dolfin team mailing list archive

["Matthew Knepley" <knepley@xxxxxxxxx>]

On Mon, Jul 21, 2008 at 4:01 PM, Garth N. Wells <gnw20@xxxxxxxxx> wrote:
>
>
> Matthew Knepley 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.
>>
>> Now I don't believe the numbers. If you preallocate, we do not do any
>> extra processing
>> outside of sorting the column indices (which every format must do for
>> efficient operations).
>> Thus, how would you save any time? If these are all in seconds, I will
>> run a 2D Poisson here
>> and tell you what I get. It would help to specify sizes with this
>> benchmark :)
>>
>
> Take a look at bench/fem/assembly/ for the details.

That has to be extremely wrong. I get about 1/100th of the assembly
time. Someone has
screwed up the loop.

  Matt

> Garth
>
>>  Matt
>>
>>> Garth
>>>
>>>
>>>
>>>>> 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));
>>>> }
>>>>
>>>>
>>>>
>>>> ------------------------------------------------------------------------
>>>>
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>>>
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>>
>>
>>
>



-- 
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