These servers are intended for GPU computing (GPGPU), aimed at intensive computing tasks, machine learning, data processing, and scientific simulation that require hardware acceleration.

Any researcher from the center can request access to these servers. Access is granted upon prior request and validation.

• Servers in the HPC computing cluster: HPC Computing Cluster
• Servers at CESGA: Request Access

Access to these servers is restricted to a specific group, specific project, or is more controlled due to resource management and planning issues.

It is essential to check the updated information in Xici at the time of requesting the service, which details the particular circumstances of each server (access criteria, priorities, usage conditions, etc.).

⚠️ The servers ctgpgpu15, ctgpgpu16, ctgpgpu17 and ctgpgpu18 have a temporary installation and assignments, and their configuration and access may change around May 2026.

Not all servers are available at all times for any use. To access the servers, you must request it in advance through the incident form. Users without access permission will receive a message of incorrect password.

To connect to the servers, you must do so via SSH. The names and IP addresses of the servers are as follows:

The connection is only available from the center's network. To connect from other locations or from the RAI network, it is necessary to use the VPN or the SSH gateway.

On the servers where there is a Slurm queue manager, its use is mandatory to submit jobs and thus avoid conflicts between processes, as two jobs should not be executed at the same time.

To submit a job to the queue, use the srun command:

srun programa_cuda argumentos_programa_cuda

The srun process waits for the job to run before returning control to the user. If you do not want to wait, you can use console session managers like screen, allowing you to leave the job waiting and disconnect the session without worry and retrieve the console output later.

Alternatively, you can use nohup and place the job in the background with &. In this case, the output is saved in the nohup.out file:

nohup srun programa_cuda argumentos_programa_cuda &

To check the status of the queue, the squeue command is used. The command produces output similar to this:

JOBID PARTITION     NAME     USER  ST       TIME  NODES NODELIST(REASON)
9  servidore ca_water pablo.qu    PD       0:00      1 (Resources)
10 servidore ca_water pablo.qu    PD       0:00      1 (Priority)
11 servidore ca_water pablo.qu    PD       0:00      1 (Priority)
12 servidore ca_water pablo.qu    PD       0:00      1 (Priority)
13 servidore ca_water pablo.qu    PD       0:00      1 (Priority)
14 servidore ca_water pablo.qu    PD       0:00      1 (Priority)
 8 servidore ca_water pablo.qu     R       0:11      1 ctgpgpu2

You can also obtain an interactive view, updated every second, with the smap command:

smap -i 1

On servers that use ts as the job manager, it is mandatory to use it to run tasks that use GPU, in order to avoid conflicts and ensure correct resource allocation.

To request a GPU, you must prepend the option -G 1 (or the number of GPUs needed):

ts -G 1 programa_cuda argumentos_programa_cuda

For example:

ts -G 1 python train.py --epochs 100

The system will take care of placing the job in the queue and running it when a GPU is available.

To check more advanced examples (multiple GPUs, additional resources, specific options, etc.), you can use the command:

usage-overview