Thanks Jason.
On Tue, Jan 28, 2014 at 2:56 PM, Jason Swails <jason.swails.gmail.com>wrote:
> On Tue, 2014-01-28 at 14:21 -0500, Guanglei Cui wrote:
> > Hi Jan-Philip and Ross,
> >
> > Thanks for the response. I suspect the performance drop I saw is most
> > likely due to running 2 jobs on the same card because I didn't specify
> > CUDA_VISIBLE_DEVICES in the first place. And, the same CUDA device ID was
> > reported in the output files.
> >
> > However, setting CUDA_VISIBLE_DEVICES in the job script doesn't seem to
> > affect the device ID reported in mdout. With the variable set to 0 and 1
> > individually, CUDA DEVICE ID 0 is reported in both mdouts, even though
> > nvidia-smi seems to suggest both GPUs are being used.
>
> Unfortunately this is expected behavior. The device number is numbered
> from 0 to N of the N GPUs specified by CUDA_VISIBLE_DEVICES. It may
> make sense for Amber to start printing out the value of the
> CUDA_VISIBLE_DEVICES environment variable when it prints out device
> number, too.
>
> This is part of the CUDA API itself, so Amber has no control.
>
> One thing to be careful of is that the device numbering in nvidia-smi
> does _not_ necessarily match the device numbering that one gets from the
> CUDA API. If you want to get the mapping that the CUDA API returns, you
> need to use the "deviceQuery" program included with the CUDA SDK
> instead.
>
> HTH,
> Jason
>
> --
> Jason M. Swails
> BioMaPS,
> Rutgers University
> Postdoctoral Researcher
>
>
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--
Guanglei Cui
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Received on Tue Jan 28 2014 - 12:30:03 PST
