Hello Matt, thanks for your quick reply. My situation is a bit different. What I am solving is a generalized eigenvalue problem. I only recently switched from scipy.eigs to slepc, because slepc support parallel eigenvalue problem. In other word, I am adapting my old code to utilizing slepc/petsc parallel support. To have a first test, I saved the matrix from eigenvalue problem with old code. Then read in this matrix for slepc/petsc validation. That's why I need to scatter it to processors. Do you have any idea how can I achieve this? with regards Yongxiang

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On 6 April 2018 at 15:00, Matthew Knepley <knep...@gmail.com> wrote: > On Fri, Apr 6, 2018 at 8:44 AM, Yongxiang Wu <yongxian...@gmail.com> > wrote: > >> Hello, everyone, >> >> I already have a scipy sparse square matrix L0 . Since my problem is >> large, parallel run is preferred. My Question is, how can I scatter my L0 >> to each of the processors? In the following code, I can get the indices >> of the localized part of the matrix. In the tutorial, the matrix element >> are directly assign with value, but in my case, the matrix are so large, >> assign each element in loop (commented code) is not efficient. So if any >> function would do the mpi scatter work? >> > > Hi Yongxiang, > > It would be really anomalous for what you propose to results in any > speedup at all. If the matrix is large, > it will not fit on one process. Any speedup from using more processes will > be eaten up by the time to > communicate the matrix. I would focus on generating the matrix in parallel. > > Thanks, > > Matt > > >> With regards and Thanks. >> >> import sys, slepc4py >> slepc4py.init(sys.argv) >> from petsc4py import PETSc >> from slepc4py import SLEPc >> >> opts = PETSc.Options() >> opts.setValue('-st_pc_factor_mat_solver_package','mumps') >> >> A = PETSc.Mat().createAIJ(size=L0.shape,comm=PETSc.COMM_WORLD) >> A.setUp() >> >> Istart, Iend = A.getOwnershipRange()# for I in range(Istart,Iend):# >> for J in range(0,L0.shape[0]):# A[I,J] = L0[I,J] >> >> The flowing code, would make the assignment from the scipy sparse matrix >> L0 to PETSc matrix A. But this would only work for one process. >> >> A = PETSc.Mat().createAIJ(size=L0.shape, >> csr=(L0.indptr, L0.indices, >> L0.data), comm=PETSc.COMM_WORLD) >> >> >> > > > -- > What most experimenters take for granted before they begin their > experiments is infinitely more interesting than any results to which their > experiments lead. > -- Norbert Wiener > > https://www.cse.buffalo.edu/~knepley/ <http://www.caam.rice.edu/~mk51/> >