dolfin team mailing list archive

[Garth Wells <668862@xxxxxxxxxxxxxxxxxx>]

On 22/11/10 23:07, Johan Hake wrote:
> On Monday November 22 2010 14:41:06 Garth Wells wrote:
>> On 22/11/10 22:28, Johan Hake wrote:
>>> On Saturday November 20 2010 10:02:36 Garth Wells wrote:
>>>> I've done some testing, and using unordered_set appears to not perform
>>>> as well as Set for standard problems.
>>>
>>> What are the differences in timing here?
>>
>> I saw around a factor of 2. unordered_map also seems to use a lot more
>> memory.
>
> Ok.
>
>>> The difference in timing using
>>> unordered_set for Real spaces where dramatically better.
>>>
>>>> Each dof (matrix row) has its own set. The sets are usually small, and
>>>> independent of the mesh size. The problem is that the size of set that
>>>> belongs to the global dof is equal to the number of columns in the
>>>> matrix, and when looping over this set (which becomes very large)
>>>> std::find is called a lot.
>>>
>>> Is this true for any form? Why is this so notable when computing the
>>> sparsity pattern when a subspace belongs to Real?
>>
>> No, only forms with global dofs (like Real).
>
> Does each row that is affected by the gobal dof become "full", due to the way
> the set is constructed, or is it just the row for the global dofs?
>
>>>> I don't see simple solution from the SparsityPattern side. What's
>>>> probably needed is an extension to UFC to distinguish between local and
>>>> global dofs.
>>>
>>> How would this help?
>>
>> If we know that a dof is global, we can just insert it in the sparsity
>> pattern for all cells. No need to perform a search (which is what get
>> expensive for the 'Real' row, which is full).
>
> That would be a nice feature!
>

https://blueprints.launchpad.net/ufc/+spec/global-dofs

Garth

> Johan
>
>> Garth
>>
>>> Johan
>

-- 
Sparsity pattern computation dead slow with 'real' spaces
https://bugs.launchpad.net/bugs/668862
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Status in DOLFIN: Confirmed

Bug description:
The computation of the sparsity pattern is dead slow when real spaces are used, i.e.,

    R = FunctionSpace(mesh, 'R', 0)

To see the problem, compare the neumann-poisson and poisson demos with fine meshes (256x256)