Hi Nikhil,

The RB method is really not intended to be used with such a large number of
Aq matrices, since the computational cost of RB grows quite fast with the
number of terms in the affine expansion (e.g. IIRC the error bound used in
the greedy depends on the number of terms to the power of 4). I think a
much better option here would be to try to reduce your number of Aq terms.
I don't know what problem you're trying to solve so I don't know if that is
possible or not, but I suggest you look into it. One option for this would
be to somehow split up your parameter domain to have fewer parameters.
Another would be to use EIM since that can compute an approximate affine
expansion which may be more efficient than what you're doing.

Best regards,
David

On Thu, Apr 23, 2020 at 1:06 AM Nikhil Vaidya <nikhilvaidy...@gmail.com>
wrote:

> Hi,
>
> I am using reduced basis for my work. The number of Aq matrices in my
> problem is very large (~250). Because of this, the memory requirements of
> my program are ~200 times the mesh-file size. I did some memory usage study
> and found out that the allocate_data_structures() function in
> RBConstruction is responsible for most of the memory usage.
>
> I think this might be due to the large number of Petsc sparse matrices
> being constructed. Each Aq matrix is known to have non-zero entries
> corresponding to only a small sub-domain of the geometry. All these
> sub-domains are separate blocks in the mesh. I get the feeling that for
> each sparse matrix memory is being allocated even for the zero entries. I
> saw that Petsc has an option MAT_IGNORE_ZERO_ENTRIES. How does one pass
> this in libMesh? Please note that I am not using libMesh directly. My code
> is a MOOSE app, and I am using the RB functionality of the underlying
> libmesh code.
>
> Best regards,
> Nikhil
>
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>

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