On Mon, Aug 1, 2011 at 5:09 PM, Barry Smith <bsmith at mcs.anl.gov> wrote:
> > On Aug 1, 2011, at 3:00 PM, Adam Byrd wrote: > > > Hello, > > > > I'm looking for help reducing the time and communication of a parallel > MatMatSolve using MUMPS. On a single processor I experience decent solve > times (~9 seconds each), but when moving to multiple processors I see longer > times with more cores. I've run with -log_summary and confirmed > (practically) all the time is spent in MatMatSolve. I'm fairly certain it's > all communication between nodes and I'm trying to figure out where I can > make optimizations, or if it is even feasible for this type of problem. It > is a parallel, dense, > > I hope you mean that the original matrix you use with MUMPS is sparse > (you should not use MUMPS to solve dense linear systems). > Oops, yes. The original matrix is sparse. It requires the solution and identity matrix to be dense. I was typing faster than thinking. > > > direct solve using MUMPS with an LU preconditioner. I know there are many > smaller optimizations that can be done in other areas, but at the moment it > is only the solve that concerns me. > > MUMPS will run slower on 2 processors than 1, this is just a fact of > life. You will only gain with parallel for MUMPS for large problems. > I see. It looks like I took off in the wrong direction then. I'm trying to solve for the inverse of a sparse matrix in parallel. I'm starting at 3600x3600 and will be moving to 30,000x30,000+ in the future. Which solver suits this sort of problem? > > Barry > > > > > > > ---------------------------------------------- PETSc Performance Summary: > ---------------------------------------------- > > > > ./cntor on a complex-c named hpc-1-0.local with 2 processors, by abyrd > Mon Aug 1 16:25:51 2011 > > Using Petsc Release Version 3.1.0, Patch 8, Thu Mar 17 13:37:48 CDT 2011 > > > > Max Max/Min Avg Total > > Time (sec): 1.307e+02 1.00000 1.307e+02 > > Objects: 1.180e+02 1.00000 1.180e+02 > > Flops: 0.000e+00 0.00000 0.000e+00 0.000e+00 > > Flops/sec: 0.000e+00 0.00000 0.000e+00 0.000e+00 > > Memory: 2.091e+08 1.00001 4.181e+08 > > MPI Messages: 7.229e+03 1.00000 7.229e+03 1.446e+04 > > MPI Message Lengths: 4.141e+08 1.00000 5.729e+04 8.283e+08 > > MPI Reductions: 1.464e+04 1.00000 > > > > Flop counting convention: 1 flop = 1 real number operation of type > (multiply/divide/add/subtract) > > e.g., VecAXPY() for real vectors of length N > --> 2N flops > > and VecAXPY() for complex vectors of length N > --> 8N flops > > > > Summary of Stages: ----- Time ------ ----- Flops ----- --- Messages > --- -- Message Lengths -- -- Reductions -- > > Avg %Total Avg %Total counts > %Total Avg %Total counts %Total > > 0: Main Stage: 1.3072e+02 100.0% 0.0000e+00 0.0% 1.446e+04 > 100.0% 5.729e+04 100.0% 1.730e+02 1.2% > > > > > ------------------------------------------------------------------------------------------------------------------------ > > See the 'Profiling' chapter of the users' manual for details on > interpreting output. > > Phase summary info: > > Count: number of times phase was executed > > Time and Flops: Max - maximum over all processors > > Ratio - ratio of maximum to minimum over all > processors > > Mess: number of messages sent > > Avg. len: average message length > > Reduct: number of global reductions > > Global: entire computation > > Stage: stages of