Editing Cluster:Compute nodes (section)

== List of compute nodes ==

'''NOTE: Imp and Dretch do not have an infiniband connection, so ceph filesystem access is slightly slower. Using the local raid for caching data is recommended.
Both machines (Imp in particular) have much less powerful GPUs than the rest of the cluster, so these two systems are ideal for testing and experimenting.
'''


The following GPU nodes are currently part of the cluster. There are more nodes which act as API servers or provide the Ceph filesystem and web services, but these are not available for standard users.

Note: Labels / Taints in this table might be outdated, use "kubectl describe node <name>" for up-to-date information.

{| class="wikitable"
|-
! scope="col"| CCU name
! scope="col"| Access
! scope="col"| Platform
! scope="col"| GPUs
! scope="col"| Labels
! scope="col"| Taints
|-
! scope="row"| imp
| all
| Dual Xeon Rack
| 4 x Titan Xp @ 12 GB
| gpumem=12, gpuarch=nvidia-titan, nvidia-compute-capability-sm70=true
| 
|-
! scope="row"| dretch
| all
| Dual Xeon Rack
| 4 x Titan RTX @ 24 GB
| gpumem=24, gpuarch=nvidia-titan, nvidia-compute-capability-sm70=true
| 
|-
! scope="row"| belial
| exc-cb
| Supermicro
| 8 x Quadro RTX 6000 @ 24 GB
| gpumem=24, gpuarch=nvidia-rtx, nvidia-compute-capability-sm75=true
| gpumem=24:NoSchedule
|-
! scope="row"| fierna
| exc-cb
| Supermicro
| 8 x Quadro RTX 6000 @ 24 GB
| gpumem=24, gpuarch=nvidia-rtx, nvidia-compute-capability-sm75=true
| gpumem=24:NoSchedule
|-
! scope="row"| vecna
| exc-cb, inf
| nVidia DGX-2
| 16 x V100 @ 32 GB
| gpumem=32, gpuarch=nvidia-v100, nvidia-compute-capability-sm80=true
| gpumem=32:NoSchedule
|-
! scope="row"| zariel
| trr161
| nVidia DGX A100
| 8 x A100 @ 40 GB
| gpumem=40, gpuarch=nvidia-a100, nvidia-compute-capability-sm80=true
| gpumem=40:NoSchedule
|-
! scope="row"| tiamat
| exc-cb
| Supermicro
| 4 x A100 @ 40 GB
| gpumem=40, gpuarch=nvidia-a100, nvidia-compute-capability-sm80=true
| gpumem=40:NoSchedule
|-
! scope="row"| asmodeus
| all
| Supermicro
| 4 x A100 HGX 320 GB, subdivided in 8 GPUs @ 40 GB
| gpumem=40, gpuarch=nvidia-a100, nvidia-compute-capability-sm80=true
| gpumem=40:NoSchedule
|-
! scope="row"| demogorgon
| exc-cb
| Delta
| 8 x A40 @ 48 GB
| gpumem=48, gpuarch=nvidia-a40, nvidia-compute-capability-sm80=true
| gpumem=48:NoSchedule
|-
! scope="row"| kiaransalee
| seds
| Delta
| 8 x H100 HGX 640 GB
| gpumem=80, gpuarch=nvidia-h100, nvidia-compute-capability-sm80=true
| gpumem=80:NoSchedule
|-
|}


The CCU name is the internal name used in the Kubernetes cluster, as well as the configured hostname of the node. Nodes are not accessible from the outside world, you have to access the cluster via kubectl through the API-server.

In the column "Access" you can find which Kubernetes user groups is allowed to access this node. Please only target a specific node if you are allowed to.

{| class="wikitable"
|-
! scope="col"| Group
! scope="col"| Desciption
|-
! scope="row"| exc-cb
| Centre for the Advanced Study of Collective Behaviour
|-
! scope="row"| trr161
| SFB Transregio 161 "Quantitative Methods for Visual Computing"
|-
! scope="row"| inf
| Department of Computer Science
|-
! scope="row"| seds
| Social and Economic Data Sciences
|-
! scope="row"| cvia
| Computer Vision and Image Analysis Group
|-
|}