Dense matrix is stored in column-major order, so you might want to use
MatGetColumnVector() to access columns of the matrix instead of the rows. For
instance, you can get the n-th column out of the matrix and place it into a
vector that shares the same parallel layout as the matrix with the following:
MatDenseGetColumn(J,n,&xarr);
VecPlaceArray(vec,xarr);
...
VecResetArray(vec);
MatDenseRestoreColumn(J,&xarr);
Hong (Mr.)
On Jun 10, 2020, at 8:08 AM, Eda Oktay
<eda.ok...@metu.edu.tr<mailto:eda.ok...@metu.edu.tr>> wrote:
Dear Matt,
Matthew Knepley <knep...@gmail.com<mailto:knep...@gmail.com>>, 10 Haz 2020 Çar,
16:03 tarihinde
şunu yazdı:
On Wed, Jun 10, 2020 at 8:56 AM Eda Oktay <eda.ok...@metu.edu.tr> wrote:
Hi all,
I am trying to get all the rows of a parallel matrix as individual
vectors. For instance, if I have 72*4 matrix, I want to get 72
different vectors having size 4.
As far as I understood, MatGetRow is only for local rows, so
MatGetOwnershipRange is used, however, when I tried this one, I
couldn't get the whole and desired row vectors.
In MatGetRow explanation, it is written that I should use
MatCreateSubMatrices first, then use MatGetRow. But I couldn't
understand to which extent I should create submatrices. I just need to
have all 72 rows as 72 different vectors each having 4 elements.
1) For sparse matrices, the storage is always divided by row, so that values
can only be retrieved for local rows with MatGetRow()
2) Is this matrix sparse? It sounds like it is dense.
Matrix is dense.
3) Are you asking to get all matrix values on all processes? If so, I think the
easiest thing to do is first wrap a Vec around the
values, then use VecScatterToAll(), then wrap each one in a MatDense again.
Yes, I want all row vectors on all processes. In a dense matrix,
should I still wrap a Vec around the values? I know I should use
scatter but I couldn't even wrap a Vec around them.
Thanks so much!
Eda
Thanks,
Matt
Thanks!
Eda
--
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/
