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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 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. ------------------------------------------------------------------------ _______________________________________________ DOLFIN-dev mailing list DOLFIN-dev@xxxxxxxxxx http://www.fenics.org/mailman/listinfo/dolfin-dev
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