a computation. Set stages with PetscLogStagePush() > and PetscLogStagePop(). > > %T - percent time in this phase %F - percent flops in this > phase > > %M - percent messages in this phase %L - percent message > lengths in this phase > > %R - percent reductions in this phase > > Total Mflop/s: 10e-6 * (sum of flops over all processors)/(max time > over all processors) > > > ------------------------------------------------------------------------------------------------------------------------ > > > > > > ########################################################## > > # # > > # WARNING!!! # > > # # > > # This code was compiled with a debugging option, # > > # To get timing results run config/configure.py # > > # using --with-debugging=no, the performance will # > > # be generally two or three times faster. # > > # # > > ########################################################## > > > > > > > > > > ########################################################## > > # # > > # WARNING!!! # > > # # > > # The code for various complex numbers numerical # > > # kernels uses C++, which generally is not well # > > # optimized. For performance that is about 4-5 times # > > # faster, specify --with-fortran-kernels=1 # > > # when running config/configure.py. # > > # # > > ########################################################## > > > > > > Event Count Time (sec) Flops > --- Global --- --- Stage --- Total > > Max Ratio Max Ratio Max Ratio Mess Avg len > Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s > > > ------------------------------------------------------------------------------------------------------------------------ > > > > --- Event Stage 0: Main Stage > > > > MatSolve 14400 1.0 1.2364e+02 1.0 0.00e+00 0.0 1.4e+04 5.7e+04 > 2.0e+01 95 0100100 0 95 0100100 12 0 > > MatLUFactorSym 4 1.0 2.0027e-05 1.4 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 > > MatLUFactorNum 4 1.0 3.4223e+00 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 2.4e+01 3 0 0 0 0 3 0 0 0 14 0 > > MatConvert 1 1.0 2.3644e-01 2.4 0.00e+00 0.0 0.0e+00 0.0e+00 > 1.1e+01 0 0 0 0 0 0 0 0 0 6 0 > > MatAssemblyBegin 14 1.0 1.9959e-01 9.3 0.00e+00 0.0 3.0e+01 5.2e+04 > 1.2e+01 0 0 0 0 0 0 0 0 0 7 0 > > MatAssemblyEnd 14 1.0 1.9908e-01 1.1 0.00e+00 0.0 4.0e+00 2.8e+01 > 2.0e+01 0 0 0 0 0 0 0 0 0 12 0 > > MatGetRow 32 1.0 4.2677e-05 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 > > MatGetSubMatrice 4 1.0 7.6661e-03 1.0 0.00e+00 0.0 1.6e+01 1.2e+05 > 2.4e+01 0 0 0 0 0 0 0 0 0 14 0 > > MatMatSolve 4 1.0 1.2380e+02 1.0 0.00e+00 0.0 1.4e+04 5.7e+04 > 2.0e+01 95 0100100 0 95 0100100 12 0 > > VecSet 4 1.0 1.8590e-02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 > > VecScatterBegin 28800 1.0 2.2810e+00 2.2 0.00e+00 0.0 1.4e+04 5.7e+04 > 0.0e+00 1 0100100 0 1 0100100 0 0 > > VecScatterEnd 14400 1.0 4.1534e+00 2.2 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 2 0 0 0 0 2 0 0 0 0 0 > > KSPSetup 4 1.0 1.1060e-0212.6 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 > > PCSetUp 4 1.0 3.4280e+00 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 5.6e+01 3 0 0 0 0 3 0 0 0 32 0 > > > ------------------------------------------------------------------------------------------------------------------------ > > > > Memory usage is given in bytes: > > > > Object Type Creations Destructions Memory Descendants' > Mem. > > Reports information only for process 