The
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The following GPU partitions are available on Lise.
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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 | 42 | 4x A100 per Node | 34 | Ice Lake 8360Y | 1000 | 4 | NVIDIA Tesla A100 80GB | 24:00:00 | full node exclusive |
| gpu-a100:shared | FIXME5 | 1 to 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
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>#!/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:
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bgnlogin2 $ sbatch example.slurm
Submitted batch job 7748544
bgnlogin2 $ squeue -u myaccount
... |
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$ srun --nodes=2 --gres=gpu:4 --partition=gpu-a100 example_cmd |
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# Note: The two GPUs may be located on different nodes.
$ srun --gpus=2 --partition=gpu-a100:shared example_cmd |
Multi Instance GPU slice on the according partition
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# Note: Two GPUs on the same node. $ srun --nodes=1 --gres=gpu:2 --partition=gpu-a100:shared |
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example_cmd |
Lise (Berlin)
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Partition (number holds cores per node)
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Max jobs (running/ queued)
per user
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Usable memory MB per node
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CPU
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Charged core-hours per node
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16 / 500
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747 000
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1522 000
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very fat memory nodes for data pre- and postprocessing
12 hours are too short? See here how to pass the 12h walltime limit with job dependencies.
List of CPUs and GPUs at HLRN
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Cores per unit
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Clock speed
[GHz]
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640/5120*
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432/6912*
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$ srun --gpus=1 --partition=gpu-a100:shared:mig example_cmd |