The GPU A100 shares the same slurm batch system with all partitions of System Lise. The following slurm partitions are specific for the GPU A100 partition.

Slurm partition	Node number	CPU	Main memory (GB)	GPUs per node	GPU hardware	Walltime (hh:mm:ss)	Description
gpu-a100	34	Ice Lake 8360Y	1000	4	NVIDIA Tesla A100 80GB	24:00:00	full node exclusive
gpu-a100:shared	5			4	NVIDIA Tesla A100 80GB		shared node access, exclusive use of the requested GPUs
gpu-a100:shared:mig	1			28 (4 x 7)	1 to 28 1g.10gb A100 MIG slices		shared node access, shared GPU devices via Multi Instance GPU. Each of the four GPUs is logically split into usable seven slices with 10 GB of GPU memory associated to each slice
gpu-a100:test	2			4	NVIDIA Tesla A100 80GB	01:00:00	nodes reserved for short job tests before scheduling longer jobs with more resources

See Slurm usage how to pass a 24h walltime limit with job dependencies.

Charge rates

Charge rates for the slurm partitions you find in Accounting.

Examples

Assuming a job script

Job script example.slurm

#!/bin/bash
#SBATCH --partition=gpu-a100
#SBATCH --nodes=2
#SBATCH --ntasks=8 
#SBATCH --gres=gpu:4

module load openmpi/gcc.11/4.1.4
mpirun ./mycode.bin

you can submit a job to the slurm batch system via the line:

Job submission

bgnlogin2 $ sbatch example.slurm
Submitted batch job 7748544
bgnlogin2 $ squeue -u myaccount
...

Example: Exclusive usage of two nodes with 4 GPUs each

$ srun --nodes=2 --gres=gpu:4 --partition=gpu-a100 example_cmd

Example: Request two GPUs within the shared partition

# Note: The two GPUs may be located on different nodes.
$ srun --gpus=2 --partition=gpu-a100:shared example_cmd

# Note: Two GPUs on the same node.
$ srun --nodes=1 --gres=gpu:2 --partition=gpu-a100:shared example_cmd

Example: Request a single Multi Instance GPU slice on the according Slurm partition

$ srun --gpus=1 --partition=gpu-a100:shared:mig example_cmd

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