efficiency of tranposing a Matrix?

[Hong Zhang]

Shi,

I added preallocation to MatTranspose_MPIAIJ(),
in which, d_nnz is computed from L, but
o_nnz is set as d_nnz.
This avoids data communication,
and allocates sufficient space in most cases,
I believe :-)

You may either get petsc-dev,
or replace MatTranspose_MPIAIJ() in your
~petsc/src/mat/impls/aij/mpi/mpiaij.c
with the one attached. Then rebuild the petsc lib.

Let us know if you still have slow down
in MatTranspose().


Hong

On Wed, 21 Feb 2007, Shi Jin wrote:

> Hi there,
>
> I have a code that keeps on using the same matrix L
> and its transpose in all time updates.
> I can improve the performance of the code by replacing
> the MatMultTranspose() with MatMult() and computing
> the transposed matrix at the beginning of the code for
> only once. The cost is of course extra storage of the
> transposed matrix.
>
> However, I have a question regarding the efficiency of
> transposing the matrix. I created the Matrix L with
> MPIAIJ and preallocated the proper memory for it.
> Then I call MatTranspose(L,&LT) to compute LT which is
> the transposed L. But I noticed that this process is
> extremely slow, 6 times slower than the creation of
> Matrix L itself.
>
> The first question is do I need to preallocate the
> memory  for LT also? I didn't do it since I suppose
> PETSc is smart enough to figure out the necessary
> storage.
>
> Secondly, I am not sure why MatTranspose is so slow. I
> understand in order to transpose a Matrix, one may
> need to call MPI_Alltoall which is extremely
> expensive. But it seems trivial that I can go through
> a similar process of creating the Matrix L and be much
> faster. I am not sure how MatTraspose() is implemented
> and whether I should actually compose LT instead of
> transpose L.
>
> Thank you very much.
>
> Shi
>
>
>
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