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SchedMD joins forces with NVIDIA
by Danny Auble 15 Dec '25

15 Dec '25

Today marks a new journey in the history of SchedMD - we are joining forces with NVIDIA. NVIDIA is committed to developing and supporting Slurm as the open-source vendor-neutral workload manager it has always been. For more details, please see NVIDIA's blog post: https://blogs.nvidia.com/blog/nvidia-acquires-schedmd/ Danny

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Slurm 25.05 and 25.11 release video, slides from recent presentations
by Tim Wickberg 12 Dec '25

12 Dec '25

The release video covering what's new in Slurm 25.05 and 25.11 is online now on the SchedMDSlurm YouTube channel: https://youtu.be/gk42j2_kxFg The is the same content that we presented at the Slurm Community Birds-of-a-Feather session at SC'25 last month, but hopefully in a more broadly accessible location. The slides are in the publication archive as usual, as well as the other recent presentations from SC'25 and KubeCon NA 2025: https://www.schedmd.com/publications/ The direct link to the release sides is: http://slurm.schedmd.com/SC25/Slurm_BoF_SC25.pdf - Tim -- Tim Wickberg Chief Technology Officer, SchedMD LLC Commercial Slurm Development and Support

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GPU usage in fair share factor
by Massimo Sgaravatto 12 Dec '25

12 Dec '25

Dear all I have these settings in my slurm.conf: PriorityType=priority/multifactor PriorityDecayHalfLife=7-0 PriorityFavorSmall=NO PriorityMaxAge=10-0 PriorityWeightAge=100000 PriorityWeightFairshare=1000000 PriorityWeightJobSize=100000 PriorityWeightPartition=100000 PriorityWeightQOS=100000 PriorityFlags=ACCRUE_ALWAYS,FAIR_TREE AccountingStorageTRES=gres/gpu,gres/gpu:nvidia-h100,gres/gpu:nvidia-l40s If I have got it right, with these settings, when calculating the Job_priority (I am referring to the formula in https://slurm.schedmd.com/priority_multifactor.html) the fair-share_factor is calculated only considering cores*seconds. So if a job also used some GPUs, this is not taken into account. Am I right ? If I want to take memory and GPUs into account as well (with different weights for different GPU models), my understanding is that I should use the TRESBillingWeights attribute, setting e.g: TRESBillingWeights="CPU=1.0,Mem=0.25G,gres/gpu=nvidia-h100=10.0,gres/gpu=nvidia-l40s=5.0" Is this correct ? Thanks, Massimo

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Problems with salloc
by Gestió Servidors 10 Dec '25

10 Dec '25

Hello, I am getting some problems with "salloc" execution. My cluster is running SLURM v23.11.0. I'm going to explain problems: 1. My first problem is that until some previous version (I can't remember what version exactly), when I ran a "salloc", I got a console inside the assigned host. Then, I could execute whatever I wanted. However, now, after execute "salloc" (without forcing host), SLURM always tries to assign me the last host where a job has been executed. But if that host is now powered off, salloc waits and waits but not tries to connect to other host 2. Second problem is that previous execution gives me a host (the host is powered on), my session continues in the allocation host, not in the assigned host. So if I want to execute some program, I need to run with "srun". Is there anyway to go directly to the assigned host? 3. Third problem is a question. How can I limit the hosts where I (as cluster admin) allow to run a "salloc"? For example, I would like that when a user runs a "salloc", it were execute always in a partition with "x" hosts, avoiding SLURM to assign me a host of the list of hosts. Thanks.

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Invalid generic resource (gres) specification after RMA
by Lee 04 Dec '25

