These servers are intended for GPU computing (GPGPU), focused on intensive calculation tasks, machine learning, data processing, and scientific simulation that require acceleration by graphics hardware.

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

• Servers in the HPC computing cluster: HPC Computing Cluster
• Servers in 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 on Xici at the time of requesting the service, which details the specific circumstances of each server (access criteria, priorities, usage conditions, etc.).

⚠️ Servers ctgpgpu15, ctgpgpu16, ctgpgpu17 and ctgpgpu18 have a temporary installation and assignments, and their configuration and access may be altered around May 2026.

(*) For now, these servers are using only ts (task-spooler) but a migration to a gpuctl-based system with optional tsp (task-spooler) usage is planned soon.

Not all servers are available at all times for any use. To access the servers, a request must be made in advance through the incident form. Users without access permission will receive a message about 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, you need to use the VPN or the SSH gateway.

On servers with a Slurm queue manager, its use is mandatory for submitting jobs to avoid conflicts between processes, as two jobs should not run at the same time.

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

srun programa_cuda argumentos_programa_cuda

The srun process waits for the job to execute to return control to the user. If you do not want to wait, session managers like screen can be used to leave the job pending and disconnect the session without worry and retrieve the console output later.

Alternatively, nohup can be used and the job sent to 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 command squeue is used. The command shows 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

An interactive view, updated every second, can also be obtained with the smap command:

smap -i 1

On servers using ts as a job manager, it is mandatory to use it to execute tasks utilizing GPU, in order to avoid conflicts and ensure proper resource allocation.

To request a GPU, the option -G 1 (or the number of required GPUs) must precede:

ts -G 1 programa_cuda argumentos_programa_cuda

For example:

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

The system will handle queuing the job and executing it when a GPU is available.

For more advanced usage examples (multiple GPUs, additional resources, specific options, etc.), the command can be used:

usage-overview

On servers using gpuctl, it is necessary to use the gpu command to request GPUs and execute jobs on them.

The gpu command does not incorporate a job queue or parallelism control. It can be used interactively or from scripts, but to queue multiple jobs or execute them in parallel, external tools like task-spooler (command tsp) must be used.

There are two main workflows:

This method is most suitable if you only want to execute a single command. gpu exec acts as a wrapper: it takes care of claiming the GPU, preparing the necessary environment for the command, and releasing it upon completion.

gpu exec python ./train.py

This is the recommended option for simple executions as it avoids managing GPU reservation and release manually.

This method is most suitable if you want to maintain an active reservation between several commands, whether in an interactive session or in a workflow with multiple consecutive executions.

gpu claim
gpu exec python ./train.py
gpu release

Alternatively:

gpu claim
eval "$(gpu env --shell)"
python ./train.py
gpu release

If there is already an active reservation, gpu exec reuses it and automatically prepares the appropriate environment for the command.

It is important to remember that, after creating or modifying a reservation, you must run eval "$(gpu env --shell)" again if you are going to launch the commands directly from the shell. Alternatively, you can use gpu exec, which prepares the environment for each execution.

Reservations are maintained as long as real computational activity is detected on the GPU.

If a GPU remains reserved without activity for an extended time, the reservation may be lost automatically due to inactivity. Therefore, if a job is attempted to be executed afterward assuming the reservation is still active, the job may fail.

The reservation is not maintained simply for having a terminal open, but for real GPU activity.

The expiration times without real GPU activity are configured as follows:

• Monday to Friday, from 10:00 to 18:00 → 1 hour
• At other times → 2 hours
• If the reservation appears abandoned (no user processes on the system or any shell) → 10 minutes

If you are working with a manual reservation and want to check if it is still active, you can use:

gpu mine

Or, if you want to see general information about all GPUs, the time when actual use was captured for each, and the current configured expiration time, you can execute:

gpu status

This is especially advisable if time has passed since the last execution or if the GPU may have been unused for a long period.

Besides the main reservation, which has priority in the guaranteed queue, it is possible to reserve a second GPU in the burst_preemptible queue.

This second GPU can be used as long as it is free, but it is not guaranteed: it can be lost even during a job if another user needs that GPU for their main reservation.

This allows taking advantage of additional spare capacity when available, but jobs depending on this second GPU must be prepared to tolerate its loss.

To request this second GPU, simply running gpu claim a second time is sufficient:

gpu claim
gpu claim

You can also specify the number of GPUs explicitly:

gpu claim --numgpus 2

In this case, the command will wait until it can reserve the requested number of GPUs.

It is important to note that these two methods are not exactly equivalent:

• with gpu claim followed by another gpu claim, the first reservation is obtained first and the second is requested afterwards;
• with gpu claim --numgpus 2, the request is made jointly and the command will wait until both requested GPUs can be reserved simultaneously.

It is essential to remember that, after making any reservation, you must run eval "$(gpu env --shell)" again or make executions with gpu exec.

If the second GPU is later lost due to being preemptible, jobs that are using it could fail or be interrupted.

