GPU nodes

Para el uso de cualquier GPU al enviar trabajos es necesario indicar las opciones *–gres=gpu* (si no se indica la opción *–gres=gpu* no se asignará la GPU). En la página del manual de sbatch aparece:

–gres=<list>

Specifies a comma delimited list of generic consumable resources. The

format of each entry on the list is “name[[:type]:count]”. The name is that of the consumable resource. The count is the number of those resources with a default value of 1. The specified resources will be allocated to the job on each node. The available generic consumable resources is configurable by the system administrator. A list of available generic consumable resources will be printed and the command will exit if the option argument is “help”. Examples of use include “–gres=gpu”, “–gres=gpu2”, and “–gres=help”.

–gres-flags=enforce-binding

If set, the only CPUs available to the job will be those bound to the

selected GRES (i.e. the CPUs identified in the gres.conf file will be strictly enforced rather than advisory). This option may result in delayed initiation of a job. For example a job requiring two GPUs and one CPU will be delayed until both GPUs on a single socket are available rather than using GPUs bound to separate sockets, however the application performance may be improved due to improved communication speed. Requires the node to be configured with more than one socket and resource filtering will be performed on a per-socket basis.

Adicionalmente es necesario enviar al trabajo a la correspondiente partición según el modelo de GPU.

Existen los siguientes modelos de GPU actualmente habilitados en el Finis Terrae II:

*–partition=gpu-shared-v100*

*srun -p gpu-shared-v100 –gres=gpu -t 20 nvidia-smi topo -m* |image14|

Per GPU: CUDA Capability Major/Minor version number:3.7 MEMORY: 12 GB of GDDR5 BANDWIDTH 240 GB/s (13) Multiprocessors, (192) CUDA Cores/MP: 2496 CUDA Cores GPU Max Clock rate: 824 MHz (0.82 GHz)

Uso interactivo: *compute –gpu* *–partition=gpu-shared*

*srun -p gpu-shared –gres=gpu -t 20 nvidia-smi topo -m* |image15|

*–partition=gpu-shared-k2 –qos=shared*

*srun -p gpu-shared-k2 –qos=shared –gres=gpu -t 20 nvidia-smi topo -m* |image16|

Uso interactivo: *compute –gpu-t4*

*nvidia-smi topo -m* |image17|