_type agg >>> > -pc_gamg_agg_nsmooths 1 >>> > -pc_gamg_coarse_eq_limit 1000 >>> > -pc_gamg_reuse_interpolation true >>> > -pc_gamg_square_graph 1 >>> > -pc_gamg_threshold 0.05 >>> > -pc_gamg_threshold_scale .0 >>> > >>> > >>> > >>> > >>> > On Thu, Jun 7, 2018 at 6:52 PM, Junchao Zhang <[email protected]> >>> wrote: >>> > OK, I have thought that space was a typo. btw, this option does not >>> show up in -h. >>> > I changed number of ranks to use all cores on each node to avoid >>> misleading ratio in -log_view. Since one node has 36 cores, I ran with >>> 6^3=216 ranks, and 12^3=1728 ranks. I also found call counts of MatSOR etc >>> in the two tests were different. So they are not strict weak scaling tests. >>> I tried to add -ksp_max_it 6 -pc_mg_levels 6, but still could not make the >>> two have the same MatSOR count. Anyway, I attached the load balance output. >>> > >>> > I find PCApply_MG calls PCMGMCycle_Private, which is recursive and >>> indirectly calls MatSOR_MPIAIJ. I believe the following code in >>> MatSOR_MPIAIJ practically syncs {MatSOR, MatMultAdd}_SeqAIJ between >>> processors through VecScatter at each MG level. If SOR and MatMultAdd are >>> imbalanced, the cost is accumulated along MG levels and shows up as large >>> VecScatter cost. >>> > 1460: while >>> > (its--) { >>> > >>> > 1461: VecScatterBegin(mat->Mvctx,xx >>> ,mat->lvec,INSERT_VALUES,SCATTER_FORWARD >>> > ); >>> > >>> > 1462: VecScatterEnd(mat->Mvctx,xx,m >>> at->lvec,INSERT_VALUES,SCATTER_FORWARD >>> > ); >>> > >>> > >>> > 1464: /* update rhs: bb1 = bb - B*x */ >>> > 1465: VecScale >>> > (mat->lvec,-1.0); >>> > >>> > 1466: (*mat->B->ops->multadd)(mat-> >>> > B,mat->lvec,bb,bb1); >>> > >>> > >>> > 1468: /* local sweep */ >>> > 1469: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP, >>> > fshift,lits,1,xx); >>> > >>> > 1470: } >>> > >>> > >>> > >>> > --Junchao Zhang >>> > >>> > On Thu, Jun 7, 2018 at 3:11 PM, Smith, Barry F. <[email protected]> >>> wrote: >>> > >>> > >>> > > On Jun 7, 2018, at 12:27 PM, Zhang, Junchao <[email protected]> >>> wrote: >>> > > >>> > > Searched but could not find this option, -mat_view::load_balance >>> > >>> > There is a space between the view and the : load_balance is a >>> particular viewer format that causes the printing of load balance >>> information about number of nonzeros in the matrix. >>> > >>> > Barry >>> > >>> > > >>> > > --Junchao Zhang >>> > > >>> > > On Thu, Jun 7, 2018 at 10:46 AM, Smith, Barry F. <[email protected]> >>> wrote: >>> > > So the only surprise in the results is the SOR. It is >>> embarrassingly parallel and normally one would not see a jump. >>> > > >>> > > The load balance for SOR time 1.5 is better at 1000 processes than >>> for 125 processes of 2.1 not worse so this number doesn't easily explain >>> it. >>> > > >>> > > Could you run the 125 and 1000 with -mat_view ::load_balance and >>> see what you get out? >>> > > >>> > > Thanks >>> > > >>> > > Barry >>> > > >>> > > Notice that the MatSOR time jumps a lot about 5 secs when the >>> -log_sync is on. My only guess is that the MatSOR is sharing memory >>> bandwidth (or some other resource? cores?) with the VecScatter and for some >>> reason this is worse for 1000 cores but I don't know why. >>> > > >>> > > > On Jun 6, 2018, at 9:13 PM, Junchao Zhang <[email protected]> >>> wrote: >>> > > > >>> > > > Hi, PETSc developers, >>> > > > I tested Michael Becker's code. The code calls the same