Ross,
Thanks for the reply- I had figured that the non equivalent split across
the cards was to do with scaling, but I just wanted to double check to be
sure.
I'll try to check again to make sure there isn't something interfering with
it- the performance using both cards is better than a K20, but the increase
(+5ns/day) just seemed a bit smaller than I'd have expected when running
across two cards. But that could just as well be that the system is small
and isn't optimized.
Best,
Kenneth
On Tue, May 12, 2015 at 11:42 PM, Ross Walker <ross.rosswalker.co.uk> wrote:
> Hi Kenneth,
>
> The non-100% utilization is normal - the scaling is not perfect so there
> is no reason to expect both cards to be running at 100% as they would be if
> you ran two separate MD runs, one on each GPU.
>
> In terms of performance take a look at http://ambermd.org/gpus/ where,
> under benchmarks, you should find single and dual GTX-Titan-Z numbers. You
> should be able to get within a few % of these numbers - certainly much
> better than a K20. If you aren't then something is setup wrong with your
> simulation or someone else is also running on the machine at the same time.
> (I'd also suggest stopping X11 'init 3' if it is running).
>
> All the best
> Ross
>
> > On May 12, 2015, at 9:22 AM, Kenneth Huang <kennethneltharion.gmail.com>
> wrote:
> >
> > Dear all,
> >
> > I was curious as to what the performance on 2 in 1 GPUs, specifically the
> > GTX Titan Z in a Ubuntu 14.04 machine with cuda 6.5. When I'm trying to
> > benchmark the card, I'm running into a strange situation where it does
> > appear to be running on both cards, it's not fully using both cards?
> >
> > For example, running this command-
> >
> > mpirun -np 2 /home/curvelinux/bin/amber14/bin/pmemd.cuda.MPI -O -i
> > 05_prod1a.in -o test_prod3.out -p test_sol.prmtop -c test_prod2.rst -r
> > test_prod3.rst -x test_prod3.mdcrd
> >
> > And then checking with nvidia-smi gives me-
> >
> > +------------------------------------------------------+
> >
> > | NVIDIA-SMI 346.46 Driver Version: 346.46
> > |
> >
> |-------------------------------+----------------------+----------------------+
> > | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr.
> > ECC |
> > | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util
> Compute
> > M. |
> >
> |===============================+======================+======================|
> > | 0 GeForce GTX TIT... Off | 0000:04:00.0 On |
> > N/A |
> > | 57% 82C P2 125W / 189W | 857MiB / 6143MiB | 53%
> > Default |
> >
> +-------------------------------+----------------------+----------------------+
> > | 1 GeForce GTX TIT... Off | 0000:05:00.0 Off |
> > N/A |
> > | 90% 89C P2 160W / 189W | 589MiB / 6143MiB | 98%
> > Default |
> >
> +-------------------------------+----------------------+----------------------+
> >
> > Where it seems that by all appearances, both cards are being used, but
> the
> > load per card seems to be disparate, so I was just wondering if this is
> > expected behaviour? There doesn't seem to be much of a speed up compared
> to
> > running on a K20 (23ns/day vs 27ns/day), although this is a small system
> > (50,000 atoms) and I haven't optimized for GPU runs.
> >
> > Mostly, I was just wondering if it would be better to split the cards up
> to
> > run jobs in parallel, instead of trying jobs on both of them. Searching
> > google gives the suggestion that enabling double precision mode in
> nvidia,
> > but I was wondering if I was missing something in my setup for pmemd.
> >
> > Best,
> >
> > Kenneth
> >
> > --
> > Ask yourselves, all of you, what power would hell have if those
> imprisoned
> > here could not dream of heaven?
> > _______________________________________________
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> > http://lists.ambermd.org/mailman/listinfo/amber
>
>
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Received on Wed May 13 2015 - 18:30:02 PDT
