Thanks for the response.....this is the nvidia-smi output after submitting
aMD on single GPU card:-
+------------------------------------------------------+
| NVIDIA-SMI 4.304.84 Driver Version: 304.84 |
|-------------------------------+----------------------+----------------------+
| GPU Name | Bus-Id Disp. | Volatile Uncorr.
ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute
M. |
|===============================+======================+======================|
| 0 Tesla M2090 | 0000:14:00.0 Off |
0 |
| N/A N/A P0 180W / 225W | 19% 1016MB / 5375MB | 99%
Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla M2090 | 0000:15:00.0 Off |
0 |
| N/A N/A P12 29W / 225W | 0% 10MB / 5375MB | 0%
Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Compute processes: GPU
Memory |
| GPU PID Process name Usage
|
|=============================================================================|
| 0 22849 pmemd.cuda
1003MB |
+-----------------------------------------------------------------------------+
i don't think this is running on two cards, one card is still free.. and i
am not submitting jobs together...
Am i missing some basic thing here?
Thanks
On Sun, Jan 11, 2015 at 8:09 PM, Ryan Novosielski <novosirj.ca.rutgers.edu>
wrote:
> -----BEGIN PGP SIGNED MESSAGE-----
> Hash: SHA1
>
> Sure. I've run both multiple jobs on a single GPU, single GPU jobs on
> multiple cards, and multi-GPU MPI jobs. All work fine. I'd be curious
> to see nvidia-smi after the first job is running (you are submitting
> them separately, right?) to see if maybe something funny isn't
> happening like one job takes out two cards somehow.
>
> On 01/11/2015 07:45 AM, Asmita Gupta wrote:
> > Dear users,
> >
> >
> > I am able to successfully run an accelerated MD simulation on
> > single M2090 GPU cards, but when i am trying to submit the same
> > simulation on 2 GPU cards on a single node, i am getting this
> > message:-
> >
> > cudaGetDeviceCount failed no CUDA-capable device is detected
> > cudaGetDeviceCount failed no CUDA-capable device is detected
> >
> > .... Does AMBER supports accelerated MD simulation runs on multiple
> > GPU cards??
> >
> > I ran nvidia-smi and everything seemed to be normal:-
> >
> >
> >
> >
> |-------------------------------+----------------------+----------------------+
> >
> >
> | GPU Name | Bus-Id Disp. | Volatile Uncorr.
> > ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage |
> > GPU-Util Compute M. |
> >
> |===============================+======================+======================|
> >
> >
> | 0 Tesla M2090 | 0000:14:00.0 Off |
> > 0 | | N/A N/A P0 77W / 225W | 0% 9MB / 5375MB |
> > 0% Default |
> >
> +-------------------------------+----------------------+----------------------+
> >
> >
> | 1 Tesla M2090 | 0000:15:00.0 Off |
> > 0 | | N/A N/A P0 77W / 225W | 0% 9MB / 5375MB |
> > 0% Default |
> >
> +-------------------------------+----------------------+----------------------+
> >
> >
> >
> >
> +-----------------------------------------------------------------------------+
> >
> >
> | Compute processes: GPU
> > Memory | | GPU PID Process name
> > Usage |
> >
> |=============================================================================|
> >
> >
> | No running compute processes found
> > |
> >
> +-----------------------------------------------------------------------------+
> >
> > thanks
> >
> > Asmita _______________________________________________ AMBER
> > mailing list AMBER.ambermd.org
> > http://lists.ambermd.org/mailman/listinfo/amber
> >
>
> - --
> ____ *Note: UMDNJ is now Rutgers-Biomedical and Health Sciences*
> || \\UTGERS |---------------------*O*---------------------
> ||_// Biomedical | Ryan Novosielski - Senior Technologist
> || \\ and Health | novosirj.rutgers.edu - 973/972.0922 (2x0922)
> || \\ Sciences | OIRT/High Perf & Res Comp - MSB C630, Newark
> `'
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Received on Sun Jan 11 2015 - 08:00:03 PST
