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'sacct -a' missing running jobs 'held' by user
by Lee 08 Jan '26

08 Jan '26

Hello, I am using slurm 23.02.6. I have a strange issue. I periodically use sacct to dump job data. I then generate reports based on the resource allocation of our users. Recently, I noticed some 'missing' jobs from my query. The missing jobs came from a user who had a large array job, who then 'held' all of the array jobs. This included 'holding' the Running array jobs. Now, if I run `sacct -a -S YYYY-MM-DD --format="jobidraw,jobname"`, the job will be missing from that query. However, if I query specifically for that job, i.e. `sacct -j RAWJOBID -S YYYY-MM-DD --format="jobidraw,jobname", the job is present. *Question* : 1. How can I include the 'held' running job when I do my bulk query with `sacct -a`? Finding these outliers and adding them ad-hoc to my dumped file is too laborious and isn't feasible. *Minimum working example *: #. Submit a job : myuser@clusterb01:~$ srun --pty bash # landed on dgx29 #. Hold job myuser@clusterb01:~$ scontrol hold 120918 myuser@clusterb01:~$ scontrol show job=120918 JobId=120918 JobName=bash UserId=myuser(123456) GroupId=rdusers(7000) MCS_label=N/A Priority=0 Nice=0 Account=allusers QOS=normal JobState=*RUNNING* Reason=*JobHeldUser* Dependency=(null) Requeue=0 Restarts=0 BatchFlag=0 Reboot=0 ExitCode=0:0 RunTime=00:00:29 TimeLimit=7-00:00:00 TimeMin=N/A SubmitTime=2025-12-15T13:31:28 EligibleTime=Unknown AccrueTime=Unknown StartTime=2025-12-15T13:31:28 EndTime=2025-12-22T13:31:28 Deadline=N/A SuspendTime=None SecsPreSuspend=0 LastSchedEval=2025-12-15T13:31:28 Scheduler=Main Partition=defq AllocNode:Sid=clusterb01:4145861 ReqNodeList=(null) ExcNodeList=(null) NodeList=dgx29 BatchHost=dgx29 NumNodes=1 NumCPUs=2 NumTasks=1 CPUs/Task=1 ReqB:S:C:T=0:0:*:* ReqTRES=cpu=1,mem=9070M,node=1,billing=1 AllocTRES=cpu=2,mem=18140M,node=1,billing=2 Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=* MinCPUsNode=1 MinMemoryCPU=9070M MinTmpDiskNode=0 Features=(null) DelayBoot=00:00:00 OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null) Command=bash WorkDir=/home/myuser Power= #. Release job myuser@clusterb01:~$ scontrol release 120918 #. Show job again myuser@clusterb01:~$ scontrol show job=120918 JobId=120918 JobName=bash UserId=myuser(123456) GroupId=rdusers(7000) MCS_label=N/A Priority=1741 Nice=0 Account=allusers QOS=normal JobState=*RUNNING* Reason=*None* Dependency=(null) Requeue=0 Restarts=0 BatchFlag=0 Reboot=0 ExitCode=0:0 RunTime=00:01:39 TimeLimit=7-00:00:00 TimeMin=N/A SubmitTime=2025-12-15T13:31:28 EligibleTime=Unknown AccrueTime=Unknown StartTime=2025-12-15T13:31:28 EndTime=2025-12-22T13:31:28 Deadline=N/A SuspendTime=None SecsPreSuspend=0 LastSchedEval=2025-12-15T13:31:28 Scheduler=Main Partition=defq AllocNode:Sid=clusterb01:4145861 ReqNodeList=(null) ExcNodeList=(null) NodeList=dgx29 BatchHost=dgx29 NumNodes=1 NumCPUs=2 NumTasks=1 CPUs/Task=1 ReqB:S:C:T=0:0:*:* ReqTRES=cpu=1,mem=9070M,node=1,billing=1 AllocTRES=cpu=2,mem=18140M,node=1,billing=2 Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=* MinCPUsNode=1 MinMemoryCPU=9070M MinTmpDiskNode=0 Features=(null) DelayBoot=00:00:00 OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null) Command=bash WorkDir=/home/myuser/ Power= #. In slurmctld, I see : root@clusterb01:~# grep 120918 /var/log/slurmctld [2025-12-15T13:31:28.706] sched: _slurm_rpc_allocate_resources JobId=120918 NodeList=dgx29 usec=1269 [2025-12-15T13:31:47.751] sched: _hold_job_rec: hold on JobId=120918 by uid 123456 [2025-12-15T13:31:47.751] sched: _update_job: set priority to 0 for JobId=120918 [2025-12-15T13:31:47.751] _slurm_rpc_update_job: complete JobId=120918 uid=123456 usec=189 [2025-12-15T13:32:48.081] sched: _release_job_rec: release hold on JobId=120918 by uid 123456 [2025-12-15T13:32:48.081] _slurm_rpc_update_job: complete JobId=120918 uid=123456 usec=268 [2025-12-15T13:33:20.552] _job_complete: JobId=120918 WEXITSTATUS 0 [2025-12-15T13:33:20.552] _job_complete: JobId=120918 done #. Job is NOT missing, when identifying it by jobid myuser@clusterb01:~$ sacct -j 120918 --starttime=2025-12-12 -o "jobidraw,jobid,node,start,end,elapsed,state,submitline%30" JobIDRaw JobID NodeList Start End Elapsed State SubmitLine ------------ ------------ --------------- ------------------- ------------------- ---------- ---------- ------------------------------ 120918 120918 dgx29 2025-12-15T13:31:28 2025-12-15T13:33:20 00:01:52 COMPLETED srun --pty bash 120918.0 120918.0 dgx29 2025-12-15T13:31:28 2025-12-15T13:33:20 00:01:52 COMPLETED srun --pty bash #. Job IS *missing* when using -a myuser@clusterb01:~$ sacct -a --starttime=2025-12-12 -o "jobidraw,jobid,node,start,end,elapsed,state,submitline%30" | grep -i 120918 ## *MISSING* Best regards, Lee

