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Message #08795
Re: Assembly benchmark
On Mon, Jul 21, 2008 at 01:48:23PM +0100, Garth N. Wells wrote:
>
>
> Anders Logg wrote:
> > I have updated the assembly benchmark to include also MTL4, see
> >
> > bench/fem/assembly/
> >
> > Here are the current results:
> >
> > Assembly benchmark | Elasticity3D PoissonP1 PoissonP2 PoissonP3 THStokes2D NSEMomentum3D StabStokes2D
> > -------------------------------------------------------------------------------------------------------------
> > uBLAS | 9.0789 0.45645 3.8042 8.0736 14.937 9.2507 3.8455
> > PETSc | 7.7758 0.42798 3.5483 7.3898 13.945 8.1632 3.258
> > Epetra | 8.9516 0.45448 3.7976 8.0679 15.404 9.2341 3.8332
> > MTL4 | 8.9729 0.45554 3.7966 8.0759 14.94 9.2568 3.8658
> > Assembly | 7.474 0.43673 3.7341 8.3793 14.633 7.6695 3.3878
> >
>
> How was the MTL4 matrix intialised? I don't know if it does anything
> with the sparsity pattern yet. I've been intialising MTL4 matrices by
> hand so far with a guess as to the max number of nonzeroes per row.
> Without setting this, the performance is near idenetical to uBLAS. When
> it is set, I observe at least a factor two speed up.
>
> Garth
The same way as all other backends, which is by a precomputed
sparsity pattern. It looks like this is currently ignored in the
MTL4Matrix implementation:
void MTL4Matrix::init(const GenericSparsityPattern& sparsity_pattern)
{
init(sparsity_pattern.size(0), sparsity_pattern.size(1));
}
--
Anders
> > The differences are very small, but this may be caused by
> >
> > 1. Overhead from the Python wrappers.
> >
> > 2. The computation of the sparsity pattern dominates.
> >
> > I have plans to extract more fine-grained results (using the new
> > Timing class) so that we may report the time for computing the
> > sparsity pattern, initialization, and assembly separately.
> >
> >
> >
> > ------------------------------------------------------------------------
> >
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>
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