[petsc-users] Varying TAO optimization solve iterations using BLMVM

[Justin Chang]

I was unable to do quad precision or even with 64 bit integers because my
data files rely on intricate binary files that have been written in 32 bit.

However, I noticed a couple things which are puzzling to me:

1) I am solving a transient problem using my own backward euler function.
Basically I call TaoSolve at each time level. What I find strange is that
the number of TAO solve iterations vary at each time level for a given
number of processors. The solution is roughly the same when I change the
number of processors. Any idea why this is happening, or might this have
more to do with the job scheduling/compute nodes on my HPC machine?

2) Sometimes, I get Tao Termination reason of -5, and from what I see from
the online documentation, it means the number of function evaluations
exceeds the maximum number of function evaluations. I only get this at
certain time levels, and it also varies when I change the number of
processors.

I can understand the number of iterations going down the further in time i
go (this is due to the nature of my problem), but I am not sure why the
above two observations are happening. Any thoughts?

Thanks,
Justin

On Fri, Jun 19, 2015 at 11:52 AM, Justin Chang <jychang48 at gmail.com> wrote:

> My code sort of requires HDF5 so installing quad precision might be a
> little difficult. I could try to work around this but that might take some
> effort.
>
> In the mean time, is there any other potential explanation or alternative
> to figuring this out?
>
> Thanks,
> Justin
>
>
> On Thursday, June 18, 2015, Matthew Knepley <knepley at gmail.com> wrote:
>
>> On Thu, Jun 18, 2015 at 1:52 PM, Jason Sarich <jason.sarich at gmail.com>
>> wrote:
>>
>>> BLMVM doesn't use a KSP or preconditioner, it updates using the L-BFGS-B
>>> formula
>>>
>>
>> Then this sounds like a bug, unless one of the constants is partition
>> dependent.
>>
>>   Matt
>>
>>
>>> On Thu, Jun 18, 2015 at 1:45 PM, Matthew Knepley <knepley at gmail.com>
>>> wrote:
>>>
>>>>   On Thu, Jun 18, 2015 at 12:15 PM, Jason Sarich <
>>>> jason.sarich at gmail.com> wrote:
>>>>
>>>>> Hi Justin,
>>>>>
>>>>>  I can't tell for sure why this is happening, have you tried using
>>>>> quad precision to make sure that numerical cutoffs isn't the problem?
>>>>>
>>>>>  1 The Hessian being approximate and the resulting implicit
>>>>> computation is the source of the cutoff, but would not be causing different
>>>>> convergence rates in infinite precision.
>>>>>
>>>>>  2 the local size may affect load balancing but not the resulting
>>>>> norms/convergence rate.
>>>>>
>>>>
>>>>  This sounds to be like the preconditioner is dependent on the
>>>> partition. Can you send -tao_view -snes_view
>>>>
>>>>    Matt
>>>>
>>>>
>>>>>  Jason
>>>>>
>>>>>
>>>>> On Thu, Jun 18, 2015 at 10:44 AM, Justin Chang <jychang48 at gmail.com>
>>>>> wrote:
>>>>>
>>>>>>  I solved a transient diffusion across multiple cores using TAO
>>>>>> BLMVM. When I simulate the same problem but on different numbers of
>>>>>> processing cores, the number of solve iterations change quite drastically.
>>>>>> The numerical solution is the same, but these changes are quite vast. I
>>>>>> attached a PDF showing a comparison between KSP and TAO. KSP remains
>>>>>> largely invariant with number of processors but TAO (with bounded
>>>>>> constraints) fluctuates.
>>>>>>
>>>>>> My question is, why is this happening? I understand that accumulation
>>>>>> of numerical round-offs may attribute to this, but the differences seem
>>>>>> quite vast to me. My initial thought was that
>>>>>>
>>>>>>  1) the Hessian is only projected and not explicitly computed, which
>>>>>> may have something to do with the rate of convergence
>>>>>>
>>>>>> 2) local problem size. Certain regions of my domain have different
>>>>>> number of "violations" which need to be corrected by the bounded
>>>>>> constraints so the rate of convergence depends on how these regions are
>>>>>> partitioned?
>>>>>>
>>>>>> Any thoughts?
>>>>>>
>>>>>> Thanks,
>>>>>> Justin
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>  --
>>>> 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
>>>>
>>>
>>>
>>
>>
>> --
>> 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
>>
>
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