Slurm partition GPU A100
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 | 24:00:00 | full node exclusive | |
gpu-a100:shared | 5 | 4 | 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 | 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