[petsc-users] Need advice to do blockwise matrix-vector multiplication

[Fangbo Wang]

Thank you very much for your reply, Barry.

The block matrix size is 1.35 million by 1.35 million, and the matrix can
be divided into 45 blocks along each direction, so there are 45X45=2025
blocks in the matrix (size of each block is 30,000 by 30,000) . Fortunately
there are only around 700 non-zeros blocks which are also sparse.
Correspondingly,
the unknown vector and right-hand-side vector (both with size 1.35million
by 1) can also be divided into 45 block vectors.

 Most of the blocks are similar, assume I have a block *A*, there are
dozens of blocks similar to* A* by a scalar multiplication (*2A*, *3A*
...). Finally, I only need to save 45 different blocks and a few scalars
for this matrix to save memory usage(the reason why 45 different blocks
comes from physical concept).


Is this clear to explain my problem?

Thank you very much!

Best regards,

Fangbo
​






On Thu, Jan 19, 2017 at 6:19 PM, Barry Smith <bsmith at mcs.anl.gov> wrote:

>
> > On Jan 19, 2017, at 4:10 PM, Fangbo Wang <fangbowa at buffalo.edu> wrote:
> >
> > Hi,
> >
> > Background:
> >
> > I am using stochastic finite element to solve a solid mechanics problem
> with random material properties. At the end of the day, I get a linear
> system of equations Ax=b to solve.
> >
> > The matrix A is very large with size of 1.3million by 1.3 million, and
> to save this matrix needs more than 100 G memory. Fortunately, matrix A has
> some nice features that it is a block matrix, most of the blocks inside the
> matrix are similar, each block is 10,000 by 10,000.
> >
> >
> > Hence, I only need to save some  blocks (in my case 45). Most of the
> computation  in my iterative solver is matrix-vec multiplication, that's
> why I want to do it using block matrices.
> > <FIG-2-Color-online-A-symmetric-block-Toeplitz-
> matrix-Each-block-is-also-a-symmetric.png>
> >
> >
> > ​
> >
> > Current:
> > I tried to parallelize all my 45 block matrices in all the processors,
> and all the corresponding 45 block vectors in all the processors. However,
> the computation seems to be very slow, and no scalability at all.
> > I am thinking of using small groups of processors to separate the
> computation, like using intra-communicators and inter-communicators. Maybe
> this will help to reduce the communication.
>
>    No, just make things excessively complex.
>
> >
> > Any one have some experiences on this? Is there any Petsc function to do
> these jobs? I am open to any suggestions.
>
>    Based on your picture it looks like if the matrix was explicitly formed
> it would be dense? Or are your 45 "small matrices" sparse? Are there any
> "empty" block matrices in your diagram or are they all one of the 45 small
> ones?
>
>    There are two ways to order your unknowns; one with all unknowns for
> one "block" then all unknowns for the next block ... or interlacing the
> unknowns between blocks. Depending on the structure of the problem one or
> the other way can be significently better.
>
>    The MatNest construct may be the way to go; it will behave like forming
> the full matrix but for each block in the matrix you would just have a
> pointer to the correct small matrix so you don't store the individual
> matrices more than once.
>
>    Also if you get no speed up you need to verify that it is not due to
> the hardware or badly configured software so run the streams benchmark and
> make sure you have a good MPI binding http://www.mcs.anl.gov/petsc/
> documentation/faq.html#computers
>
>
>
>
>   Barry
>
> >
> > Thank you very much!
> >
> >
> >
> > Fangbo Wang
> >
> > --
> > Fangbo Wang, PhD student
> > Stochastic Geomechanics Research Group
> > Department of Civil, Structural and Environmental Engineering
> > University at Buffalo
> > Email: fangbowa at buffalo.edu
>
>


-- 
Fangbo Wang, PhD student
Stochastic Geomechanics Research Group
Department of Civil, Structural and Environmental Engineering
University at Buffalo
Email: *fangbowa at buffalo.edu <fangbowa at buffalo.edu>*
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