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Re: AMD vs Intel

From: Leonid Gibiansky <lgibiansky>
Date: Tue, 19 Nov 2019 23:03:52 -0500

Thanks to all who shared their experience.

Here is the brief summary of observations:
4 combinations of Intel Fortran or gfortran with Xeon or AMD processors
(of approximately the same base frequency) provided similar speed but
different results. Time comparison is not straightforward as the number
of iterations required for convergence varied between these 4 versions
(FOCEI, LAPLACIAN, and SAEM with ADVAN13 were used for all tests).
Results are numerically different, but not really different as parameter
estimates differ by no more than the respective confidence intervals of
parameter estimates: few percents for the well defined parameters, more
for parameters with large RSEs. Thus, any of these 4 combinations can be
used, but it is better not to mix them in one analysis. Also it seems to
be a good practice to specify not only OS and compiler with options, but
also processor or at least processor type to ensure exact
reproducibility of results.

Unlike earlier (10+ years ago) reports, Intel (old, v.11) compiler seems
to provide similar speed on both Intel and AMD new processors.

Thanks!
Leonid



On 11/19/2019 4:32 AM, Rikard Nordgren wrote:
> Hi Leonid,
>
> When upgrading from gfortran 4.4.7 to 5.1.1 we ran around 20 models with
> both compilers and turning off the -ffast-math. The runs where on the
> same hardware. The differences in the parameter estimates and OFV were
> in general small. One big difference we could see was that the success
> of the covariance step was seemingly random. It could succeed on one
> compiler version, but not the other and it could also start failing when
> the option was turned off. I have kept the runs, so let me know if you
> would be interested. I also started some experiments using machine
> dependent compiler flags, but as our cluster is heterogeneous I
> abandoned this testing.
>
> I think that getting identical results could be possible, but that it
> would be quite a challenge. There are many components that affect the
> results. The compiler, the compiler flags, the libc implementation, the
> hardware and sometimes the operating system. To see for example where
> the standard libraries comes into play you can do nm nonmem on the
> nonmem executable (in linux) to list all symbols compiled in. Some are
> function from external libraries, for example my exponential function is
> from libc: exp
> from text strings could introduce rounding errors since the text
> representation is decimal and the internal floating point number is binary.
>
> Best regards,
> Rikard Nordgren
>
> --
> Rikard Nordgren
> Systems developer
>
> Dept of Pharmaceutical Biosciences
> Faculty of Pharmacy
> Uppsala University
> Box 591
> 75124 Uppsala
>
> Phone: +46 18 4714308
> www.farmbio.uu.se/research/researchgroups/pharmacometrics/
>
>
>
>
> On 2019-11-18 23:54, Leonid Gibiansky wrote:
>> Hi Jeroin,
>>
>> Thanks for your input, very interesting. As far as the goal is
>> concerned, I am mostly interested to find options that would give
>> identical results on two platform rather than in speed. So far no
>> luck: 4 combinations of gfortran / Intel compilers on Xeon / AMD
>> processors give 4 sets of results that are close but not identical.
>>
>> Related question to the group: have anybody experimented with gfortran
>> options (rather than using default provided by Nonmem distribution)?
>> Any recommendations? Same goal: maximum reproducibility across
>> different OSs, parallelization options, and processor types.
>>
>> Thanks
>> Leonid
>>
>>
>>
>>
>> On 11/18/2019 5:28 PM, Jeroen Elassaiss-Schaap (PD-value B.V.) wrote:
>>> Hi Leonid,
>>>
>>> "A while" back we compared model development trajectories and results
>>> between two computational platforms, Itanium and Xeon, see
>>> https://www.page-meeting.org/?abstract=1188. The results roughly
>>> were: 1/3 equal, 1/3 rounding differences and 1/3 real different