04 Dec '25

Good afternoon, I have a cluster that is managed by Base Command Manager (v10) and it has several Nvidia DGXs. dgx09 is a problem child. The entire node was RMA'd and now it no longer behaves the same as my other DGXs. I think the below symptoms are caused by a single underlying issue. *Symptoms : * 1. When I look at our 8 non-MIG DGXs, via `scontrol show node=dgxXY | grep Gres`, 7/8 DGXs report "Gres=gpu:*H100*:8(S:0-1)" while dgx09 reports " Gres=gpu:*h100*:8(S:0-1)" 2. When I submit a job to this node, I get : $ srun --reservation=g09_test --gres=gpu:2 --pty bash srun: error: Unable to create step for job 105035: Invalid generic resource (gres) specification ### No job is running on the node, yet AllocTRES shows consumed resources... $ scontrol show node=dgx09 | grep -i AllocTRES *AllocTRES=gres/gpu=2* ### dgx09 : /var/log/slurmd contains no information ### slurmctld shows : root@h01:# grep 105035 /var/log/slurmctld [2025-11-13T07:44:56.380] sched: _slurm_rpc_allocate_resources JobId=105035 NodeList=dgx09 usec=3420 [2025-11-13T07:44:56.482] _job_complete: JobId=105035 WTERMSIG 1 [2025-11-13T07:44:56.483] _job_complete: JobId=105035 done *Configuration : * 1. gres.conf : # This section of this file was automatically generated by cmd. Do not edit manually! # BEGIN AUTOGENERATED SECTION -- DO NOT REMOVE AutoDetect=NVML NodeName=dgx[01,02] Name=gpu Type=1g.20gb Count=32 AutoDetect=NVML NodeName=dgx[03-10] Name=gpu Type=h100 Count=8 AutoDetect=NVML # END AUTOGENERATED SECTION -- DO NOT REMOVE 2. grep NodeName slurm.conf root@h01:# grep NodeName slurm.conf NodeName=dgx[01,02] RealMemory=2063937 Boards=1 SocketsPerBoard=2 CoresPerSocket=56 ThreadsPerCore=2 MemSpecLimit=30017 Gres=gpu:1g.20gb:32 Feature=location=local NodeName=dgx03-10] RealMemory=2063937 Boards=1 SocketsPerBoard=2 CoresPerSocket=56 ThreadsPerCore=2 MemSpecLimit=30017 Gres=gpu:H100:8 Feature=location=local 3. What slurmd detects on dgx09 root@dgx09:~# slurmd -C NodeName=dgx09 CPUs=224 Boards=1 SocketsPerBoard=2 CoresPerSocket=56 ThreadsPerCore=2 RealMemory=2063937 UpTime=8-00:39:10 root@dgx09:~# slurmd -G slurmd: gpu/nvml: _get_system_gpu_list_nvml: 8 GPU system device(s) detected slurmd: Gres Name=gpu Type=h100 Count=1 Index=0 ID=7696487 File=/dev/nvidia0 Cores=0-55 CoreCnt=224 Links=-1,0,0,0,0,0,0,0 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=h100 Count=1 Index=1 ID=7696487 File=/dev/nvidia1 Cores=0-55 CoreCnt=224 Links=0,-1,0,0,0,0,0,0 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=h100 Count=1 Index=2 ID=7696487 File=/dev/nvidia2 Cores=0-55 CoreCnt=224 Links=0,0,-1,0,0,0,0,0 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=h100 Count=1 Index=3 ID=7696487 File=/dev/nvidia3 Cores=0-55 CoreCnt=224 Links=0,0,0,-1,0,0,0,0 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=h100 Count=1 Index=4 ID=7696487 File=/dev/nvidia4 Cores=56-111 CoreCnt=224 Links=0,0,0,0,-1,0,0,0 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=h100 Count=1 Index=5 ID=7696487 File=/dev/nvidia5 Cores=56-111 CoreCnt=224 Links=0,0,0,0,0,-1,0,0 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=h100 Count=1 Index=6 ID=7696487 File=/dev/nvidia6 Cores=56-111 CoreCnt=224 Links=0,0,0,0,0,0,-1,0 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=h100 Count=1 Index=7 ID=7696487 File=/dev/nvidia7 Cores=56-111 CoreCnt=224 Links=0,0,0,0,0,0,0,-1 Flags=HAS_FILE,HAS_TYPE,ENV_NVML *Questions : * 1. As far as I can tell, dgx09 is identical to all my non-MIG DGX nodes in terms of configuration and hardware. Why does scontrol report it having 'h100' with a lower case 'h' unlike the other dgxs which report with an upper case 'H'? 2. Why is dgx09 not accepting GPU jobs and afterwards it artificially thinks that there are GPUs allocated even though no jobs are on the node? 3. Are there additional tests / configurations that I can do to probe the differences between dgx09 and all my other nodes? Best regards, Lee

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Doubts Regarding Definition of Metrics AveRSS and AveDiskWrite
by Manuel G. Marciani 03 Dec '25

