dolfin team mailing list archive

["Matthew Knepley" <knepley@xxxxxxxxx>]

On Tue, Jul 22, 2008 at 3:30 AM, Garth N. Wells <gnw20@xxxxxxxxx> wrote:
>
>
> Matthew Knepley wrote:
>>
>> On Mon, Jul 21, 2008 at 4:48 PM, Anders Logg <logg@xxxxxxxxx> wrote:
>>>
>>> On Mon, Jul 21, 2008 at 04:37:28PM -0500, Matthew Knepley wrote:
>>>>
>>>> On Mon, Jul 21, 2008 at 4:35 PM, Anders Logg <logg@xxxxxxxxx> wrote:
>>>>>
>>>>> On Mon, Jul 21, 2008 at 04:03:11PM -0500, Matthew Knepley wrote:
>>>>>>
>>>>>> On Mon, Jul 21, 2008 at 3:55 PM, Matthew Knepley <knepley@xxxxxxxxx>
>>>>>> wrote:
>>>>>>>
>>>>>>> On Mon, Jul 21, 2008 at 3:50 PM, Garth N. Wells <gnw20@xxxxxxxxx>
>>>>>>> wrote:
>>>>>>>>
>>>>>>>> Anders Logg wrote:
>>>>>>>>>
>>>>>>>>> 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
>>>>>>>>>>>
>>>>>>>>
>>>>>>>> I specified in MTL4Matrix maximum 30 nonzeroes per row, and the
>>>>>>>> results
>>>>>>>> change quite a bit,
>>>>>>>>
>>>>>>>>  Assembly benchmark  |  Elasticity3D  PoissonP1  PoissonP2
>>>>>>>>  PoissonP3
>>>>>>>> THStokes2D  NSEMomentum3D  StabStokes2D
>>>>>>>>
>>>>>>>>
>>>>>>>> -------------------------------------------------------------------------------------------------------------
>>>>>>>>  uBLAS               |        7.1881    0.32748     2.7633
>>>>>>>> 5.8311
>>>>>>>>    10.968         7.0735        2.8184
>>>>>>>>  PETSc               |        5.7868    0.30673     2.5489
>>>>>>>> 5.2344
>>>>>>>>    9.8896          6.069        2.3661
>>>>>>>>  MTL4                |        2.8641    0.18339     1.6628
>>>>>>>> 2.6811
>>>>>>>>    2.8519         3.4843       0.85029
>>>>>>>>  Assembly            |        5.5564    0.30896     2.6858
>>>>>>>> 5.9675
>>>>>>>>    10.622         5.7144        2.4519
>>>>>>>>
>>>>>>>>
>>>>>>>> MTL4 is a lot faster in all cases.
>>>>>>
>>>>>> Okay, if you run KSP ex2 (Poisson 2D) and add a logging stage that
>>>>>> times assembly (I checked it in to petsc-dev)
>>>>>> then 1M unknowns takes about 1s
>>>>>>
>>>>>>  Matrix Object:
>>>>>>    type=seqaij, rows=1000000, cols=1000000
>>>>>>    total: nonzeros=4996000, allocated nonzeros=5000000
>>>>>>      not using I-node routines
>>>>>> Summary of Stages:   ----- Time ------  ----- Flops -----  ---
>>>>>> Messages ---  -- Message Lengths --  -- Reductions --
>>>>>>                        Avg     %Total     Avg     %Total   counts
>>>>>> %Total     Avg         %Total   counts   %Total
>>>>>>  0:      Main Stage: 1.4997e+00  56.3%  3.8891e+08 100.0%  0.000e+00
>>>>>> 0.0%  0.000e+00        0.0%  2.200e+01  51.2%
>>>>>>  1:        Assembly: 1.1648e+00  43.7%  0.0000e+00   0.0%  0.000e+00
>>>>>> 0.0%  0.000e+00        0.0%  0.000e+00   0.0%
>>>>>>
>>>>>> I just cut the solve off. Thus all thos enumber are extemely fishy.
>>>>>>
>>>>>>  Matt
>>>>>
>>>>> We shouldn't trust those numbers just yet. Some of it may be Python
>>>>> overhead (calling the FFC JIT compiler etc).
>>>>>
>>>>> Does 1M unknowns mean a unit square divided into 2x1000x1000 right
>>>>> triangles?
>>>>
>>>> Its FD Poisson, which gives the same sparsity and values as P1 Poisson,
>>>> so
>>>> its a 1000x1000 quadrilateral grid. This was just to time insertion.
>>>>
>>>>  Matt
>>>
>>> But this is a different problem. Since you know the sparsity pattern a
>>> priori, you may be able to (i) not compute the sparsity pattern, (ii)
>>
>> No, we only allocate correctly here.
>>
>
> Matt,
>
> Is there much of a performance difference with MatSeqAIJSetPreallocation
> between setting the maximum number of non-zeroes per row (PetscInt nz), and
> setting the number of non-zeroes for each row (PetscInt nnz[]) when the
> number of non-zeroes per row doesn't differ greatly?

There should be no difference at all.

   Matt

> Garth
>
>
>>> compute the entries more efficiently, (iii) not compute the
>>> local-to-global mapping, and (iv) insert the entries more efficiently.
>>
>> Insertion is the same and we compute the same mapping we always use.
>> I think you guys overcompute for the l2g.
>>
>>  Matt
>>
>>> Our timings include all these steps + Python overhead. I'm going to
>>> rewrite it in C++ so we can eliminate that source of uncertainty.
>>>
>>> --
>>> Anders
>>>
>>> -----BEGIN PGP SIGNATURE-----
>>> Version: GnuPG v1.4.6 (GNU/Linux)
>>>
>>> iD8DBQFIhQQgTuwUCDsYZdERAnUzAJ93hfI/Psx6IccOdOr3GhbODAdFgACdFAj9
>>> Mc0MiBbB+aiTEMXOajyrnog=
>>> =oLL0
>>> -----END PGP SIGNATURE-----
>>>
>>> _______________________________________________
>>> DOLFIN-dev mailing list
>>> DOLFIN-dev@xxxxxxxxxx
>>> http://www.fenics.org/mailman/listinfo/dolfin-dev
>>>
>>>
>>
>>
>>
>
>



-- 
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which
their experiments lead.
-- Norbert Wiener