[petsc-users] How to create a local to global mapping and construct matrix correctly

[Ji Zhang]

Thanks for your previous suggestion and the construction from little m to
big M have accomplished.

For a MPI program, a arbitrary matrix is been shorted in different cups
(i.e. 3), and each cup only contain part of then. So I think the matrix
have two kinds of indexes, a local one indicate the location of values at
the corresponding cup, and the global one indicate the location at the
whole matrix. I would like to know the relation between them and find the
way to shift the index from one to another.

I have one more question, the function VecGetArray() only return a pointer
to the local data array. What should I do if I need a pointer to the whole
data array?

Wayne

On Sat, Sep 17, 2016 at 9:00 AM, Barry Smith <bsmith at mcs.anl.gov> wrote:

>
> > On Sep 16, 2016, at 7:52 PM, Ji Zhang <gotofd at gmail.com> wrote:
> >
> > Sorry. What I mean is that, for example, I have a matrix
> >             [a1, a2, a3]
> >     mij = [b1, b2, b3] ,
> >             [c1, c2, c3]
> > and using 3 cups. Thus, mij in cpu 2 is
> >     mij_2 = [b1, b2, b3] .
> >
> > The local index of element b1 is (1, 1) and it's global index is (2, 1).
> How can I get the global index from the local index, and local index from
> global index?
>
>    That is something your code needs to generate and deal with, it is not
> something PETSc can do for you directly. You are defining the little m's
> and the big M and deciding where to put the little m's into the big ends.
> PETSc/we have no idea what the little m's represent in terms of the big M
> and where they would belong, that is completely the business of your
> application.
>
>    Barry
>
>
>
> >
> > Thanks.
> > 2016-09-17
> > Best,
> > Regards,
> > Zhang Ji
> > Beijing Computational Science Research Center
> > E-mail: gotofd at gmail.com
> >
> >
> >
> >
> > Wayne
> >
> > On Sat, Sep 17, 2016 at 2:24 AM, Barry Smith <bsmith at mcs.anl.gov> wrote:
> >
> >    "Gives wrong answers" is not very informative. What answer do you
> expect and what answer do you get?
> >
> >   Note that each process is looping over mSizes?
> >
> > for i in range(len(mSizes)):
> >     for j in range(len(mSizes)):
> >
> >   Is this what you want? It doesn't seem likely that you want all
> processes to generate all information in the matrix. Each process should be
> doing a subset of the generation.
> >
> >    Barry
> >
> > > On Sep 16, 2016, at 11:03 AM, Ji Zhang <gotofd at gmail.com> wrote:
> > >
> > > Dear all,
> > >
> > > I have a number of small 'mpidense' matrices mij, and I want to
> construct them to a big 'mpidense' matrix M like this:
> > >      [  m11  m12  m13  ]
> > > M =  |  m21  m22  m23  |   ,
> > >      [  m31  m32  m33  ]
> > >
> > > And a short demo is below. I'm using python, but their grammar are
> similar.
> > > import numpy as np
> > > from petsc4py import PETSc
> > > import sys, petsc4py
> > >
> > >
> > > petsc4py.init(sys.argv)
> > > mSizes = (2, 2)
> > > mij = []
> > >
> > > # create sub-matrices mij
> > > for i in range(len(mSizes)):
> > >     for j in range(len(mSizes)):
> > >         temp_m = PETSc.Mat().create(comm=PETSc.COMM_WORLD)
> > >         temp_m.setSizes(((None, mSizes[i]), (None, mSizes[j])))
> > >         temp_m.setType('mpidense')
> > >         temp_m.setFromOptions()
> > >         temp_m.setUp()
> > >         temp_m[:, :] = np.random.random_sample((mSizes[i], mSizes[j]))
> > >         temp_m.assemble()
> > >         temp_m.view()
> > >         mij.append(temp_m)
> > >
> > > # Now we have four sub-matrices. I would like to construct them into a
> big matrix M.
> > > M = PETSc.Mat().create(comm=PETSc.COMM_WORLD)
> > > M.setSizes(((None, np.sum(mSizes)), (None, np.sum(mSizes))))
> > > M.setType('mpidense')
> > > M.setFromOptions()
> > > M.setUp()
> > > mLocations = np.insert(np.cumsum(mSizes), 0, 0)    # mLocations = [0,
> mSizes]
> > > for i in range(len(mSizes)):
> > >     for j in range(len(mSizes)):
> > >         temp_m = mij[i*len(mSizes)+j].getDenseArray()
> > >         for k in range(temp_m.shape[0]):
> > >             M.setValues(mLocations[i]+k, np.arange(mLocations[j],
> mLocations[j+1],dtype='int32'), temp_m[k, :])
> > > M.assemble()
> > > M.view()
> > > The code works well in a single cup, but give wrong answer for 2 and
> more cores.
> > >
> > > Thanks.
> > > 2016-09-17
> > > Best,
> > > Regards,
> > > Zhang Ji
> > > Beijing Computational Science Research Center
> > > E-mail: gotofd at gmail.com
> > >
> > >
> >
> >
>
>
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