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[kent-and@xxxxxxxxx]

Sounds amazing!

I'd like to see that code although I can not promise you to
much response during my holiday, which is starting tomorrow.

Have you compared matrix vector product with vector products using uBlas
or PETSc ?

Kent


> Hello!
>
> In light of the long and interesting discussion we had a while ago about
> assembler performance I decided to try to squeeze more out of the uBlas
> backend. This was not very successful.
>
> However, I've been following the development of MTL4
> (http://www.osl.iu.edu/research/mtl/mtl4/) with a keen eye on the
> interesting insertion scheme they provide. I implemented a backend --
> without sparsity pattern computation -- for the dolfin assembler and here
> are some first benchmarks results:
>
> Incomp Navier Stokes on 50x50x50 unit cube
>
> MTL --------------------------------------------------------
> assembly time: 8.510000
> reassembly time: 6.750000
> vecor assembly time: 6.070000
>
> memory: 230 mb
>
> UBLAS ------------------------------------------------------
> assembly time: 23.030000
> reassembly time: 12.140000
> vector assembly time: 6.030000
>
> memory: 642 mb
>
> Poisson on 2000x2000 unit square
>
> MTL --------------------------------------------------------
> assembly time: 9.520000
> reassembly time: 6.650000
> assembly time: 4.730000
> vector linear solve: 0.000000
>
> memory: 452 mb
>
> UBLAS ------------------------------------------------------
> assembly time: 15.400000
> reassembly time: 7.520000
> vector assembly time: 5.020000
>
> memory: 1169 mb
>
> Conclusions? MTL is more than twice as fast and allocates less than half
> the memory (since there is no sparsity pattern computation) across a set
> of forms I've tested.
>
> The code is not perfectly done yet, but I'd still be happy to share it
> with whoever wants to mess around with it.
>
> Cheers!
>
> /Dag
>
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