KSPSolve >>> 1000 times in the second stage and needs cubic number of processors to run. >>> I ran with 125 ranks and 1000 ranks, with or without -log_sync option. I >>> attach the log view output files and a scaling loss excel file. >>> > > > I profiled the code with 125 processors. It looks {MatSOR, >>> MatMult, MatMultAdd, MatMultTranspose, MatMultTransposeAdd}_SeqAIJ in aij.c >>> took ~50% of the time, The other half time was spent on waiting in MPI. >>> MatSOR_SeqAIJ took 30%, mostly in PetscSparseDenseMinusDot(). >>> > > > I tested it on a 36 cores/node machine. I found 32 ranks/node >>> gave better performance (about 10%) than 36 ranks/node in the 125 ranks >>> testing. I guess it is because processors in the former had more balanced >>> memory bandwidth. I collected PAPI_DP_OPS (double precision operations) and >>> PAPI_TOT_CYC (total cycles) of the 125 ranks case (see the attached files). >>> It looks ranks at the two ends have less DP_OPS and TOT_CYC. >>> > > > Does anyone familiar with the algorithm have quick explanations? >>> > > > >>> > > > --Junchao Zhang >>> > > > >>> > > > On Mon, Jun 4, 2018 at 11:59 AM, Michael Becker < >>> [email protected]> wrote: >>> > > > Hello again, >>> > > > >>> > > > this took me longer than I anticipated, but here we go. >>> > > > I did reruns of the cases where only half the processes per node >>> were used (without -log_sync): >>> > > > >>> > > > 125 procs,1st 125 procs,2nd >>> 1000 procs,1st 1000 procs,2nd >>> > > > Max Ratio Max Ratio >>> Max Ratio Max Ratio >>> > > > KSPSolve 1.203E+02 1.0 1.210E+02 1.0 >>> 1.399E+02 1.1 1.365E+02 1.0 >>> > > > VecTDot 6.376E+00 3.7 6.551E+00 4.0 >>> 7.885E+00 2.9 7.175E+00 3.4 >>> > > > VecNorm 4.579E+00 7.1 5.803E+00 10.2 >>> 8.534E+00 6.9 6.026E+00 4.9 >>> > > > VecScale 1.070E-01 2.1 1.129E-01 2.2 >>> 1.301E-01 2.5 1.270E-01 2.4 >>> > > > VecCopy 1.123E-01 1.3 1.149E-01 1.3 >>> 1.301E-01 1.6 1.359E-01 1.6 >>> > > > VecSet 7.063E-01 1.7 6.968E-01 1.7 >>> 7.432E-01 1.8 7.425E-01 1.8 >>> > > > VecAXPY 1.166E+00 1.4 1.167E+00 1.4 >>> 1.221E+00 1.5 1.279E+00 1.6 >>> > > > VecAYPX 1.317E+00 1.6 1.290E+00 1.6 >>> 1.536E+00 1.9 1.499E+00 2.0 >>> > > > VecScatterBegin 6.142E+00 3.2 5.974E+00 2.8 >>> 6.448E+00 3.0 6.472E+00 2.9 >>> > > > VecScatterEnd 3.606E+01 4.2 3.551E+01 4.0 >>> 5.244E+01 2.7 4.995E+01 2.7 >>> > > > MatMult 3.561E+01 1.6 3.403E+01 1.5 >>> 3.435E+01 1.4 3.332E+01 1.4 >>> > > > MatMultAdd 1.124E+01 2.0 1.130E+01 2.1 >>> 2.093E+01 2.9 1.995E+01 2.7 >>> > > > MatMultTranspose 1.372E+01 2.5 1.388E+01 2.6 >>> 1.477E+01 2.2 1.381E+01 2.1 >>> > > > MatSolve 1.949E-02 0.0 1.653E-02 0.0 >>> 4.789E-02 0.0 4.466E-02 0.0 >>> > > > MatSOR 6.610E+01 1.3 6.673E+01 1.3 >>> 7.111E+01 1.3 7.105E+01 1.3 >>> > > > MatResidual 2.647E+01 1.7 2.667E+01 1.7 >>> 2.446E+01 1.4 2.467E+01 1.5 >>> > > > PCSetUpOnBlocks 5.266E-03 1.4 5.295E-03 1.4 >>> 5.427E-03 1.5 5.289E-03 1.4 >>> > > > PCApply 1.031E+02 1.0 1.035E+02 1.0 >>> 1.180E+02 1.0 1.164E+02 1.0 >>> > > > >>> > > > I also slimmed down my code and basically wrote a simple weak >>> scaling test (source files attached) so you can profile it yourself. I >>> appreciate the offer Junchao, thank you. >>> > > > You can adjust the system size per processor at runtime via >>> "-nodes_per_proc 30" and the number of repeated calls to the function >>> containing KSPsolve() via "-iterations 1000". The physical problem is >>> simply calculating the electric potential