03 Dec '25

Hello, fellow users: I have been using Slurm for the past three years, but recently I bumped into a doubt. I am using Slurm's (version 23.02.7) collected metrics (jobacct_gather/linux) to do a performance analysis of an application. I have read the documentation regarding the metrics (https://slurm.schedmd.com/sacct.html) but still find the Ave* metrics confusing, and more specifically the AveRSS and AveDiskWrite. AveDiskWrite is defined as "Average number of bytes written by all tasks in job." So, if I double the workload, which say that it had x avediskwrite, I should observe 2x. So far it is what I observed. But, then, if I double the resources while maintaining the workload I observe again x, and not 2x. So my suspicion is that the metric is the sum of written bytes across time, then divided by the number of nodes. But then with AveRSS, defined as "Average resident set size of all tasks in job," I observe what I expected with AveDiskWrite. That is, that this metric scales with the workload irrespective of the resources it has available. So I am not sure what the "Ave" references here. I would be thankful if someone could clarify the behavior, and even more grateful if someone could point me where in the code these metrics are aggregated and processed to be stored in the database. Many thanks, Manu. -- Manuel G. Marciani - マルシアニ·マヌエル First Stage Reseacher at Computational Earth Sciences (CES) - Earth Sciences Department Barcelona Supercomputing Center - Centro Nacional de Supercomputación Ph.D Student at Departament d'Arquitectura de Computadors (DAC) - Facultat d'Informàtica de Barcelona (FIB) Universitat Politècnica de Catalunya (UPC) BSC building Plaça Eusebi Güell, 1-3, 08034 Barcelona, Spain Desk BSC-PL0-6-20/8 mail to: manuel.gimenez(a)bsc.es mail to: manuel.gimenez.de.castro(a)upc.edu mail to: manuel.marciani(a)a.riken.jp mail to: manuel.gimenez.de.castro.marciani(a)hu-berlin.de

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MIG and eBPF issues (Slurm 24.11.6)
by wk5ng＠uwaterloo.ca 28 Nov '25

28 Nov '25

Hi all, I'm having some trouble getting Slurm 24.11.6 to work with MIG, and the slurmd logs seem to point to an issue with eBPF. For some context, this is an LXD unprivileged container where I'm trying to get MIG to work with Slurm. Other compute nodes without MIG work fine and isolate the GPUs accordingly. What I'm seeing in slurmd logs: [2025-11-24T23:32:50.197] [331.interactive] cgroup/v2: cgroup_p_constrain_apply: CGROUP: EBPF Closing and loading bpf program into /sys/fs/cgroup/system.slice/slurmstepd.scope/job_331 [2025-11-24T23:32:50.197] [331.interactive] error: load_ebpf_prog: BPF load error (Operation not permitted). Please check your system limits (MEMLOCK). I've tried increasing the system limits for MEMLOCK by setting DefaultLimitMEMLOCK=infinity in /etc/systemd/system.conf, and I've copied my slurmd.service file below where I've set Delegate=yes and LimitMEMLOCK=infinity. Previously only Delegate=yes wasn't set (I had rifled through the cgroupv2 documentaton for Slurm and found that setting), but in both cases I see the same BPF load error. Just wondering if this was something that other people had come across before and maybe I'm doing something silly here. I've checked that my slurm.conf has the corresponding parameters set according to Slurm's own documentation for cgroup.conf and my cgroup.conf is also copied below. Some portion of the gres.conf is also copied below, and even though I tried AutoDetect=nvml for this node, it's still doesn't work, which was why I changed to manually setting it based off the output of slurmd -G. Maybe I should try switching back to cgroupv1 and see if that helps fix things, but I'm not sure at this point if MIG and Slurm are compatible using cgroupv1. I can send other parts of logs, configuration files etc. Any help would be greatly appreciated! ###### slurmd.service file [Unit] Description=Slurm node daemon After=network.target munge.service ConditionPathExists=/etc/slurm/slurm.conf [Service] Type=forking EnvironmentFile=-/etc/sysconfig/slurmd ExecStart=/usr/sbin/slurmd -d /usr/sbin/slurmstepd $SLURMD_OPTIONS ExecReload=/bin/kill -HUP $MAINPID PIDFile=/var/run/slurmd.pid KillMode=process LimitNOFILE=51200 Delegate=yes LimitMEMLOCK=infinity LimitSTACK=infinity [Install] WantedBy=multi-user.target ###### cgroup.conf CgroupPlugin=autodetect ConstrainCores=yes ConstrainDevices=yes ConstrainRAMSpace=yes ##### gres.conf NodeName=gpu-3 AutoDetect=nvml Name=gpu NodeName=gpu-4 Name=gpu MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap30,/dev/nvidia-caps/nvidia-cap31