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New, faster, simpler makefiles.
by Alex Tingle 08 Jan '26

08 Jan '26

Hello, I've written a set of replacement makefiles for Slurm, which are faster, and simpler than Automake. Faster incremental builds make the code much more pleasant to work with. Try it out here: https://github.com/alex-smartptr/slurm Instructions: 1. Run `./configure` as usual (all settings work as before). 2. `make -s` You can find a full README here: https://github.com/alex-smartptr/slurm/blob/newmake/mk/README.md I'm looking for feedback, especially problems that I've not foreseen, but also suggestions for improvements. -Alex Tingle --

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Announcing slurm-quota: CPU and GPU time quotas for Slurm
by Rémi Palancher 08 Jan '26

08 Jan '26

Hello Slurm users, I would like to announce slurm-quota, an open-source tool providing time-based CPU and GPU quota accounting and enforcement for Slurm users and accounts. Slurm does not currently provide native enforcement of consumption-based quotas (e.g. CPU or GPU minutes). slurm-quota addresses this by integrating with Slurm job submission and job completion mechanisms. Key features include: - Definition and enforcement of CPU and GPU time quotas at user and account levels - Support for heterogeneous GPU clusters using billing factors - Clear command-line reporting of usage, remaining quota, with visual progress bars - Lightweight design with minimal dependencies (Python, SQLite and few common Lua libraries) slurm-quota is released under the MIT license and is available on GitHub, along with documentation describing architecture, deployment, and integration details. GitHub Project: https://github.com/rackslab/slurm-quota Comments, questions, and feedback are very welcome! -- Rémi Palancher Rackslab: Open Source Solutions for HPC Operations https://rackslab.io

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pam_slurm_adopt malfunct after slurm upgrade to 25.11
by taleintervenor＠sjtu.edu.cn 17 Dec '25