>>> results. From discussions with the technical knowledgeable people I
>>> worked with at the time, I recall that there are three levels/sources
>>> for those differences:
>>>
>>> 1) computational (hardware) platform
>>>
>>> 2) compilers (+ optimization settings)
>>>
>>> 3) libraries (floating point handling does matter)
>>>
>>> Assuming you would like to compare the speed of the platforms wrt
>>> NONMEM, my advice would be to test a large series of different
>>> models, from simple ADVAN1 or 2 to complex ODE, ranging from FO to
>>> LAPLACIAN INT NUMERICAL, while keeping compilers and libraries the
>>> same. Also small and large datasets, as in some instances you might
>>> be testing only the L1/L2/L3 cache strategies and Turbo settings. And
>>> with and without parallelization - as that might determine runtime
>>> bottlenecks in practice.
>>>
>>> Just having a peek at Epyc - seems interesting (noticed results w
>>> gcc7.4 compilation). As long as you are able to hold the computation
>>> in cache, a big if for the 64-core, there might be an advantage.
>>>
>>> All in all I am not sure that it is worth the trouble. For any given
>>> PK-PD model there is a lot you can tune to gain speed, but the
>>> optimal settings might be very different for the next and overrule
>>> any platform differences.
>>>
>>> Hope this helps,
>>>
>>> Jeroen
>>>
>>> http://pd-value.com
>>> jeroen
>>>
>>> +31 6 23118438
>>> -- More value out of your data!
>>>
>>> On 18/11/19 6:34 pm, Leonid Gibiansky wrote:
>>>> Thanks Bob and Peter!
>>>>
>>>> The model is quite stable, but this is LAPLACIAN, so requires second
>>>> derivatives. At iteration 0, gradients  differ by about 50 to 100%
>>>> between Intel and AMD. This leads to differences in minimization
>>>> path, and slightly different results. Not that different to change
>>>> the recommended dose, but sufficiently different to notice (OF
>>>> difference of 6 points; 50% more model evaluations to get to
>>>> convergence).
>>>> Thanks
>>>> Leonid
>>>>
>>>>
>>>>
>>>> On 11/18/2019 12:15 PM, Bonate, Peter wrote:
>>>>> Leonid - when you say different.  What do you mean?  Fixed effect
>>>>> and random effects?  Different OFV?
>>>>>
>>>>> We did a poster at AAPS a decade or so ago comparing results across
>>>>> different platforms using the same data and model.  We got
>>>>> different results on the standard errors (which related to matrix
>>>>> inversion and how those are done using software-hardware
>>>>> configurations). And with overparameterized models we got different
>>>>> error messages - some platforms converged with no problem while
>>>>> some did not converge and gave R matrix singularity.
>>>>>
>>>>> Did your problems go beyond this?
>>>>>
>>>>> pete
>>>>>
>>>>>
>>>>>
>>>>> Peter Bonate, PhD
>>>>> Executive Director
>>>>> Pharmacokinetics, Modeling, and Simulation
>>>>> Astellas
>>>>> 1 Astellas Way, N3.158
>>>>> Northbrook, IL  60062
>>>>> Peter.bonate
>>>>> (224) 205-5855
>>>>>
>>>>>
>>>>>
>>>>> Details are irrelevant in terms of decision making -  Joe Biden.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> -----Original Message-----
>>>>> From: owner-nmusers
>>>>> On Behalf Of Leonid Gibiansky
>>>>> Sent: Monday, November 18, 2019 11:05 AM
>>>>> To: nmusers <nmusers
>>>>> Subject: [NMusers] AMD vs Intel
>>>>>
>>>>> Dear All,
>>>>>
>>>>> I am testing the new Epyc processors from AMD (comparing with Intel
>>>>> Xeon), and getting different results. Just wondering whether
>>>>> anybody faced the problem of differences between AMD and Intel
>>>>> processors and knows how to solve it. I am using Intel compiler but
>>>>> ready to switch to gfortran or anything else if this would help to
>>>>> get identical results.
>>>>> There were reports of Intel slowing the AMD execution in the past,
>>>>> but in my tests, speed is comparable but the results differ.
>>>>>
>>>>> Thanks
>>>>> Leonid
>>>>>
>>>>>
>>>>>
>>>>
>>
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Received on Tue Nov 19 2019 - 23:03:52 EST

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