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
--
Paul Bauer, PhD
GROMACS Development Manager
KTH Stockholm, SciLifeLab
0046737308594
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