Hi,
On Tue, 25 Jul 2017 21:03 Ross Walker <ross.rosswalker.co.uk> wrote:
>
> >
> > Note since AMBER sits entirely on a GPU so you can run multiple jobs on a
> >> node without contention (1 per GPU). This is not true with Gromacs due
> to
> >> all the CPU to CPU and CPU to GPU communication that floods the
> >> communication channels between CPU cores and the PCI-E bus to the GPU.
> As
> >> such you can't reliably run say 2 Gromacs jobs on the same node where
> one
> >> uses 20 cores and 2 GPUs and another uses the remaining 20 cores and
> >> remaining 2 GPUs.
> >>
> >
> > Uh, no. These run fine (ie *reliable*) and when set up properly will
> > naturally run out of phase with eachother and maximise throughput. See
> e.g.
> >
> http://onlinelibrary.wiley.com/doi/10.1002/jcc.24030/abstract;jsessionid=3CD2B4EE326378381D60FCB0BD1B26A0.f02t02
> > (or same on arxiv.
> >
> > Mark
>
> Only if you go to the trouble of placing threads properly and locking
> things to the right cores and corresponding GPUs.
Most people interested in this are running multiple copies of the same kind
of simulation, and this is managed automatically with gmx_mpi mdrun -multi.
As that paper says ;-)
This, and the complexity in choosing hardware for Gromacs as illustrated by
> the plethora of options and settings highlighted in that paper, is
> something that is generally way beyond the average user and a pain in the
> butt to configure properly with most queuing systems. So while it works in
> theory my experience is that this is very difficult to achieve reliably in
> practice.
>
Indeed, not very easy to use. But thoroughly reliable.
So we can learn, how does one target different AMBER simulations to
different GPUs?
Mark
All the best
> Ross
>
>
>
>
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Received on Tue Jul 25 2017 - 14:30:02 PDT
