On Thu, Jul 26, 2018 at 9:51 AM, Junchao Zhang <jczh...@mcs.anl.gov> wrote:
> Hi, Pierre, > From your log_view files, I see you did strong scaling. You used 4X more > cores, but the execution time only dropped from 3.9143e+04 to 1.6910e+04. > From my previous analysis of a GAMG weak scaling test, it looks > communication is one of the reasons that caused poor scaling. In your > case, VecScatterEnd time was doubled from 1.5575e+03 to 3.2413e+03. Its > time percent jumped from 1% to 17%. This time can contribute to the big > time ratio in MatMultAdd ant MatMultTranspose, misleading you guys thinking > there was load-imbalance computation-wise. > The reason is that I found in the interpolation and restriction phases > of gamg, the communication pattern is very bad. Few processes communicate > with hundreds of neighbors with message sizes of a few bytes. > We may need to truncate interpolation/restriction operators. Also do some aggressive coarsening. Unfortunately, GAMG currently does not support. Fande, > If we can avoid this pattern algorithmically (which I don't know), or find > ways with faster communication (which I am working), then we can get better > scalability. > > --Junchao Zhang > > On Thu, Jul 26, 2018 at 10:02 AM, Pierre Jolivet < > pierre.joli...@enseeiht.fr> wrote: > >> >> >> > On 26 Jul 2018, at 4:24 PM, Karl Rupp <r...@iue.tuwien.ac.at> wrote: >> > >> > Hi Pierre, >> > >> >> I’m using GAMG on a shifted Laplacian with these options: >> >> -st_fieldsplit_pressure_ksp_type preonly >> >> -st_fieldsplit_pressure_pc_composite_type additive >> >> -st_fieldsplit_pressure_pc_type composite >> >> -st_fieldsplit_pressure_sub_0_ksp_pc_type jacobi >> >> -st_fieldsplit_pressure_sub_0_pc_type ksp >> >> -st_fieldsplit_pressure_sub_1_ksp_pc_gamg_square_graph 10 >> >> -st_fieldsplit_pressure_sub_1_ksp_pc_type gamg >> >> -st_fieldsplit_pressure_sub_1_pc_type ksp >> >> and I end up with the following logs on 512 (top) and 2048 (bottom) >> processes: >> >> MatMult 1577790 1.0 3.1967e+03 1.2 4.48e+12 1.6 7.6e+09 >> 5.6e+03 0.0e+00 7 71 75 63 0 7 71 75 63 0 650501 >> >> MatMultAdd 204786 1.0 1.3412e+02 5.5 1.50e+10 1.7 5.5e+08 >> 2.7e+02 0.0e+00 0 0 5 0 0 0 0 5 0 0 50762 >> >> MatMultTranspose 204786 1.0 4.6790e+01 4.3 1.50e+10 1.7 5.5e+08 >> 2.7e+02 0.0e+00 0 0 5 0 0 0 0 5 0 0 145505 >> >> [..] >> >> KSPSolve_FS_3 7286 1.0 7.5506e+02 1.0 9.14e+11 1.8 7.3e+09 >> 1.5e+03 2.6e+05 2 14 71 16 34 2 14 71 16 34 539009 >> >> MatMult 1778795 1.0 3.5511e+03 4.1 1.46e+12 1.9 4.0e+10 >> 2.4e+03 0.0e+00 7 66 75 61 0 7 66 75 61 0 728371 >> >> MatMultAdd 222360 1.0 2.5904e+0348.0 4.31e+09 1.9 2.4e+09 >> 1.3e+02 0.0e+00 14 0 4 0 0 14 0 4 0 0 2872 >> >> MatMultTranspose 222360 1.0 1.8736e+03421.8 4.31e+09 1.9 2.4e+09 >> 1.3e+02 0.0e+00 0 0 4 0 0 0 0 4 0 0 3970 >> >> [..] >> >> KSPSolve_FS_3 7412 1.0 2.8939e+03 1.0 2.66e+11 2.1 3.5e+10 >> 6.1e+02 2.7e+05 17 11 67 14 28 17 11 67 14 28 148175 >> >> MatMultAdd and MatMultTranspose (performed by GAMG) somehow ruin the >> scalability of the overall solver. The pressure space “only” has 3M >> unknowns so I’m guessing that’s why GAMG is having a hard time strong >> scaling. >> > >> > 3M unknowns divided by 512 processes implies less than 10k unknowns per >> process. It is not unusual to see strong scaling roll off at this size. >> Also note that the time per call(!) for "MatMult" is the same for both >> cases, indicating that your run into a latency-limited regime. >> > >> > Also, have a look at the time ratios: With 2048 processes, MatMultAdd >> and MatMultTranspose show a time ratio of 48 and 421, respectively. Maybe >> one of your MPI ranks is getting a huge workload? >> >> Maybe inside GAMG itself (how could I check this?), but since the timing >> and ratio of the MatMult look OK and the distribution of the pressure space >> is the same as the other three fields, I’m guessing this does not come from >> my global Mat, but I may be wrong. >> >> >> For the other fields, the matrix is somehow distributed nicely, i.e., >> I don’t want to change the overall distribution of the matrix. >> >> Do you have any suggestion to improve the performance of GAMG in that >> scenario? I had two ideas in mind but please correct me if I’m wrong or if >> this is not doable: >> >> 1) before setting up GAMG, first use a PCTELESCOPE to avoid having too >> many processes work on this small problem >> >> 2) have the sub_0_ and the sub_1_ work on two different nonoverlapping >> communicators of size PETSC_COMM_WORLD/2, do the solve concurrently, and >> then sum the solutions (only worth doing because of -pc_composite_type >> additive). I have no idea if this easily doable with PETSc command line >> arguments >> > >> > 1) is the more flexible approach, as you have better control over the >> system sizes after 'telescoping’. >> >> Right, but the advantage of 2) is that I wouldn't have one half or more >> of processes idling and I could overlap the solves of both subpc in the >> PCCOMPOSITE. >> >> I’m attaching the -log_view for both runs (I trimmed some options). >> >> Thanks for your help, >> Pierre >> >> >> >> > Best regards, >> > Karli >> >> >> >
