BinAC2/SLURM Partitions: Difference between revisions
F Bartusch (talk | contribs) No edit summary |
F Bartusch (talk | contribs) No edit summary |
||
Line 34: | Line 34: | ||
=== GPU Jobs === |
=== GPU Jobs === |
||
BinAC 2 provides different GPU models for computations. Please select the appropriate GPU type |
BinAC 2 provides different GPU models for computations. Please select the appropriate GPU type and the amount of GPUs with the <code>--gres=aXX:N</code> option in your job script |
||
{| class="wikitable" |
{| class="wikitable" |
||
Line 46: | Line 46: | ||
| 24GB |
| 24GB |
||
| 2 |
| 2 |
||
| <code>--gres=gpu:a30: |
| <code>--gres=gpu:a30:N</code> |
||
|- |
|- |
||
| Nvidia A100 |
| Nvidia A100 |
||
| 80GB |
| 80GB |
||
| 4 |
| 4 |
||
| <code>--gres=gpu:a100: |
| <code>--gres=gpu:a100:N</code> |
||
|- |
|- |
||
|} |
|} |
Revision as of 17:12, 4 December 2024
Partitions
The bwForCluster BinAC 2 provides two partitions (e.g. queues) for job submission. Within a partition job allocations are routed automatically to the most suitable compute node(s) for the requested resources (e.g. amount of nodes and cores, memory, number of GPUs).
Partition | Node Access Policy | Node Types | Default | Limits |
---|---|---|---|---|
compute (default) | shared | cpu | ntasks=1, time=00:10:00, mem-per-cpu=1gb | nodes=2, time=14-00:00:00 |
gpu | shared | gpu | ntasks=1, time=00:10:00, mem-per-cpu=1gb | nodes=1, time=14-00:00:00 |
Parallel Jobs
In order to submit parallel jobs to the InfiniBand part of the cluster, i.e., for fast inter-node communication, please select the appropriate nodes via the --constraint=ib
option in your job script. For less demanding parallel jobs, you may try the --constraint=eth
option, which utilizes 100Gb/s Ethernet instead of the low-latency 100Gb/s InfiniBand.
GPU Jobs
BinAC 2 provides different GPU models for computations. Please select the appropriate GPU type and the amount of GPUs with the --gres=aXX:N
option in your job script
GPU | GPU Memory | # GPUs per Node [N] | Submit Option |
---|---|---|---|
Nvidia A30 | 24GB | 2 | --gres=gpu:a30:N
|
Nvidia A100 | 80GB | 4 | --gres=gpu:a100:N
|