Therefore:

• the first GPU is the priority and guaranteed reservation;
• the second GPU is opportunistic and will only be available while it is not needed for another priority reservation;
• if any GPU cannot be reserved, the command will wait and the user will move to the corresponding queue;
• when reserving two GPUs at once, the command will wait until they can both be reserved simultaneously.

Since gpu does not incorporate a job queue or parallelism control, a practical option for queuing executions is to use task-spooler, with the command tsp, combined with gpu.

gpu claim
tsp gpu exec python ./train1.py
tsp gpu exec python ./train2.py
tsp gpu exec python ./train3.py
tsp gpu release

This method allows reusing the same reservation for multiple consecutive jobs queued in tsp.

If several jobs are to be run simultaneously, the number of tsp slots can be increased:

gpu claim
tsp -S 2
tsp gpu exec python ./train1.py
tsp gpu exec python ./train2.py
tsp gpu exec python ./train3.py
tsp gpu exec python ./train4.py
tsp -w
tsp -S 1
gpu release

In this case, care must be taken not to run gpu release before all jobs have finished. First, you must ensure that the queue has finished, and only then release the GPU.

Attention: running multiple jobs in parallel does not imply reserving multiple GPUs. If only one GPU is reserved, all processes will share that same GPU and compete for its resources, such as memory and compute time.

It is also important to remember that the reservation is maintained only while real computational activity is detected on the GPU. If jobs spend too much time without using it, the reservation may automatically expire due to inactivity.

Using tmux allows maintaining terminal sessions, disconnecting from them, and reconnecting later, maintaining an interactive session without having to keep the connection open. Disconnecting does not send HUP to the processes either.

To start a tmux session or reconnect to it if it already exists, it is recommended to use a fixed name:

tmux new -A -s main

This creates the main session if it does not exist, or reconnects to it if it was already created.

Once inside tmux, to disconnect while maintaining the session active, you must press Control+b, release, and then press d.

At this point, you can disconnect from the machine without interrupting the processes. To return to the same session, simply execute the previous command again.

If you wish to terminate the session completely from outside:

tmux kill-session -t main

gpuctl allows partitioning a GPU into several MIG instances to isolate resources and run smaller workloads in parallel on a single physical GPU in a more controlled manner.

This functionality is only available on compatible GPUs; otherwise, it cannot be used. Currently, only NVIDIA H200 GPUs have this function enabled.

To reserve a GPU and enable it in MIG mode:

gpu claim --mig half
gpu claim --mig third
gpu claim --mig quarter

This will partition the GPU into two, three, or four partitions, selecting the most suitable MIG profiles based on the compatible graphics card. Note that this --mig parameter can only be used when claiming exactly one GPU.

Once a MIG reservation is created, gpu exec and gpu env will automatically prepare CUDA_VISIBLE_DEVICES with the corresponding MIG UUIDs.

gpuctl allows configuring the sending of email notifications related to the status of reservations and GPU usage.

gpu notify

gpu notify --mode off
gpu notify --mode email
gpu notify --mode telegram
gpu notify --mode both

When notifications are activated, emails or Telegram messages are sent in the following cases:

• when a GPU is automatically released for remaining unused;
• when the enforcer kills a process for using GPU without having an active reservation or losing an opportunistic GPU;
• when a reservation request is queued;
• and subsequently when that reservation is granted.

No notifications are sent if a reservation is made and the reservation is granted immediately without needing to wait, or if the GPU is released manually with release or after finishing an exec that made an automatic reservation.

gpu notify --email your.email@domain

If a custom email is not configured, the address obtained from LDAP will be used by default. To revert to the default email:

gpu notify --clear-email

After executing the following command:

gpu notify --pair-telegram

Follow the instructions to complete the pairing process. To unpair:

gpu notify --unpair-telegram

The user can send custom notifications at various points in their workflow by using:

gpu notify --send "message content"

This can be useful for receiving personal alerts at the beginning or end of an experiment, after completing a phase of processing, or at any other relevant point in the workflow.

The preferred notification method that is configured will be used, or email if neither of the two is configured.

To consult a summary of the recent usage history of the GPU by process, you can use:

 gpu report 

This displays the registered PIDs, the number of available samples, and aggregated metrics of GPU, CPU, and memory usage.

If you want to see the data of a specific process, with ASCII graphs and the time line of stored samples, you can use:

 gpu report --pid PID 

If there are many samples, the output may appear summarized. To view the full series:

 gpu report --pid PID --all-samples 

• To execute a single command, the simplest option is usually gpu exec.
• To maintain an active reservation among several commands, it is more suitable to use gpu claim + gpu exec + gpu release.
• If you are going to work directly from the shell after making a manual reservation, you must execute eval "$(gpu env --shell)" or prepend gpu exec.
• To leave active sessions and recover them later, tmux is usually the most convenient option.
• For queuing multiple jobs, tsp is a practical alternative.
• If using tsp with parallelism, it should be remembered that it does not reserve additional GPUs: processes will share the GPU or visible GPUs at that time.
• If a reservation remains inactive for too long without real GPU computation, it may be lost automatically due to inactivity.