March 2026 - slurm-users - lists.schedmd.com

Shared queue: how to request full node with all resources?
by Dustin Lang 31 Mar '26

31 Mar '26

Hi, With a partition with "OverSubscribe=FORCE" set, is there a way to request all the node resources? I see "--mem=0" does that for memory. But I do not see an option to request all the CPUs and GRES/TRES such as GPUs. I tried "--nodes=1 --ntasks=1 --cpus-per-task=0", but "--cpus-per-task=0" does not do the same thing as "--mem=0". In other words, is it possible to have a queue where nodes can be shared between jobs, but have a simple way for an sbatch script to request the full node with all its memory, cpus, and other resources? We have a heterogeneous cluster, so telling users (or jupyterhub scripts) to list exactly the resources they want doesn't really work. thanks, dustin

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How do you handle GPU node failures during long jobs?
by Antonio Jose Alonso-Stepanov 26 Mar '26

26 Mar '26

Hi all, I'm a Stanford CS student looking into how sites handle GPU node failures during long-running jobs. A couple questions: When a GPU node goes down mid-job, do most sites use Slurm's requeue or --no-kill to handle it, or is it mostly manual drain and resubmit? Is anyone using HealthCheckProgram to catch GPU issues (like ECC errors via DCGM), or do you handle GPU health monitoring outside of Slurm? Curious what's worked and what hasn't. Thanks. Antonio

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_node_config_validate: gres/gpu: Count changed on node (0 != 2)
by Xaver Stiensmeier 23 Mar '26

23 Mar '26

Hey Slurm-users list, while our regular gpu nodes are working fine, our on demand gpu nodes have a weird issue. They power up, I can ssh onto them and execute nvidia-smi on them without issue, but they are marked invalid and slurmctld logs _node_config_validate: gres/gpu: Count changed on node (0 != 2) however, node show shows that the gpus are recognized and the gres.conf are stored on the worker nodes as expected and the node entries in the slurm.conf are fine, too: # slurm.conf NodeName=my_worker_node SocketsPerBoard=16 CoresPerSocket=1 RealMemory=64075 MemSpecLimit=4000 State=CLOUD Gres=gpu:L4:2 # openstack # gres.conf on my_worker_node ubuntu@my_node:~$ cat /etc/slurm/gres.conf # GRES CONFIG Name=gpu Type=L4 File=/dev/nvidia0 Name=gpu Type=L4 File=/dev/nvidia1 Thankful for any ideas and debugging ideas. Best, Xaver PS: By executing: sudo scontrol update NodeName=$(bibiname 0) Gres= sudo scontrol reconfigure sudo scontrol update NodeName=$(bibiname 0) state=RESUME reason=None the node can be resumed. However, this is not a real solution.

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We are pleased to announce the availability of Slurm versions 25.11.4 and 25.05.7.
by Marshall Garey 12 Mar '26

12 Mar '26

We are pleased to announce the availability of Slurm versions 25.11.4 and 25.05.7. Changes in both versions include preventing SIGHUP from terminating slurmrestd. Changes in 25.11.4 include fixing several issues with the switch/nvidia_imex plugin, several memory leaks, a race that could cause jobs to be stuck in the completing state or be requeued and held if the --get-user-env option was used, flex reservations taking nodes from other reservations that do not request full nodes, and a variety of other issues. Changes in 25.05.7 include fixing the gpu/nrt plugin, and fixing some other regressions and stability issues. The full list of changes are available in the CHANGELOG files for each version: https://github.com/SchedMD/slurm/blob/slurm-25.11/CHANGELOG/slurm-25.11.md https://github.com/SchedMD/slurm/blob/slurm-25.05/CHANGELOG/slurm-25.05.md Slurm can be downloaded from: https://www.schedmd.com/download-slurm/ -- Marshall Garey Senior System Software Engineer NVIDIA

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Server with two GPUs sharing each GPU to different partitions
by Gestió Servidors 09 Mar '26

09 Mar '26

Hello, First of all, sorry if my question is about something easy, but in my environment, this is the first time I need to do this. I have one server that have 2 identical GPUs. I have two different partitions: one is accessible from some allocation nodes and the other from other allocation nodes. My question is if it's possible to share one GPU with one partition and the other GPU with the other partition. For example, something like this: * gres.conf: NodeName=gpu-node AutoDetect=off Name=gpu Type=RTX5070 File=/dev/nvidia0 Cores=0-127 NodeName=gpu-node AutoDetect=off Name=gpu Type=RTX5070 File=/dev/nvidia1 Cores=128-255 * slurm.conf: NodeName=gpu-node CPUs=256 SocketsPerBoard=2 CoresPerSocket=64 ThreadsPerCore=2 RealMemory=260000 TmpDisk=47000 Gres= RTX5070:2 PartitionName=gpu-int.q Nodes=gpu-node OverSubscribe=No State=UP AllocNodes=submit-node-1 PriorityTier=3 GraceTime=900 PartitionName=gpu-ext.q Nodes=gpu-node OverSubscribe=No State=UP AllocNodes=submit-node-1 PriorityTier=3 GraceTime=900 With this configuration, if simultaneously two different users tries to execute in one of the RTX5070 (one in gpu-int.q and the other in gpu-ext.q), could both of them execute with no problems? If user from gpu-int.q uses device #0 and user from gpu-ext.q uses device #1, could SLURM work without problems if a second simultaneously exection assigns device #1 to user from gpu-int.q and device #0 to user from gpu-ext.q? Thanks.

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Optimizing CPU allocation in Slurm with hyperthreading enabled
by Guillaume COCHARD 02 Mar '26

02 Mar '26

Hello, The Slurm documentation states that "The default allocation method within a node is cyclic allocation (allocate available CPUs in a round-robin fashion across the sockets within a node)." In our case, we are using hyperthreading, which means that if two jobs requesting one CPU each arrive at the same time on an empty node, they will be assigned to the two threads of the same physical core. To improve performance, we would like Slurm to allocate CPUs (in our case, threads) in a way that prioritizes using one thread per physical core first, and only once all cores are occupied, assign the second thread of each core. This would be similar to the Least Loaded Node (LLN) approach, but instead of distributing jobs across nodes, it would distribute them across physical cores (Least Loaded Core). We do not want to disable hyperthreading; we simply want to optimize core usage when a machine is not fully loaded. We also want this to be a cluster-wide setting, and not a user and/or job setting. Our current slurm.conf contains: ConstrainCores = no SelectType = select/cons_tres SelectTypeParameters = CR_CPU_MEMORY And our cgroup.conf contains: ConstrainDevices=yes ConstrainCores=yes Does anyone know how to achieve this? Thanks, Guillaume

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