The
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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.
Emmy (Göttingen)
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Partition (number holds cores per node)
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Usable memory MB per node
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CPU, GPU type
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gcn#
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* 600 for the nodes with 4 GPUs, and 1200 for the nodes with 8 GPUs
Which partition to choose?
If you do not request a partition, your job will be placed in the default partition, which is standard96.
The default partitions are suitable for most calculations. The :test partitions are, as the name suggests, intended for shorter and smaller test runs. These have a higher priority and a few dedicated nodes, but are limited in time and number of nodes. Shared nodes are suitable for pre- and postprocessing. A job running on a shared node is only accounted for its core fraction (cores of job / all cores per node). All non-shared nodes are exclusive to one job, which implies that full NPL are paid.
Details about the CPU/GPU types can be found below.
The network topology is described here.
The available home/local-ssd/work/perm storages are discussed in File Systems.
An overview of all partitions and node statuses is provided by: sinfo -r
To see detailed information about a nodes type: scontrol show node <nodename>
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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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
| Codeblock | ||||
|---|---|---|---|---|
| ||||
#!/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
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you can submit a job to the slurm batch system via the line:
| Codeblock | ||||
|---|---|---|---|---|
| ||||
bgnlogin2 $ sbatch example.slurm
Submitted batch job 7748544
bgnlogin2 $ squeue -u myaccount
... |
| Codeblock | ||
|---|---|---|
| ||
$ srun --nodes=2 --gres=gpu:4 --partition=gpu-a100 example_cmd |
| Codeblock | ||
|---|---|---|
| ||
# 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 |
| Codeblock | ||
|---|---|---|
| ||
$ srun --gpus=1 --partition=gpu-a100:shared:mig example_cmd |