Hi,

On Fri, Feb 9, 2018, 18:05 Alex <nedoma...@gmail.com> wrote:

> Just to quickly jump in, because Mark suggested taken a look at the
> latest doc and unfortunately I must admit that I didn't understand what
> I read. I appear to be especially struggling with the idea of gputasks.
>

Szilard's link specifically targets that issue. Is there something unclear
there? Naturally, a GPU task is work that runs on a GPU.

Can you please explain what is happening in this line?
>
> > -pme gpu -nb gpu -ntmpi 8 -ntomp 6 -npme 1 -gputasks 00000001
>
> I am seriously confused here. Also, the number of ranks is 8, while the
> number of threads is 6? Is -ntomp now specifying the _per-rank_ number of
> threads, i.e. the actual number of threads for this job would be 48?
>

Yes. The -ntomp, -nt, and -ntmpi options have always been different from
each other, and still work as they always did. See
http://manual.gromacs.org/documentation/2018-latest/user-guide/mdrun-performance.html.
There are 8 ranks. You specified one to do PME work, and for all PP work
and all PME work to go on GPUs, and that 7 of the GPU tasks go on GPU 0 and
one on GPU 1. Please check out the docs for each option rather than guess
:-)

Mark

Thank you,
>
> Alex
>
>
> On 2/9/2018 8:25 AM, Szilárd Páll wrote:
> > Hi,
> >
> > First of all,have you read the docs (admittedly somewhat brief):
> >
> http://manual.gromacs.org/documentation/2018/user-guide/mdrun-performance.html#types-of-gpu-tasks
> >
> > The current PME GPU was optimized for single-GPU runs. Using multiple
> GPUs
> > with PME offloaded works, but this mode hasn't been an optimization
> target
> > and it will often not give very good performance. Using multiple GPUs
> > requires a separate PME rank (as you have realized), only one can be used
> > (as we don't support PME decomposition on GPUs) and it comes some
> inherent
> > scaling drawbacks. For this reason, unless you _need_ your single run to
> be
> > as fast as possible, you'll be better off running multiple simulations
> > side-by side.
> >
> > A few tips for tuning the performance of a multi-GPU run with PME
> offload:
> > * expect to get at best 1.5 scaling to 2 GPUs (rarely 3 if the tasks
> allow)
> > * generally it's best to use about the same decomposition that you'd use
> > with nonbonded-only offload, e.g. in your case 6-8 ranks
> > * map the GPU task alone or at most together with 1 PP rank to a GPU,
> i.e.
> > use the new -gputasks option
> > e.g. for your case I'd expect the following to work ~best:
> > gmx mdrun -v -deffnm md -pme gpu -nb gpu -ntmpi 8 -ntomp 6 -npme 1
> > -gputasks 00000001
> > or
> > gmx mdrun -v -deffnm md -pme gpu -nb gpu -ntmpi 8 -ntomp 6 -npme 1
> > -gputasks 00000011
> >
> >
> > Let me know if that gave some improvement.
> >
> > Cheers,
> >
> > --
> > Szilárd
> >
> > On Fri, Feb 9, 2018 at 8:51 AM, Gmx QA <gmxquesti...@gmail.com> wrote:
> >
> >> Hi list,
> >>
> >> I am trying out the new gromacs 2018 (really nice so far), but have a
> few
> >> questions about what command line options I should specify, specifically
> >> with the new gnu pme implementation.
> >>
> >> My computer has two CPUs (with 12 cores each, 24 with hyper threading)
> and
> >> two GPUs, and I currently (with 2018) start simulations like this:
> >>
> >> $ gmx mdrun -v -deffnm md -pme gpu -nb gpu -ntmpi 2 -npme 1 -ntomp 24
> >> -gpu_id 01
> >>
> >> this works, but gromacs prints the message that 24 omp threads per mpi
> rank
> >> is likely inefficient. However, trying to reduce the number of omp
> threads
> >> I see a reduction in performance. Is this message no longer relevant
> with
> >> gpu pme or am I overlooking something?
> >>
> >> Thanks
> >> /PK
> >> --
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>
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