GPGPU Computing Servers

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

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

• Servers in HPC computing cluster: HPC computing cluster
• Servers in CESGA: Request access

In these servers, access 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, where the particular circumstances of each server are detailed (access criteria, priorities, conditions of use, etc.).

⚠️ The 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 only use ts (task-spooler) but a migration to a gpuctl-based system with optional tsp (task-spooler) is planned soon.

Not all servers are available at all times for any use. To access the servers, prior request is required through the incident form. Users without access permission will receive a wrong password message.

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

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 servers where there is a Slurm queue manager, its use is mandatory for submitting jobs and to avoid conflicts between processes, as no two jobs should run 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 finish to return control to the user. If you don't want to wait, you can use console session managers like screen, allowing you to leave the job in queue and disconnect without worry and retrieve the console output later.

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

nohup srun programa_cuda argumentos_programa_cuda &

To see the status of the queue, use the squeue command. The command displays output similar to this:

JOBID PARTITION     NAME     USER  ST       TIME  NODES NODELIST(REASON)
9  server ca_water pablo.qu    PD       0:00      1 (Resources)
10 server ca_water pablo.qu    PD       0:00      1 (Priority)
11 server ca_water pablo.qu    PD       0:00      1 (Priority)
12 server ca_water pablo.qu    PD       0:00      1 (Priority)
13 server ca_water pablo.qu    PD       0:00      1 (Priority)
14 server ca_water pablo.qu    PD       0:00      1 (Priority)
 8 server 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 that use gpuctl, it is necessary to use the gpu command to request GPU and be able to run jobs on them.

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

There are two main workflows:

This method is best 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 prevents the need to manually manage the reservation and release of the GPU.

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

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

Or, alternatively:

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

If there is already an active reservation, gpu exec will reuse it and automatically prepare the proper environment for the command.

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

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

If a GPU remains reserved with no activity for a prolonged period, the reservation may automatically be lost due to inactivity. Therefore, if afterwards a job is attempted to be executed assuming that the reservation is still active, the job may fail.

The reservation is not maintained simply by having an open terminal, but by actual activity on the GPU.

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

• Monday to Friday, from 10:00 to 18:00 → 1 hour
• At other times → 2 hours
• If the reservation seems abandoned (there are no user processes in the system nor 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 at which real use was captured for each, and the currently configured expiration time, you can execute:

gpu status

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

Although gpu does not implement a job queue per se, there is an implicit waiting system for GPU reservations.

When a command that requires reserving a GPU is executed and none are available, the command does not fail; it remains blocked waiting for a free GPU. The user enters an internal queue managed by gpuctl, and execution will automatically continue as soon as the reservation can be satisfied.

This behavior applies to both manual and automatic reservations.

For example:

gpu exec python ./train.py 

If no GPUs are available, this command will wait until one is freed.

Similarly:

gpu claim 

will also remain blocked until a GPU can be obtained.

By default, gpuctl automatically assigns an available GPU. However, it is possible to request a specific GPU by its index.

Both gpu claim and gpu exec allow you to use the parameter –gpu-index for this purpose:

gpu claim --gpu-index 0 
gpu exec --gpu-index 1 python ./train.py 

In this case, the command will attempt to reserve specifically the indicated GPU. If that GPU is not available, the command will wait in the queue until that particular GPU is freed.

This behavior allows you to fix executions to specific GPUs, but it may increase the wait time if the chosen GPU is in high demand.

In addition to the primary reservation made with priority in the guaranteed queue, it is possible to reserve a second GPU in the burst_preemptible queue on some servers (check the reservation policy message when entering the server, if applicable, it will refer to a second opportunistic GPU).

This second GPU can be used as long as it is free, but it is not guaranteed: it may be lost even in the middle of a job if another user needs that GPU for their primary reservation.

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

To request this second GPU, just execute gpu claim a second time:

gpu claim
gpu claim

It is also possible to explicitly specify the number of GPUs:

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 afterward;
• with gpu claim --numgpus 2, the request is made collectively and the command will wait until the two requested GPUs can be reserved.

It is important to remember that after making any reservation, you need to execute eval "$(gpu env --shell)" or perform the executions with gpu exec.

If the second GPU is later lost due to being preemptible, the jobs using it may 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 not needed for another priority reservation;
• if a GPU cannot be reserved, the command will remain waiting, and the user will enter the corresponding queue;
• when reserving two GPUs at once, the command will wait until both can be reserved simultaneously.

As gpu does not include a job queue or parallelism control, a practical option for queuing executions is to use task-spooler, with the tsp command, 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 several consecutive jobs queued in tsp.

If you want to run several jobs at the same time, you can increase the number of tsp slots:

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, you must be careful not to execute gpu release before all jobs finish. You should first ensure that the queue has completed, 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 computation time.

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

Using tmux allows maintaining terminal sessions, disconnecting from them, and retrieving them later, thus allowing for an interactive session without needing to keep the connection open. Disconnecting also does not send HUP to the processes.

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 keeping the session active, you should 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, just execute the previous command again.

If you want to completely terminate the session from outside:

tmux kill-session -t main

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

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

To reserve a GPU and activate 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, choosing the most suitable MIG profiles according to the compatible graphics card. Note that this --mig parameter can only be used when claiming exactly one GPU.

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

gpuctl allows configuring 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 due to being 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 later when that reservation is granted.

No notifications are sent if a reservation is made and granted immediately without needing to wait, or if the GPU is released manually with release or upon 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 the email if neither is configured.

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

 gpu report 

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

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

 gpu report --pid PID 

If there are many samples, the output may appear summarized. To see the complete series:

 gpu report --pid PID --all-samples 

• To run a single command, the simplest often is gpu exec.
• To maintain an active reservation between several commands, using gpu claim + gpu exec + gpu release is more appropriate.
• If you are going to work directly from the shell after making a manual reservation, you need to run eval "$(gpu env --shell)" or prefix with gpu exec.
• To leave active sessions and retrieve them later, tmux is usually the most convenient option.
• To queue multiple jobs, tsp is a practical alternative.
• If using tsp with parallelism, remember that does not reserve additional GPUs: the processes will share the visible GPU or GPUs at that moment.
• If a reservation remains too long without real computational activity on the GPU, it may automatically be lost due to inactivity.