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Slinky version 1.0.0 is now available
by Marrlow Warnicke 20 Nov '25

20 Nov '25

We are pleased to announce the availability of Slinky version 1.0.0! Slinky is SchedMD's set of components to integrate Slurm in Kubernetes environments. Slinky consists of two main projects, slurm-operator and slurm-bridge. Our landing page is here: https://www.slinky.ai The slurm-operator handles cases where users wish to run Slurm jobs within the Kubernetes cluster. Release v1.0.0: https://github.com/SlinkyProject/slurm-operator/tree/release-1.0 New features include hybrid support, integration points for external tooling, and workload protection and isolation. The full changelog may be found here: https://github.com/SlinkyProject/slurm-operator/blob/main/CHANGELOG/CHANGEL… The slurm-bridge handles the cases where you want Slurm scheduling on your cluster and to be able to run either Kubernetes or Slurm jobs. Release v1.0.0: https://github.com/SlinkyProject/slurm-bridge/tree/release-1.0 New features include support for DRA Extended Resources, support for TaintToleration and VolumeBinding plugins, and integration of new Slurm 25.11 support for granular node resource allocation assignments with GRES. The full changelog may be found here: https://github.com/SlinkyProject/slurm-bridge/blob/main/CHANGELOG/CHANGELOG… The SlinkyProject registry now has containers that support both amd64 (x86_64) and arm64 (aarch64) architectures. You may find these here: https://github.com/orgs/SlinkyProject/packages Apologies for the redundant email-we needed to clarify the release version. -- Marlow Warnicke Principal Cloud Engineer Commercial Slurm - and Slinky - Development and Support

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Slinky version 1.0.0 is now available
by Marrlow Warnicke 20 Nov '25

20 Nov '25

We are pleased to announce the availability of Slinky version 1.0.0-rc1. Slinky is SchedMD’s set of components to integrate Slurm in Kubernetes environments. Slinky consists of two main projects, slurm-operator and slurm-bridge. Our landing page is here: https://www.slinky.ai The slurm-operator handles cases where users wish to run Slurm jobs within the Kubernetes cluster. Release v1.0.0-rc1: https://github.com/SlinkyProject/slurm-operator/tree/release-1.0 New features include hybrid support, integration points for external tooling, and workload protection and isolation. The full changelog may be found here: https://github.com/SlinkyProject/slurm-operator/blob/main/CHANGELOG/CHANGEL… The slurm-bridge handles the cases where you want Slurm scheduling on your cluster and to be able to run either Kubernetes or Slurm jobs. Release v1.0.0-rc1: https://github.com/SlinkyProject/slurm-bridge/tree/release-1.0 New features include support for DRA Extended Resources, support for TaintToleration and VolumeBinding plugins, and integration of new Slurm 25.11 support for granular node resource allocation assignments with GRES. The full changelog may be found here: https://github.com/SlinkyProject/slurm-bridge/blob/main/CHANGELOG/CHANGELOG… The SlinkyProject registry now has containers that support both amd64 (x86_64) and arm64 (aarch64) architectures. You may find these here: https://github.com/orgs/SlinkyProject/packages -- Marlow Warnicke Principal Cloud Engineer, SchedMD LLC Commercial Slurm - and Slinky - Development and Support

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Recommended Stable Slurm Version for >100P Scale Clusters
by KK 18 Nov '25

18 Nov '25

We are currently planning to deploy a new HPC system with a total compute capacity exceeding 100 PF. As part of our preparation, we would like to understand which Slurm versions are considered stable and widely used at this scale. Could you please share your recommendations or experience regarding: 1. Which Slurm version is currently running reliably on very large-scale clusters (>100 PF or >10k nodes)? 2. Whether there are any versions we should avoid due to known issues at large scale. 3. Any best practices or configuration considerations for Slurm deployments of this size.

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2026

2025

2024

slurm-users