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| ||tikgpu01||Dual Tetrakaideca-Core Xeon E5-2680 v4||2.40GHz||28||503 GB||✓||1.1 TB||5 Titan Xp<<BR>>2 GTX Titan X||12 GB<<BR>>12 GB||Debian 10|| | ||tikgpu01||Dual Tetrakaideca-Core Xeon E5-2680 v4||2.40GHz||28||503 GB||✓||1.1 TB||1 !GeForce RTX 2080 Ti<<BR>>5 Titan Xp<<BR>>2 GTX Titan X||10 GB<<BR>>12 GB<<BR>>12 GB||Debian 10|| |
TIK Slurm information
The Computer Engineering and Networks Laboratory (TIK) owns nodes in the Slurm cluster with restricted access. The following information is an addendum to the main Slurm article in this wiki specific for accessing these TIK nodes.
If the information you're looking for isn't available here, please consult the main Slurm article.
Hardware
The following GPU nodes are reserved for exclusive use by TIK:
Server |
CPU |
Frequency |
Cores |
Memory |
/scratch SSD |
/scratch Size |
GPUs |
GPU Memory |
Operating System |
tikgpu01 |
Dual Tetrakaideca-Core Xeon E5-2680 v4 |
2.40GHz |
28 |
503 GB |
✓ |
1.1 TB |
1 GeForce RTX 2080 Ti |
10 GB |
Debian 10 |
tikgpu02 |
Dual Tetrakaideca-Core Xeon E5-2680 v4 |
2.40GHz |
28 |
503 GB |
✓ |
1.1 TB |
8 Titan Xp |
12 GB |
Debian 10 |
tikgpu03 |
Dual Tetrakaideca-Core Xeon E5-2680 v4 |
2.40GHz |
28 |
503 GB |
✓ |
1.1 TB |
8 Titan Xp |
12 GB |
Debian 10 |
tikgpu04 |
Dual Hectakaideca-Core Xeon Gold 6242 v4 |
2.80GHz |
32 |
754 GB |
✓ |
1.8 TB |
8 Titan RTX |
24 GB |
Debian 10 |
tikgpu05 |
AMD EPYC 7742 |
1.50 GHz |
256 |
503 GB |
✓ |
7.0 TB |
5 Titan RTX |
24 GB |
Debian 10 |
Accounts and partitions
The nodes are grouped in partitions to prioritize access for different accounts:
Partition |
Nodes |
Slurm accounts with access |
Account membership |
tikgpu.medium |
tikgpu[01-03] |
tik-external |
On request* for guests and students |
tikgpu.all |
tikgpu[01-05] |
tik-internal |
Automatic for staff members |
tikgpu.all |
tikgpu[01-05] |
tik-highmem |
On request* for guests and students |
* Please contact the person vouching for your guest access - or your supervisor if you're a student - and ask them to have you granted account membership
Overflow into gpu.normal
Jobs from TIK users will overflow to partition gpu.normal in case all TIK nodes are busy, as TIK is an institute contributing to the Slurm cluster besides owning nodes.
Dual account membership
If you're a member of account tik-external and have also been added to tik-highmem, your default account is the latter and all your jobs will by default be sent to partition tikgpu.all. So when you want to run jobs in partition tikgpu.medium you have to specify the account tik-external as in the following example:
sbatch --account=tik-external job_script.sh
If you already have a PENDING job in the wrong partition you can correct it by issuing the following command:
scontrol update jobid=<job id> partition=tikgpu.medium account=tik-external
Rules of conduct
There are no limits imposed on resources requested by jobs. Please be polite and share available resources sensibly. If you're in need of above-average resources, please coordinate with other TIK Slurm users.