from a homogeneous charge >>> distribution, done multiple times to accumulate time in KSPsolve(). >>> > > > A job would be started using something like >>> > > > mpirun -n 125 ~/petsc_ws/ws_test -nodes_per_proc 30 -mesh_size >>> 1E-4 -iterations 1000 \\ >>> > > > -ksp_rtol 1E-6 \ >>> > > > -log_view -log_sync\ >>> > > > -pc_type gamg -pc_gamg_type classical\ >>> > > > -ksp_type cg \ >>> > > > -ksp_norm_type unpreconditioned \ >>> > > > -mg_levels_ksp_type richardson \ >>> > > > -mg_levels_ksp_norm_type none \ >>> > > > -mg_levels_pc_type sor \ >>> > > > -mg_levels_ksp_max_it 1 \ >>> > > > -mg_levels_pc_sor_its 1 \ >>> > > > -mg_levels_esteig_ksp_type cg \ >>> > > > -mg_levels_esteig_ksp_max_it 10 \ >>> > > > -gamg_est_ksp_type cg >>> > > > , ideally started on a cube number of processes for a cubical >>> process grid. >>> > > > Using 125 processes and 10.000 iterations I get the output in >>> "log_view_125_new.txt", which shows the same imbalance for me. >>> > > > Michael >>> > > > >>> > > > >>> > > > Am 02.06.2018 um 13:40 schrieb Mark Adams: >>> > > >> >>> > > >> >>> > > >> On Fri, Jun 1, 2018 at 11:20 PM, Junchao Zhang < >>> [email protected]> wrote: >>> > > >> Hi,Michael, >>> > > >> You can add -log_sync besides -log_view, which adds barriers to >>> certain events but measures barrier time separately from the events. I find >>> this option makes it easier to interpret log_view output. >>> > > >> >>> > > >> That is great (good to know). >>> > > >> >>> > > >> This should give us a better idea if your large VecScatter costs >>> are from slow communication or if it catching some sort of load imbalance. >>> > > >> >>> > > >> >>> > > >> --Junchao Zhang >>> > > >> >>> > > >> On Wed, May 30, 2018 at 3:27 AM, Michael Becker < >>> [email protected]> wrote: >>> > > >> Barry: On its way. Could take a couple days again. >>> > > >> >>> > > >> Junchao: I unfortunately don't have access to a cluster with a >>> faster network. This one has a mixed 4X QDR-FDR InfiniBand 2:1 blocking >>> fat-tree network, which I realize causes parallel slowdown if the nodes are >>> not connected to the same switch. Each node has 24 processors (2x12/socket) >>> and four NUMA domains (two for each socket). >>> > > >> The ranks are usually not distributed perfectly even, i.e. for >>> 125 processes, of the six required nodes, five would use 21 cores and one >>> 20. >>> > > >> Would using another CPU type make a difference >>> communication-wise? I could switch to faster ones (on the same network), >>> but I always assumed this would only improve performance of the stuff that >>> is unrelated to communication. >>> > > >> >>> > > >> Michael >>> > > >> >>> > > >> >>> > > >> >>> > > >>> The log files have something like "Average time for zero size >>> MPI_Send(): 1.84231e-05". It looks you ran on a cluster with a very slow >>> network. A typical machine should give less than 1/10 of the latency you >>> have. An easy way to try is just running the code on a machine with a >>> faster network and see what happens. >>> > > >>> >>> > > >>> Also, how many cores & numa domains does a compute node have? I >>> could not figure out how you distributed the 125 MPI ranks evenly. >>> > > >>> >>> > > >>> --Junchao Zhang >>> > > >>> >>> > > >>> On Tue, May 29, 2018 at 6:18 AM, Michael Becker < >>> [email protected]> wrote: >>> > > >>> Hello again, >>> > > >>> >>> > > >>> here are the updated log_view files for 125 and 1000 