Looks like you've installed petsc with --download-mpich - so you should use the corresponding mpiexec [its in PETSC_DIR/PETSC_ARCH/bin/]
Since you already have 2 installs of MPI - you could have used one of them for PETSc install [by specifying the corresponding mpicc,mpif90 to PETSc configure -] instead of --download-mpich. Satish On Mon, 17 Jan 2011, Gaurish Telang wrote: > This is what I get on running mpiexec -n 2 ./ex23 -info > > Also, using mpirun in place of mpiexec and using the -info option I get the > exact same output you see below. > > As far as the MPI implmentation I am using, I have OpenMPI and MPICH > installed on my laptop. > > While installing PETSc there were some external packages required. In the > external packages folder I can see the following softwares: > > fblaslapack-3.1.1 mpich2-1.0.8 ParMetis-dev-p3 SuperLU_DIST_2.4-hg-v2 > > Possibly it is this mpich2 that should be used?? > Please let me know what I should do. I am quite new to PETSc. > > gaurish108 at > gaurish108-laptop:~/Desktop/ResearchMeetings/SUPERPETS/petsc-3.1-p5/src/ksp/ksp/examples/tutorials$ > mpiexec -n 2 ./ex23 -info > [0] PetscInitialize(): PETSc successfully started: number of processors = 1 > [0] PetscGetHostName(): Rejecting domainname, likely is NIS > gaurish108-laptop.(none) > [0] PetscInitialize(): Running on machine: gaurish108-laptop > [0] PetscCommDuplicate(): Duplicating a communicator 1140850688 -2080374784 > max tags = 2147483647 > [0] PetscCommDuplicate(): returning tag 2147483647 > [0] PetscCommDuplicate(): returning tag 2147483646 > [0] PetscCommDuplicate(): returning tag 2147483645 > [0] PetscInitialize(): PETSc successfully started: number of processors = 1 > [0] PetscGetHostName(): Rejecting domainname, likely is NIS > gaurish108-laptop.(none) > [0] PetscInitialize(): Running on machine: gaurish108-laptop > [0] PetscCommDuplicate(): Duplicating a communicator 1140850688 -2080374784 > max tags = 2147483647 > [0] PetscCommDuplicate(): returning tag 2147483647 > [0] PetscCommDuplicate(): returning tag 2147483646 > [0] PetscCommDuplicate(): returning tag 2147483645 > [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850688 > -2080374784 > [0] PetscCommDuplicate(): returning tag 2147483644 > [0] MatSetUpPreallocation(): Warning not preallocating matrix storage > [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 10 X 10; storage space: 22 > unneeded,28 used > [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 > [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 3 > [0] Mat_CheckInode(): Found 10 nodes out of 10 rows. Not using Inode > routines > [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850688 > -2080374784 > [0] PetscCommDuplicate(): returning tag 2147483643 > [0] PetscCommDuplicate(): returning tag 2147483642 > [0] PetscCommDuplicate(): returning tag 2147483641 > [0] PetscCommDuplicate(): returning tag 2147483640 > [0] PetscCommDuplicate(): returning tag 2147483639 > [0] PetscCommDuplicate(): returning tag 2147483638 > [0] PetscCommDuplicate(): returning tag 2147483637 > [0] PCSetUp(): Setting up new PC > [0] PetscCommDuplicate(): returning tag 2147483636 > [0] PetscCommDuplicate(): returning tag 2147483635 > [0] PetscCommDuplicate(): returning tag 2147483634 > [0] PetscCommDuplicate(): returning tag 2147483633 > [0] PetscCommDuplicate(): returning tag 2147483632 > [0] PetscCommDuplicate(): returning tag 2147483631 > [0] PetscCommDuplicate(): returning tag 2147483630 > [0] PetscCommDuplicate(): returning tag 2147483629 > [0] PetscCommDuplicate(): returning tag 2147483628 > [0] PetscCommDuplicate(): returning tag 2147483627 > [0] PetscCommDuplicate(): returning tag 2147483626 > [0] KSPDefaultConverged(): Linear solver has converged. Residual norm > 4.50879e-16 is less than relative tolerance 1e-07 times initial right hand > side norm 0.707107 at iteration 5 > [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850688 > -2080374784 > [0] PetscCommDuplicate(): returning tag 2147483625 > KSP Object: > type: gmres > GMRES: restart=30, using Classical (unmodified) Gram-Schmidt > Orthogonalization with no iterative refinement > GMRES: happy breakdown