[gmx-users] GPU-accelerated EM

[Mark Abraham]

Hi,

The em log files report using the GPUs, and they do. It just isn't very
useful because the EM implementations do a lot more neighbour search
stages, which doesn't use the GPUs.

So, if there's other work for the GPUs to do, then I would plan to run a
workflow heavily dominated by EM, on a non-GPU resource.

We're not aware of a use case that would justify thinking about how to make
a correct implementation of EM that could do less searching, because in
most cases the time spent in EM is a tiny fraction of the time spent in MD.
Is the workflow useful to optimize in this way?

Mark

On Fri, Sep 8, 2017 at 11:53 PM Alex <nedomacho at gmail.com> wrote:

> Hi all,
>
> I tend to run one fat node-wise simulation, but we have another user whose
> use case is different. He runs a bunch of small jobs at the same time and
> in his case most of the time is taken up by the EM. We have two
> hyperthreaded Xeons E5 (44 cores total) and 3 GPUs.
>
> So, we try to run 10 jobs using 4 cores each. Each mdrun line includes
>
> -ntomp 4 -pin on -pinoffset x -pinstride 1 -gpu_id y
>
> where pinoffset is 0, 4, 8, etc for the first, second, etc, simulation and
> y changes from 0 to 2 so that the first two GPUs are subscribed three
> times, while the third one is subscribed four times.
>
> Upon submitting this batch, each mdrun instance utilizes something like 10%
> of CPU and the EM jobs proceed slowly. Removing GPU and setting -nb cpu
> seems to fix that. With MD, everything works properly, but with EM there's
> no GPU acceleration and low CPU usage. Is this correct behavior?
>
> Thanks,
>
> Alex
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