processors. >>> I ran both problems twice, the first time with all processors per node >>> allocated ("-1.txt"), the second with only half on twice the number of >>> nodes ("-2.txt"). >>> > > >>> >>> > > >>>>> On May 24, 2018, at 12:24 AM, Michael Becker < >>> [email protected]> >>> > > >>>>> wrote: >>> > > >>>>> >>> > > >>>>> I noticed that for every individual KSP iteration, six vector >>> objects are created and destroyed (with CG, more with e.g. GMRES). >>> > > >>>>> >>> > > >>>> Hmm, it is certainly not intended at vectors be created and >>> destroyed within each KSPSolve() could you please point us to the code that >>> makes you think they are being created and destroyed? We create all the >>> work vectors at KSPSetUp() and destroy them in KSPReset() not during the >>> solve. Not that this would be a measurable distance. >>> > > >>>> >>> > > >>> >>> > > >>> I mean this, right in the log_view output: >>> > > >>> >>> > > >>>> Memory usage is given in bytes: >>> > > >>>> >>> > > >>>> Object Type Creations Destructions Memory Descendants' Mem. >>> > > >>>> Reports information only for process 0. >>> > > >>>> >>> > > >>>> --- Event Stage 0: Main Stage >>> > > >>>> >>> > > >>>> ... >>> > > >>>> >>> > > >>>> --- Event Stage 1: First Solve >>> > > >>>> >>> > > >>>> ... >>> > > >>>> >>> > > >>>> --- Event Stage 2: Remaining Solves >>> > > >>>> >>> > > >>>> Vector 23904 23904 1295501184 0. >>> > > >>> I logged the exact number of KSP iterations over the 999 >>> timesteps and its exactly 23904/6 = 3984. >>> > > >>> Michael >>> > > >>> >>> > > >>> >>> > > >>> Am 24.05.2018 um 19:50 schrieb Smith, Barry F.: >>> > > >>>> >>> > > >>>> Please send the log file for 1000 with cg as the solver. >>> > > >>>> >>> > > >>>> You should make a bar chart of each event for the two cases >>> to see which ones are taking more time and which are taking less (we cannot >>> tell with the two logs you sent us since they are for different solvers.) >>> > > >>>> >>> > > >>>> >>> > > >>>> >>> > > >>>> >>> > > >>>>> On May 24, 2018, at 12:24 AM, Michael Becker < >>> [email protected]> >>> > > >>>>> wrote: >>> > > >>>>> >>> > > >>>>> I noticed that for every individual KSP iteration, six vector >>> objects are created and destroyed (with CG, more with e.g. GMRES). >>> > > >>>>> >>> > > >>>> Hmm, it is certainly not intended at vectors be created and >>> destroyed within each KSPSolve() could you please point us to the code that >>> makes you think they are being created and destroyed? We create all the >>> work vectors at KSPSetUp() and destroy them in KSPReset() not during the >>> solve. Not that this would be a measurable distance. >>> > > >>>> >>> > > >>>> >>> > > >>>> >>> > > >>>> >>> > > >>>>> This seems kind of wasteful, is this supposed to be like this? >>> Is this even the reason for my problems? Apart from that, everything seems >>> quite normal to me (but I'm not the expert here). >>> > > >>>>> >>> > > >>>>> >>> > > >>>>> Thanks in advance. >>> > > >>>>> >>> > > >>>>> Michael >>> > > >>>>> >>> > > >>>>> >>> > > >>>>> >>> > > >>>>> <log_view_125procs.txt><log_vi >>> > > >>>>> ew_1000procs.txt> >>> > > >>>>> >>> > > >>> >>> > > >>> >>> > > >> >>> > > >> >>> > > >> >>> > > > >>> > > > >>> > > > <o-wstest-125.txt><Scaling-loss.png><o-wstest-1000.txt><o-ws >>> test-sync-125.txt><o-wstest-sync-1000.txt><MatSOR_SeqAIJ.png >>> ><PAPI_TOT_CYC.png><PAPI_DP_OPS.png> >>> > > >>> > > >>> > >>> > >>> > >>> >>> >> >
