[gmx-users] Performance issues with Gromacs 2020 on GPUs - slower than 2019.5

[Andreas Baer]

Hi,

with the link below, additional log files for runs with 1 GPU should be 
accessible now.

Thank you for the comment with the rlist, I did not know, that this will 
affect the performance negatively. I know, about the nstcalcenergy, but 
I need it for several of my simulations.

Cheers,
Andreas

On 26.02.20 16:50, Szilárd Páll wrote:
> Hi,
>
> Can you please check the performance when running on a single GPU 2019 vs
> 2020 with your inputs?
>
> Also note that you are using some peculiar settings that will have an
> adverse effect on performance (like manually set rlist disallowing the dual
> pair-list setup, and nstcalcenergy=1).
>
> Cheers,
>
> --
> Szilárd
>
>
> On Wed, Feb 26, 2020 at 4:11 PM Andreas Baer <andreas.baer at fau.de> wrote:
>
>> Hello,
>>
>> here is a link to the logfiles.
>>
>> https://faubox.rrze.uni-erlangen.de/getlink/fiX8wP1LwSBkHRoykw6ksjqY/benchmarks_2019-2020
>>
>> If necessary, I can also provide some more log or tpr/gro/... files.
>>
>> Cheers,
>> Andreas
>>
>>
>> On 26.02.20 16:09, Paul bauer wrote:
>>> Hello,
>>>
>>> you can't add attachments to the list, please upload the files
>>> somewhere to share them.
>>> This might be quite important to us, because the performance
>>> regression is not expected by us.
>>>
>>> Cheers
>>>
>>> Paul
>>>
>>> On 26/02/2020 15:54, Andreas Baer wrote:
>>>> Hello,
>>>>
>>>> from a set of benchmark tests with large systems using Gromacs
>>>> versions 2019.5 and 2020, I obtained some unexpected results:
>>>> With the same set of parameters and the 2020 version, I obtain a
>>>> performance that is about 2/3 of the 2019.5 version. Interestingly,
>>>> according to nvidia-smi, the GPU usage is about 20% higher for the
>>>> 2020 version.
>>>> Also from the log files it seems, that the 2020 version does the
>>>> computations more efficiently, but spends so much more time waiting,
>>>> that the overall performance drops.
>>>>
>>>> Some background info on the benchmarks:
>>>> - System contains about 2.1 million atoms.
>>>> - Hardware: 2x Intel Xeon Gold 6134 („Skylake“) @3.2 GHz = 16 cores +
>>>> SMT; 4x NVIDIA Tesla V100
>>>>    (similar results with less significant performance drop (~15%) on a
>>>> different machine: 2 or 4 nodes with each [2x Intel Xeon 2660v2 („Ivy
>>>> Bridge“) @ 2.2GHz = 20 cores + SMT; 2x NVIDIA Kepler K20])
>>>> - Several options for -ntmpi, -ntomp, -bonded, -pme are used to find
>>>> the optimal set. However the performance drop seems to be persistent
>>>> for all such options.
>>>>
>>>> Two representative log files are attached.
>>>> Does anyone have an idea, where this drop comes from, and how to
>>>> choose the parameters for the 2020 version to circumvent this?
>>>>
>>>> Regards,
>>>> Andreas
>>>>
>> --
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