Hi
Depending on what cards you have, and what version of slurm you have,
there is a way to run multiple jobs on a gpu, using Multi-Instance GPU
(MIG).
https://www.nvidia.com/en-us/technologies/multi-instance-gpu/
This is a "newer" nvidia feature, and slurm has only recently started
supporting it.
I am not sure anyone in the amber community has truly tested this combo,
so beware or bumps in the road.
The second way is to send MANY jobs inside one single slurm script such as:
pmemd,cuda -O "job 1 info"
.
.
.
pmemd,cuda -O "job N info"
This way the whole thing gets into the queue at once, and runs the jobs
sequentially.
You can even do this is for example job 2 depends on job finishing, etc.
You can have a bash script inside your slurm submission file to do this
automatically.
Adrian
On 2/15/22 10:14 AM, Prithviraj Nandigrami wrote:
> [External Email]
>
> Dear All,
>
> I am utilizing a GPU with 4 graphics cards to run relatively short amber md
> jobs with amber20 pmemd.cuda executable. I have a total of ~ 80,000 such
> short amber jobs. Each job takes < 30 seconds to complete on a graphics
> card on the GPU.
>
> A (potential) problem with this strategy is that currently I can not run
> multiple jobs at the same time on a particular graphics card. As a result,
> the queue time is rather long as I am only running four such jobs at a time
> (on four graphics cards).
>
> Is there a way around this? I am using the SLURM queuing system to submit
> the jobs on the GPU.
>
> Thank you.
> P. Nandigrami
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--
Dr. Adrian E. Roitberg (he/him/el)
V.T. and Louise Jackson Professor in Chemistry
Department of Chemistry
University of Florida
roitberg.ufl.edu
352-392-6972
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Received on Tue Feb 15 2022 - 07:30:03 PST