0. > > > > --- Event Stage 0: Main Stage > > > > Matrix 27 27 208196712 0 > > Vec 36 36 1027376 0 > > Vec Scatter 11 11 7220 0 > > Index Set 42 42 22644 0 > > Krylov Solver 1 1 34432 0 > > Preconditioner 1 1 752 0 > > > ======================================================================================================================== > > Average time to get PetscTime(): 1.90735e-07 > > Average time for MPI_Barrier(): 3.8147e-06 > > Average time for zero size MPI_Send(): 7.51019e-06 > > #PETSc Option Table entries: > > -log_summary > > -pc_factor_mat_solver_package mumps > > -pc_type lu > > #End of PETSc Option Table entries > > Compiled without FORTRAN kernels > > Compiled with full precision matrices (default) > > sizeof(short) 2 sizeof(int) 4 sizeof(long) 8 sizeof(void*) 8 > sizeof(PetscScalar) 16 > > Configure run at: Mon Jul 11 15:28:42 2011 > > Configure options: PETSC_ARCH=complex-cpp-mumps --with-cc=mpicc > --with-fc=mpif90 --with-blas-lapack-dir=/usr/lib64 --with-shared > --with-clanguage=c++ --with-scalar-type=complex --download-mumps=1 > --download-blacs=1 --download-scalapack=1 --download-parmetis=1 > --with-cxx=mpicxx > > ----------------------------------------- > > Libraries compiled on Mon Jul 11 15:39:58 EDT 2011 on sc.local > > Machine characteristics: Linux sc.local 2.6.18-194.11.1.el5 #1 SMP Tue > Aug 10 19:05:06 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux > > Using PETSc directory: /panfs/storage.local/scs/home/abyrd/petsc-3.1-p8 > > Using PETSc arch: complex-cpp-mumps > > ----------------------------------------- > > Using C compiler: mpicxx -Wall -Wwrite-strings -Wno-strict-aliasing -g > -fPIC > > Using Fortran compiler: mpif90 -fPIC -Wall -Wno-unused-variable -g > > ----------------------------------------- > > Using include paths: > -I/panfs/storage.local/scs/home/abyrd/petsc-3.1-p8/complex-cpp-mumps/include > -I/panfs/storage.local/scs/home/abyrd/petsc-3.1-p8/include > -I/panfs/storage.local/scs/home/abyrd/petsc-3.1-p8/complex-cpp-mumps/include > -I/usr/mpi/gnu/openmpi-1.4.2/include -I/usr/mpi/gnu/openmpi-1.4.2/lib64 > > ------------------------------------------ > > Using C linker: mpicxx -Wall -Wwrite-strings -Wno-strict-aliasing -g > > Using Fortran linker: mpif90 -fPIC -Wall -Wno-unused-variable -g > > Using libraries: > -Wl,-rpath,/panfs/storage.local/scs/home/abyrd/petsc-3.1-p8/complex-cpp-mumps/lib > -L/panfs/storage.local/scs/home/abyrd/petsc-3.1-p8/complex-cpp-mumps/lib > -lpetsc -lX11 > -Wl,-rpath,/panfs/storage.local/scs/home/abyrd/petsc-3.1-p8/complex-cpp-mumps/lib > -L/panfs/storage.local/scs/home/abyrd/petsc-3.1-p8/complex-cpp-mumps/lib > -lcmumps -ldmumps -lsmumps -lzmumps -lmumps_common -lpord -lparmetis -lmetis > -lscalapack -lblacs -Wl,-rpath,/usr/lib64 -L/usr/lib64 -llapack -lblas -lnsl > -lrt -Wl,-rpath,/usr/mpi/gnu/openmpi-1.4.2/lib64 > -L/usr/mpi/gnu/openmpi-1.4.2/lib64 > -Wl,-rpath,/usr/lib/gcc/x86_64-redhat-linux/4.1.2 > -L/usr/lib/gcc/x86_64-redhat-linux/4.1.2 -ldl -lmpi -lopen-rte -lopen-pal > -lnsl -lutil -lgcc_s -lpthread -lmpi_f90 -lmpi_f77 -lgfortran -lm -lm -lm > -lm -lmpi_cxx -lstdc++ -lmpi_cxx -lstdc++ -ldl -lmpi -lopen-rte -lopen-pal > -lnsl -lutil -lgcc_s -lpthread -ldl > > > > Respectfully, > > Adam Byrd > > <PETScCntor.zip> > > -------------- next part -------------- An HTML attachment was scrubbed... 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