Updated: 2024-07-01
Access
Is it possible run jobs from three submit hosts (aka access hosts):
labsrv0.math.unipd.it (new, please give it a try)
labsrv7.math.unipd.it
labsrv8.math.unipd.it
You can connect to these hosts via ssh from any host internal to the Department of Mathematics. To connect from the general internet you have to perform as first step an ssh connection to riemann.math.unipd.it or labta.math.unipd.it or guestportal.math.unipd.it (depending from your status: faculty member, student, guest) and then connect to the submit hosts.
Computing resources
The cluster is made up of 38 computing nodes. Some nodes are equipped with a CUDA card. Please, note the column ‘Labels’, this declare the “Features” (in SLURM terms) that can be used to select the nodes with the ‘--constraint=LABEL‘ switch (see the examples). This is the hardware list:
| Node | CPU | GPU | RAM | #Cores | Labels (aka Features) | Connectivity | LocalStorage |
| hpblade04 | 2 x Intel(R) Xeon(R) CPU E5520 @ 2.27GHz | none | 32GB | 8 | hpblade04, matlab | Ethernet 1GB | 50GB |
| hpblade05 | 2 x Intel(R) Xeon(R) CPU E5520 @ 2.27GHz | none | 32GB | 8 | hpblade05 | Ethernet 1GB | 50GB |
| hpblade06 | 2 x Intel(R) Xeon(R) CPU E5520 @ 2.27GHz | none | 32GB | 8 | hpblade06 | Ethernet 1GB | 50GB |
| hpblade07 | 2 x Intel(R) Xeon(R) CPU X5650 @ 2.67GHz | none | 64GB | 12 | hpblade07, matlab | Ethernet 1GB | 50GB |
| hpblade08 | 2 x Intel(R) Xeon(R) CPU X5650 @ 2.67GHz | none | 96GB | 12 | hpblade08, matlab | Ethernet 1GB | 50GB |
| hpblade12 | 2 x Intel(R) Xeon(R) CPU X5650 @ 2.67GHz | none | 32GB | 8 | hpblade12, matlab | Ethernet 1GB | 50GB |
| hpblade13 | 2 x Intel(R) Xeon(R) CPU X5650 @ 2.67GHz | none | 32GB | 8 | hpblade13, matlab | Ethernet 1GB | 50GB |
| hpblade16 | 2 x Intel(R) Xeon(R) CPU E5-2680 0 @ 2.70GHz | none | 256GB | 16 | hpblade16 | Ethernet 1GB | 80GB |
| dellcuda0 | 2 x Intel(R) Xeon(R) CPU E5-2630L v3 @ 1.80GHz | 1 x Nvidia V100 | 192GB | 16 | dellcuda0, matlab, V100, cudadrv495, volta | Ethernet 10GB | 200GB |
| dellcuda1 | 2 x Intel(R) Xeon(R) CPU E5-2630L v3 @ 1.80GHz | 1 x Nvidia A100 | 192GB | 16 | dellcuda1, matlab, A100, cudadrv495, ampere | Ethernet 10GB | 200GB |
| dellcuda2 | 2 x AMD EPYC 7301 16-Core | 1 x Nvidia V100 | 256GB | 32 | dellcuda2, V100, cudadrv495, volta | Ethernet 10GB | 500GB |
| dellsrv1 | 2 x Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz | 1 x Nvidia V100 | 160GB | 20 | dellsrv1, matlab, V100, cudadrv470, volta | Ethernet 10GB | 200GB |
| dellsrv2 | 2 x Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz | 1 x Nvidia T4 | 160GB | 20 | dellsrv2, matlab, T4, cudadrv495, turing | Ethernet 10GB | 200GB |
| dellsrv3 | 2 x Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz | 1 x Nvidia T4 | 160GB | 20 | dellsrv3, matlab, T4, cudadrv495, turing | Ethernet 10GB | 200GB |
| dellsrv4 | 2 x Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz | 1 x Nvidia T4 | 160GB | 20 | dellsrv4, matlab, T4, cudadrv510, turing | Ethernet 10GB | 200GB |
| dellsrv5 | 2 x Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz | 1 x Nvidia T4 | 160GB | 20 | dellsrv5, matlab, T4, cudadrv510, turing | Ethernet 10GB | 200GB |
| vgpu0-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu0-0, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu1-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu1-0, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu2-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu2-0, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu3-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu3-0, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu4-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu4-0, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu5-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu5-0, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu0-1 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu0-1, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu1-1 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu1-1, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu2-1 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu2-1, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu3-1 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu3-1, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu4-1 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu4-1, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu5-1 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 60GB | 4 | vgpu5-1, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu6-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A6000 | 120GB | 6 | vgpu6-0, A6000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu7-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A6000 | 120GB | 6 | vgpu7-0, A6000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu8-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A5000 | 120GB | 6 | vgpu8-0, A5000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu9-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A6000 | 120GB | 6 | vgpu9-0, A6000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| vgpu10-0 | 1 x Intel(R) Xeon(R) | 1 x Nvidia RTX A6000 | 120GB | 6 | vgpu10-0, A6000, cudadrv510, ampere | Ethernet 10GB | 20GB |
| debug00 | Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz | 1 x Nvidia RTX 2000 Ada Generation | 32GB | 6 | debug00, RTX2000, cudadrv555, ada | Ethernet 1GB | 40GB |
| debug01 | Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz | 1 x Nvidia RTX 2000 Ada Generation | 32GB | 6 | debug01, RTX2000, cudadrv555, ada | Ethernet 1GB | 40GB |
| debug02 | Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz | 1 x Nvidia RTX 2000 Ada Generation | 32GB | 6 | debug02, RTX2000, cudadrv555, ada | Ethernet 1GB | 40GB |
| debug03 | Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz | 1 x Nvidia RTX 2000 Ada Generation | 32GB | 6 | debug03, RTX2000, cudadrv555, ada | Ethernet 1GB | 40GB |
| debug04 | Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz | 1 x Nvidia RTX 2000 Ada Generation | 32GB | 6 | debug04, RTX2000, cudadrv555, ada | Ethernet 1GB | 40GB |
There are 352 cpu-cores and 30 gpu. As previously the field ‘Storage of the table describes the amount dof disk space available locally for every node for temporary storage of the intermediate results of the computations.
Storage
How stated before every node has an average of 50Gb of local disk space, other storage can be accessed via the network.
The table below describes the various storage unit with the ‘mount directory’ that has to be used for the access:
| Generic Name | Size (TB) | Availability | Mount directory | Connection |
| Home | 35 | All users | /home |
Ethernet 10GB |
| Storage | 34 | All users (on request) | /storage |
Ethernet 10GB |
| Extra | 90 | All users (on request) | /extra |
Ethernet 10GB |