tolerance 1e-30 > maximum iterations=10000, initial guess is zero > tolerances: relative=1e-07, absolute=1e-50, divergence=10000 > left preconditioning > using PRECONDITIONED norm type for convergence test > PC Object: > type: jacobi > linear system matrix = precond matrix: > Matrix Object: > type=seqaij, rows=10, cols=10 > total: nonzeros=28, allocated nonzeros=50 > not using I-node routines > Norm of error < 1.e-12, Iterations 5 > [0] PetscFinalize(): PetscFinalize() called > [0] PetscCommDuplicate(): returning tag 2147483624 > [0] Petsc_DelViewer(): Deleting viewer data in an MPI_Comm -2080374784 > [0] Petsc_DelComm(): Deleting PETSc communicator imbedded in a user MPI_Comm > 1140850688 > [0] PetscCommDestroy(): Deleting PETSc MPI_Comm -2080374784 > [0] Petsc_DelCounter(): Deleting counter data in an MPI_Comm -2080374784 > [0] Petsc_DelComm(): Deleting PETSc communicator imbedded in a user MPI_Comm > -2080374784 > [0] Petsc_DelViewer(): Deleting viewer data in an MPI_Comm -2080374784 > [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850688 > -2080374784 > [0] PetscCommDuplicate(): returning tag 2147483644 > [0] MatSetUpPreallocation(): Warning not preallocating matrix storage > [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 10 X 10; storage space: 22 > unneeded,28 used > [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 > [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 3 > [0] Mat_CheckInode(): Found 10 nodes out of 10 rows. Not using Inode > routines > [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850688 > -2080374784 > [0] PetscCommDuplicate(): returning tag 2147483643 > [0] PetscCommDuplicate(): returning tag 2147483642 > [0] PetscCommDuplicate(): returning tag 2147483641 > [0] PetscCommDuplicate(): returning tag 2147483640 > [0] PetscCommDuplicate(): returning tag 2147483639 > [0] PetscCommDuplicate(): returning tag 2147483638 > [0] PetscCommDuplicate(): returning tag 2147483637 > [0] PCSetUp(): Setting up new PC > [0] PetscCommDuplicate(): returning tag 2147483636 > [0] PetscCommDuplicate(): returning tag 2147483635 > [0] PetscCommDuplicate(): returning tag 2147483634 > [0] PetscCommDuplicate(): returning tag 2147483633 > [0] PetscCommDuplicate(): returning tag 2147483632 > [0] PetscCommDuplicate(): returning tag 2147483631 > [0] PetscCommDuplicate(): returning tag 2147483630 > [0] PetscCommDuplicate(): returning tag 2147483629 > [0] PetscCommDuplicate(): returning tag 2147483628 > [0] PetscCommDuplicate(): returning tag 2147483627 > [0] PetscCommDuplicate(): returning tag 2147483626 > [0] KSPDefaultConverged(): Linear solver has converged. Residual norm > 4.50879e-16 is less than relative tolerance 1e-07 times initial right hand > side norm 0.707107 at iteration 5 > [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850688 > -2080374784 > [0] PetscCommDuplicate(): returning tag 2147483625 > KSP Object: > type: gmres > GMRES: restart=30, using Classical (unmodified) Gram-Schmidt > Orthogonalization with no iterative refinement > GMRES: happy breakdown tolerance 1e-30 > maximum iterations=10000, initial guess is zero > tolerances: relative=1e-07, absolute=1e-50, divergence=10000 > left preconditioning > using PRECONDITIONED norm type for convergence test > PC Object: > type: jacobi > linear system matrix = precond matrix: > Matrix Object: > type=seqaij, rows=10, cols=10 > total: nonzeros=28, allocated nonzeros=50 > not using I-node routines > Norm of error < 1.e-12, Iterations 5 > [0] PetscFinalize(): PetscFinalize() called > [0] PetscCommDuplicate(): returning tag 2147483624 > [0] Petsc_DelViewer(): Deleting viewer data in an MPI_Comm -2080374784 > [0] Petsc_DelComm(): Deleting PETSc communicator imbedded in a user MPI_Comm > 1140850688 > [0] PetscCommDestroy(): Deleting PETSc MPI_Comm -2080374784 > [0] Petsc_DelCounter(): Deleting counter data in an MPI_Comm -2080374784 > [0] Petsc_DelComm(): Deleting PETSc communicator imbedded in a user MPI_Comm > -2080374784 > [0] Petsc_DelViewer(): Deleting viewer data in an MPI_Comm -2080374784 > gaurish108 at > gaurish108-laptop:~/Desktop/ResearchMeetings/SUPERPETS/petsc-3.1-p5/src/ksp/ksp/examples/tutorials$ > > > On Mon, Jan 17, 2011 at 5:46 PM, Gaurish Telang <gaurish108 at > gmail.com>wrote: > > > Hi. > > > > I had two questions > > > > (1) > > > > I was curious to know why the following happens with the PETSc standard > > output. Having created the executable 'test' when I try to run it with > > mpiexec -n 2 ./test > > the same output is printed to the terminal twice. If I use 3 processors, > > then the same output is printed thrice. > > > > In short the number of processors = number of times the output from PETSc > > is printed. Could this be a mistake with my PETSc installation??? > > > > For example, consider the code in src/ksp/ksp/examples/tutorials/ex23.c > > After creating ex23 the executable and running it with two processors gives > > the following terminal output: > > > > gaurish108 at > > gaurish108-laptop:~/Desktop/ResearchMeetings/SUPERPETS/petsc-3.1-p5/src/ksp/ksp/examples/tutorials$ > > mpiexec -n 1 ./ex23 > > KSP Object: > > type: gmres > > GMRES: restart=30, using Classical (unmodified) Gram-Schmidt > > Orthogonalization with no iterative refinement > > GMRES: happy breakdown tolerance 1e-30 > > maximum iterations=10000, initial guess is zero > > tolerances: relative=1e-07, absolute=1e-50, divergence=10000 > > left preconditioning > > using PRECONDITIONED norm type for convergence test > > PC Object: > > type: jacobi > > linear system matrix = precond matrix: > > Matrix Object: > > type=seqaij, rows=10, cols=10 > > total: nonzeros=28, allocated nonzeros=50 > > not using I-node routines > > Norm of error < 1.e-12, Iterations 5 > > gaurish108 at > > gaurish108-laptop:~/Desktop/ResearchMeetings/SUPERPETS/petsc-3.1-p5/src/ksp/ksp/examples/tutorials$ > > mpiexec -n 2 ./ex23 > > KSP Object: > > type: gmres > > GMRES: restart=30, using Classical (unmodified) Gram-Schmidt > > Orthogonalization with no iterative refinement > > GMRES: happy breakdown tolerance 1e-30 > > maximum iterations=10000, initial guess is zero > > tolerances: relative=1e-07, absolute=1e-50, divergence=10000 > > left preconditioning > > using PRECONDITIONED norm type for convergence test > > PC Object: > > type: jacobi > > linear system matrix = precond matrix: > > Matrix Object: > > type=seqaij, rows=10, cols=10 > > total: nonzeros=28, allocated nonzeros=50 > > not using I-node routines > > Norm of error < 1.e-12, Iterations 5 > > KSP Object: > > type: gmres > > GMRES: restart=30, using Classical (unmodified) Gram-Schmidt > > Orthogonalization with no iterative refinement > > GMRES: happy breakdown tolerance 1e-30 > > maximum iterations=10000, initial guess is zero > > tolerances: relative=1e-07, absolute=1e-50, divergence=10000 > > left preconditioning > > using PRECONDITIONED norm type for convergence test > > PC Object: > > type: jacobi > > linear system matrix = precond matrix: > > Matrix Object: > > type=seqaij, rows=10, cols=10 > > total: nonzeros=28, allocated nonzeros=50 > > not using I-node routines > > Norm of error < 1.e-12, Iterations 5 > > gaurish108 at > > gaurish108-laptop:~/Desktop/ResearchMeetings/SUPERPETS/petsc-3.1-p5/src/ksp/ksp/examples/tutorials$ > > > > > > > > > > (2) > > > > Also I was told yesterday on the PETSC users mailing list that the MATLAB m > > file PetscBinaryWrite.m converts a sparse matrix in MATLAB into Petsc Binary > > format. > > The following are the comments in the code near the heading saying that > > it works only for square sparse matrices . But it seems to be working quite > > well for rectangular sparse MATLAB matrices also. > > I have tested this in conjunction with PetscBinaryRead.m also, which reads > > in a Petsc binary file into MATLAB as a sparse matrix. > > > > Is there something I might have missed or some error that I might be > > making??? > > > > Comments in PetscBinaryWrite.m > > "-================================================ > > % Writes in PETSc binary file sparse matrices and vectors > > % if the array is multidimensional and dense it is saved > > % as a one dimensional array > > % > > % Only works for square sparse matrices > > %: > > .. > > .. > > .. > > .. > > .. > > .. > > . > > . > > . > > > > > > > > > > >
