[R] Possible causes of unexpected behavior
Eric Berger
er|cjberger @end|ng |rom gm@||@com
Fri Mar 4 11:13:23 CET 2022
If I understand correctly, steps i,ii can be ignored.
i.e. we just focus on step iii with A,B,C,D fixed.
You do the optimization of f(v) to calculate, say, v* = argmin f(v).
This optimization is single threaded.
(A)
In that case, I suggest you add some logging so that for each call to f(),
you output its input and output.
Then you can (re-) confirm your validation test - i.e. that the "manual"
calc of f(v*) gives a different result than what is found in the log file.
(B) If (A) doesn't lead you anywhere ....
Re-reading your original description of the process, it seems that the time
consuming part is creating A,B,C,D.
If the evaluation of f(v) is not overly time consuming, then run the
optimization under valgrind. It is possible that you are depending on some
uninitialized variables, or trashing memory somewhere.
On Fri, Mar 4, 2022 at 11:54 AM Arthur Fendrich <arthfen using gmail.com> wrote:
> Dear Eric,
>
> Yes, I can confirm that I have distributed calculations running in
> parallel.
>
> I am not sure if this is a precise answer to the thread-safe question
> since I'm not familiar with this definition, but what I do is:
> i) First, chunks of A, B, C and D are calculated from X in parallel by
> the worker nodes.
> ii) Second, all the chunks are combined on my master node, and the final
> A, B, C and D are saved to disk.
> iii) Then, still on the master node, I optimize f(v) using the final A,
> B, C and D.
>
> When I debug, I skip steps i) and ii) and check only iii) manually by
> loading A, B, C and D from the disk and evaluating f(v*). Does that seem
> correct?
>
> Best regards,
> Arthur
>
> Em sex., 4 de mar. de 2022 às 10:33, Eric Berger <ericjberger using gmail.com>
> escreveu:
>
>> Can you confirm you have a distributed calculation running in parallel?
>> Have you determined that it is thread safe? How?
>> Your check on the smaller examples may not have ruled out such
>> possibilities.
>>
>> On Fri, Mar 4, 2022 at 11:21 AM Arthur Fendrich <arthfen using gmail.com>
>> wrote:
>>
>>> Dear Eric,
>>>
>>> Thank you for the response. Yes, I can confirm that, please see below
>>> the behavior.
>>> For #1, results are identical. For #2, they are not identical but very
>>> close. For #3, they are completely different.
>>>
>>> Best regards,
>>> Arthur
>>>
>>> --
>>>
>>> For #1,
>>> - qsub execution:
>>> [1] "ll: 565.7251"
>>> [1] "norm gr @ minimum: 2.96967368608131e-08"
>>>
>>> - manual check:
>>> f(v*): 565.7251
>>> gradient norm at v*: 2.969674e-08
>>>
>>> #
>>> For #2,
>>>
>>> - qsub execution:
>>> [1] "ll: 14380.8308"
>>> [1] "norm gr @ minimum: 0.0140857561408041"
>>>
>>> - manual check:
>>> f(v*): 14380.84
>>> gradient norm at v*: 0.01404779
>>>
>>> #
>>> For #3,
>>>
>>> - qsub execution:
>>> [1] "ll: 14310.6812"
>>> [1] "norm gr @ minimum: 6232158.38877002"
>>>
>>> - manual check:
>>> f(v*): 97604.69
>>> gradient norm at v*: 6266696595
>>>
>>> Em sex., 4 de mar. de 2022 às 09:48, Eric Berger <ericjberger using gmail.com>
>>> escreveu:
>>>
>>>> Please confirm that when you do the manual load and check that f(v*)
>>>> matches the result from qsub() it succeeds for cases #1,#2 but only fails
>>>> for #3.
>>>>
>>>>
>>>> On Fri, Mar 4, 2022 at 10:06 AM Arthur Fendrich <arthfen using gmail.com>
>>>> wrote:
>>>>
>>>>> Dear all,
>>>>>
>>>>> I am currently having a weird problem with a large-scale optimization
>>>>> routine. It would be nice to know if any of you have already gone
>>>>> through
>>>>> something similar, and how you solved it.
>>>>>
>>>>> I apologize in advance for not providing an example, but I think the
>>>>> non-reproducibility of the error is maybe a key point of this problem.
>>>>>
>>>>> Simplest possible description of the problem: I have two functions:
>>>>> g(X)
>>>>> and f(v).
>>>>> g(X) does:
>>>>> i) inputs a large matrix X;
>>>>> ii) derives four other matrices from X (I'll call them A, B, C and D)
>>>>> then
>>>>> saves to disk for debugging purposes;
>>>>>
>>>>> Then, f(v) does:
>>>>> iii) loads A, B, C, D from disk
>>>>> iv) calculates the log-likelihood, which vary according to a vector of
>>>>> parameters, v.
>>>>>
>>>>> My goal application is quite big (X is a 40000x40000 matrix), so I
>>>>> created
>>>>> the following versions to test and run the codes/math/parallelization:
>>>>> #1) A simulated example with X being 100x100
>>>>> #2) A degraded version of the goal application, with X being 4000x4000
>>>>> #3) The goal application, with X being 40000x40000
>>>>>
>>>>> When I use qsub to submit the job, using the exact same code and
>>>>> processing
>>>>> cluster, #1 and #2 run flawlessly, so no problem. These results tell me
>>>>> that the codes/math/parallelization are fine.
>>>>>
>>>>> For application #3, it converges to a vector v*. However, when I
>>>>> manually
>>>>> load A, B, C and D from disk and calculate f(v*), then the value I get
>>>>> is
>>>>> completely different.
>>>>> For example:
>>>>> - qsub job says v* = c(0, 1, 2, 3) is a minimum with f(v*) = 1.
>>>>> - when I manually load A, B, C, D from disk and calculate f(v*) on the
>>>>> exact same machine with the same libraries and environment variables,
>>>>> I get
>>>>> f(v*) = 1000.
>>>>>
>>>>> This is a very confusing behavior. In theory the size of X should not
>>>>> affect my problem, but it seems that things get unstable as the
>>>>> dimension
>>>>> grows. The main issue for debugging is that g(X) for simulation #3
>>>>> takes
>>>>> two hours to run, and I am completely lost on how I could find the
>>>>> causes
>>>>> of the problem. Would you have any general advices?
>>>>>
>>>>> Thank you very much in advance for literally any suggestions you might
>>>>> have!
>>>>>
>>>>> Best regards,
>>>>> Arthur
>>>>>
>>>>> [[alternative HTML version deleted]]
>>>>>
>>>>> ______________________________________________
>>>>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>> PLEASE do read the posting guide
>>>>> http://www.R-project.org/posting-guide.html
>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>
>>>>
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