17 Dec '25

Hello everyone: We recently upgrade slurm to 25.11 and find the pam_slurm_adopt got broken. This cause the users cannot ssh to the compute node where their jobs is running on. Of course we have make sure all the slurm related packages have been upgraded together. Simplest test process is like: ``` > cat test1.sh #!/bin/bash #SBATCH --job-name=test #SBATCH --partition=debug #SBATCH --nodes=1 #SBATCH --nodelist=cas639 sleep 6000 > sbatch test1.sh Submitted batch job 51047091 > squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 51047091 debug test tester R 0:28 1 cas639 > ssh cas639 (wait for a long time...) Connection closed by 172.16.3.129 port 22 (finally failed to ssh) ``` on the target compute node, we can see the following debug message from pam_slurm_adopt.so: ``` cas639 pam_slurm_adopt[1007301]: debug2: _establish_config_source: using config_file=/etc/slurm/slurm.conf (default) cas639 pam_slurm_adopt[1007301]: debug: slurm_conf_init: using config_file=/etc/slurm/slurm.conf cas639 pam_slurm_adopt[1007301]: debug: Reading slurm.conf file: /etc/slurm/slurm.conf cas639 pam_slurm_adopt[1007301]: PreemptMode=GANG is a cluster-wide option and cannot be set at partition level, option ignored. cas639 pam_slurm_adopt[1007301]: debug3: Trying to load plugin /usr/lib64/slurm/auth_munge.so cas639 pam_slurm_adopt[1007301]: debug3: plugin_load_from_file->_verify_syms: found Slurm plugin name:Munge authentication plugin type:auth/munge version:0x190b00 cas639 pam_slurm_adopt[1007301]: debug: auth/munge: init: loaded cas639 pam_slurm_adopt[1007301]: debug3: Success. cas639 pam_slurm_adopt[1007301]: debug3: Trying to load plugin /usr/lib64/slurm/certgen_script.so cas639 pam_slurm_adopt[1007301]: debug3: plugin_load_from_file->_verify_syms: found Slurm plugin name:Certificate generation script plugin type:certgen/script version:0x190b00 cas639 pam_slurm_adopt[1007301]: debug: certgen/script: init: loaded cas639 pam_slurm_adopt[1007301]: debug3: Success. cas639 pam_slurm_adopt[1007301]: debug3: Trying to load plugin /usr/lib64/slurm/hash_k12.so cas639 pam_slurm_adopt[1007301]: debug3: plugin_load_from_file->_verify_syms: found Slurm plugin name:KangarooTwelve hash plugin type:hash/k12 version:0x190b00 cas639 pam_slurm_adopt[1007301]: debug: hash/k12: init: init: KangarooTwelve hash plugin loaded cas639 pam_slurm_adopt[1007301]: debug3: Success. cas639 pam_slurm_adopt[1007301]: debug3: Trying to load plugin /usr/lib64/slurm/tls_none.so cas639 pam_slurm_adopt[1007301]: debug3: plugin_load_from_file->_verify_syms: found Slurm plugin name:Null tls plugin type:tls/none version:0x190b00 cas639 pam_slurm_adopt[1007301]: debug: tls/none: init: tls/none loaded cas639 pam_slurm_adopt[1007301]: debug3: Success. cas639 pam_slurm_adopt[1007301]: debug3: Trying to load plugin /usr/lib64/slurm/accounting_storage_slurmdbd.so cas639 pam_slurm_adopt[1007301]: debug3: plugin_load_from_file->_verify_syms: found Slurm plugin name:Accounting storage SLURMDBD plugin type:accounting_storage/slurmdbd version:0x190b00 cas639 pam_slurm_adopt[1007301]: accounting_storage/slurmdbd: init: Accounting storage SLURMDBD plugin loaded cas639 pam_slurm_adopt[1007301]: debug3: Success. cas639 pam_slurm_adopt[1007301]: debug3: Trying to load plugin /usr/lib64/slurm/cred_munge.so cas639 pam_slurm_adopt[1007301]: debug3: plugin_load_from_file->_verify_syms: found Slurm plugin name:Munge credential signature plugin type:cred/munge version:0x190b00 cas639 pam_slurm_adopt[1007301]: cred/munge: init: Munge credential signature plugin loaded cas639 pam_slurm_adopt[1007301]: debug3: Success. cas639 pam_slurm_adopt[1007301]: debug: Reading cgroup.conf file /etc/slurm/cgroup.conf cas639 pam_slurm_adopt[1007301]: debug4: found StepId=51047091.extern cas639 pam_slurm_adopt[1007301]: debug4: found StepId=51047091.batch cas639 pam_slurm_adopt[1007301]: Connection by user tester: user has only one job 51047091 cas639 pam_slurm_adopt[1007301]: debug: _adopt_process: trying to get StepId=51047091.extern to adopt 1007301 cas639 pam_slurm_adopt[1007301]: debug: Leaving stepd_add_extern_pid cas639 pam_slurm_adopt[1007301]: debug: Leaving stepd_get_x11_display cas639 pam_slurm_adopt[1007301]: debug: entering stepd_get_namespace_fd ``` It looks like something block during stepd_get_namespace_fd? And we found nothing in slurmd log even with SlurmdDebug = debug5, so I guess the pam module had not run to the step to talk with slurmd (if it should). Would it be a compatibility problem between slurm25.11 and EL8 system or cgroup/v1? Or can anyone help to give some suggestion on how to further locate the fault point? Best regards, Hermes

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Slurm version 25.11.1 is now available
by Marshall Garey 16 Dec '25

16 Dec '25

We are pleased to announce the availability of Slurm version 25.11.1. This release fixes two critical bugs when upgrading which caused older jobs to be aborted and slurmstepd to crash, a regression in 25.11 that broke pam_slurm_adopt, some stability issues in slurmctld, and various other minor to moderate bugs. The full list of changes are available in the CHANGELOG file: https://github.com/SchedMD/slurm/blob/slurm-25.11/CHANGELOG/slurm-25.11.md Slurm can be downloaded from: https://www.schedmd.com/download-slurm/ -- Marshall Garey Release Management, Support, and Development SchedMD LLC - Commercial Slurm Development and Support

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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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