Difference between revisions of "Cluster:Compute nodes"

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(List of compute nodes)
m (List of compute nodes)
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| nVidia DGX-2
 
| nVidia DGX-2
 
| 16 x V100 @ 32 GB
 
| 16 x V100 @ 32 GB
| gpumem-32=true, nvidia-v100=true, nvidia-compute-capability-sm80=true
+
| gpumem-32, nvidia-v100, nvidia-compute-capability-sm80
| gpumem-32=true
+
| gpumem-32
 
|-
 
|-
 
! scope="row"| Glasya
 
! scope="row"| Glasya
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| Dual Xeon Rack
 
| Dual Xeon Rack
 
| 4 x Titan RTX @ 24 GB
 
| 4 x Titan RTX @ 24 GB
 +
| gpumem-24, nvidia-rtx, nvidia-compute-capability-sm80
 +
| gpumem-24
 
|-
 
|-
 
! scope="row"| Belial
 
! scope="row"| Belial
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| Supermicro
 
| Supermicro
 
| 8 x Quadro RTX 6000 @ 24 GB
 
| 8 x Quadro RTX 6000 @ 24 GB
 +
| gpumem-24, nvidia-rtx, nvidia-compute-capability-sm75
 +
| gpumem-24
 
|-
 
|-
 
! scope="row"| Fierna
 
! scope="row"| Fierna
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| Supermicro
 
| Supermicro
 
| 8 x Quadro RTX 6000 @ 24 GB
 
| 8 x Quadro RTX 6000 @ 24 GB
 +
| gpumem-24, nvidia-rtx, nvidia-compute-capability-sm75
 +
| gpumem-24
 
|-
 
|-
 
! scope="row"| Zariel
 
! scope="row"| Zariel
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| nVidia DGX A100
 
| nVidia DGX A100
 
| 8 x A100 @ 40 GB
 
| 8 x A100 @ 40 GB
 +
| gpumem-40, nvidia-a100, nvidia-compute-capability-sm80
 +
| gpumem-40
 
|-
 
|-
 
! scope="row"| Tiamat
 
! scope="row"| Tiamat
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| Supermicro
 
| Supermicro
 
| 4 x A100 @ 40 GB
 
| 4 x A100 @ 40 GB
 +
| gpumem-40, nvidia-a100, nvidia-compute-capability-sm80
 +
| gpumem-40
 
|-
 
|-
 
! scope="row"| Asmodeus
 
! scope="row"| Asmodeus
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| Supermicro
 
| Supermicro
 
| 4 x A100 HGX 320 GB, subdivided in 16 GPUs @ 20 GB
 
| 4 x A100 HGX 320 GB, subdivided in 16 GPUs @ 20 GB
 +
| gpumem-20, nvidia-a100, nvidia-compute-capability-sm80
 +
|
 
|-
 
|-
 
! scope="row"| Demogorgon
 
! scope="row"| Demogorgon
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| Delta
 
| Delta
 
| 8 x A40 @ 40 GB
 
| 8 x A40 @ 40 GB
 +
| gpumem-40, nvidia-a40, nvidia-compute-capability-sm80
 +
| gpumem-40
 
|-
 
|-
 
|}
 
|}

Revision as of 17:47, 27 November 2021

List of compute nodes

The following nodes are currently part of the cluster. Note that the master node is CPU only and not used for computations, as it hosts all CCU infrastructure (among a few other things).

CCU name Access Platform GPUs Labels Taints
Vecna exc-cb, inf nVidia DGX-2 16 x V100 @ 32 GB gpumem-32, nvidia-v100, nvidia-compute-capability-sm80 gpumem-32
Glasya trr161 Dual Xeon Rack 4 x Titan RTX @ 24 GB gpumem-24, nvidia-rtx, nvidia-compute-capability-sm80 gpumem-24
Belial exc-cb Supermicro 8 x Quadro RTX 6000 @ 24 GB gpumem-24, nvidia-rtx, nvidia-compute-capability-sm75 gpumem-24
Fierna exc-cb Supermicro 8 x Quadro RTX 6000 @ 24 GB gpumem-24, nvidia-rtx, nvidia-compute-capability-sm75 gpumem-24
Zariel trr161 nVidia DGX A100 8 x A100 @ 40 GB gpumem-40, nvidia-a100, nvidia-compute-capability-sm80 gpumem-40
Tiamat exc-cb Supermicro 4 x A100 @ 40 GB gpumem-40, nvidia-a100, nvidia-compute-capability-sm80 gpumem-40
Asmodeus cvia Supermicro 4 x A100 HGX 320 GB, subdivided in 16 GPUs @ 20 GB gpumem-20, nvidia-a100, nvidia-compute-capability-sm80
Demogorgon exc-cb Delta 8 x A40 @ 40 GB gpumem-40, nvidia-a40, nvidia-compute-capability-sm80 gpumem-40


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 can access this node.

Group Desciption
exc-cb Centre for the Advanced Study of Collective Behaviour
trr161 SFB Transregio 161 "Quantitative Methods for Visual Computing"
inf Department of Computer Science
cvia Computer Vision and Image Analysis Group