> On Sep 16, 2016, at 7:52 PM, Ji Zhang <got...@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: got...@gmail.com
>
>
>
>
> Wayne
>
> On Sat, Sep 17, 2016 at 2:24 AM, Barry Smith <bsm...@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 <got...@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: got...@gmail.com
> >
> >
>
>
