[petsc-users] Merge two MATMPIAIJ into one

Matthew Knepley knepley at gmail.com
Mon Jan 11 13:41:20 CST 2010


On Mon, Jan 11, 2010 at 1:21 PM, Barry Smith <bsmith at mcs.anl.gov> wrote:

>
> On Jan 11, 2010, at 12:38 PM, Vasia Kalavri wrote:
>
>  Hello.
>> I could use some help regarding the following issues:
>>
>> 1. I have created 2 structurally identical MATMPIJ matrices A1 and A2 with
>> N rows and M columns. I want to merge them in order to create a matrix of
>> the double size A3 with 2*N rows and M columns so that the first row of A2
>> will be the N+1-th row of the new matrix. Is there any function I could use
>> to do this?
>>
>
>   Since PETSc lays out the matrix rows across processors by having the
> first set of rows on the first process, the next set of rows on the next
> process etc. where is your "appended matrix" rows going to live? Do you want
> the new matrix to have the A1 matrix on the first half of the processors and
> the A2 matrix on the second half of the processors?
>
>   PETSc doesn't have tools for "putting together" collections of sparse
> parallel matrices, so there is some direct access to the sparse matrix you
> need to do to get what you want.
>

Dave May at ETHZ had a package called PetscExt that does compose sparse
matrices.

   Matt


>
>> 2. Is there a way to split a matrix after its values have been set by 1
>> process? What I have done is to create a MATMPIJ and make that process send
>> the data to the other processes in order to get the distributed matrix. But
>> I hope there is a smarter way to do this!
>>
>
>   In general one should create the entries to a PETSc matrix on the same
> number of processors as where the matrix lives so one does not need to
> "spread out" the matrix entries.
> Again, there isn't code to spread around sparse matrix entries.
>
>
>   Barry
>
>
>
>> Any idea is welcome,
>> Vasia.
>>
>> Windows Live: Keep your friends up to date with what you do online.
>>
>
>


-- 
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
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