thanks jed. I can't seem to find a stored profile. I'd have to recreate 
one. But i'm thinking roughly twice as many functions evaluations as 
jacobian evaluations.





On Thu, 24 Jun 2010, Jed Brown wrote:

> On Thu, 24 Jun 2010 14:28:03 -0500, David Fuentes <[email protected]> wrote:
>> On 6/24/10, Jed Brown <[email protected]> wrote:
>>> On Thu, 24 Jun 2010 13:59:21 -0500 (CDT), David Fuentes
>>> <[email protected]> wrote:
>>>>
>>>> I typically use Petsc Nonlinear Solvers in 3D and my bottle neck is
>>>> typically in the assembly with Petsc SNESSolve taking ~10% of the time
>>>> about ~50% in the jacobian, ~30% in the residual, and the
>>>> rest is distributed.
>>>
>>> So the linear solves are really easy.  Are you caching a lot of stuff in
>>> the residual evaluation, it's not normal for it to be so much compared
>>> to Jacobian assembly unless you don't use an analytic Jacobian
>>> (e.g. -snes_mf_operator).
>>
>> Not sure, what do you typically cache ?
>
> I cache a local linearization at quadrature points, but that is for fast
> matrix-free Jacobian application of high-order operators.  It doesn't
> pay off in terms of time or storage for Q1 or P1, even in 3D.  But if
> you had e.g. an expensive constitutive relation involving lookup tables,
> and you weren't overly concerned about using the minimum possible
> memory, but didn't want to do the extra work to integrate and insert the
> element matrices (because there was a high chance of the line search
> shortening the step), then you might cache the local linearization even
> for lowest-order elements.  It sounds like this is not the case.
>
> What does -log_summary show?  Are you doing a lot more function
> evaluations than Jacobian assemblies?  It's surprising to me that they
> would cost almost the same amount per call, perhaps there is a hot spot
> somewhere in your residual evaluation.
>
> Jed
>

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