Hi Chris,
The P2P algorithm used in AMBER 16 only supports power of 2 GPUs. As such you will always see poor performance on 3 GPUs. For such a machine, indeed for almost all machines, your best option is to run 3 independent calculations, one per GPU, you'll get much better overall sampling that way since the multi-GPU scaling is never great. You could also run a 1 x 2GPU job and a 1 x 1 GPU job. On a DGX I wouldn't recommend going above 2 GPUs per run. Sure it will scale to 4 but the improvement is not great and you mostly end up just wasting resources for a few extra %. On a DGX system (or any 8 GPU system for that matter) your best option with AMBER 16 is probably to run either 8 x 1 GPU or 4 x 2 GPU or a combination of those. Unless you are running a large GB calculation in which case you can get almost linear scaling out to 8 GPUs - even over regular PCI-E (no need for gold plated DGX nodes).
All the best
Ross
> On Apr 3, 2017, at 19:43, Chris Neale <candrewn.gmail.com> wrote:
>
> Dear AMBER users:
>
> I have a system with ~ 200,000 atoms that scales quite well on 4 GPUs on a
> DGX machine with Amber16. I now have access to a different node for testing
> purposes that has 3 Tesla P100 GPUs. I find that 1 GPU gives 21 ns/day, 2
> GPUs give 31 ns/day and 3 GPUs give 21 ns/day. Strange thing is that 2 GPUs
> gives a consistent speed when I use GPUs 0,1 or 1,2 or 0,2 -- leading me to
> think that there is PCI-based peer-to-peer across all 3 GPUs (though I
> don't know how to verify that). So then why does performance drop off with
> 3 GPUs? I don't currently have the ability to re-test with 3 GPUs on a DGX,
> though I will look into testing that, since it could give a definitve
> answer.
>
> I'm wondering whether there is something obviously inherent to the code
> that doesn't like 3 GPUs (vs. 2 or 4)? Any thoughts?
>
> Thank you for your help,
> Chris.
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Received on Tue Apr 04 2017 - 08:30:02 PDT