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strange GPU "no resources selected" error
by Paul Raines 23 Jul '25

23 Jul '25

Recently (past 3 days) on our NVIDIA DGX A100 systems running Ubuntu 22.04.5 and slurm 24.11.5 we have had jobs that ask for a gpu, get started by Slurm, but fail to be given a GPU and then fail. In the slurmctld log we see a line like: [2025-07-22T02:46:29.697] error: gres/gpu: job 6919154 node A100-04 no resources selected on the slurmd log I see no errors for the job but there is a line like [2025-07-22T02:46:29.757] [6919154.extern] task/cgroup: _handle_device_access: GRES: job devices.deny: adding c 195:0 rwm(/dev/nvidia0) for all 8 of the GPUs on the node. Other jobs still seem to start up and get a GPU fine. If you look at the job stats one sees: ReqTRES : billing=7,cpu=1,gres/gpu=1,mem=96G,node=1 AllocTRES : billing=3,cpu=1,mem=96G,node=1 showing that even though the gpu was requested, it was not allocated. Occasionly on these boxes (and only these -- my Dell Rocky 8 boxes with GPUS have no problem) we see the nodes go into drain mode with the "res/gpu GRES core specification ... doesn't match socket boundaries." message as per https://support.schedmd.com/show_bug.cgi?id=22498 It does seem to happen after slurmctld restart. I then restart slurmd on the nodes and can resume SLURM on the nodes whenever that happens. Otherwise nothing has changed on these nodes with SLURM config or the OS in over a month. Definition of the nodes are NodeName=A100-[01-04] \ CPUs=128 Boards=1 SocketsPerBoard=2 CoresPerSocket=64 \ ThreadsPerCore=1 RealMemory=1031000 MemSpecLimit=2048 \ TmpDisk=1400000 Feature=amd,epyc,a100 \ Gres=gpu:a100-sxm4-40gb:8 and gres.conf on the nodes is simply AutoDetect=nvml --------------------------------------------------------------- Paul Raines http://help.nmr.mgh.harvard.edu MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging 149 (2301) 13th Street Charlestown, MA 02129 USA The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Mass General Brigham Compliance HelpLine at https://www.massgeneralbrigham.org/complianceline <https://www.massgeneralbrigham.org/complianceline> . Please note that this e-mail is not secure (encrypted). If you do not wish to continue communication over unencrypted e-mail, please notify the sender of this message immediately. Continuing to send or respond to e-mail after receiving this message means you understand and accept this risk and wish to continue to communicate over unencrypted e-mail.

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slurm 25.05.0 : allocates less GPUs than requested by --gres=gpu:count
by Jan Gmys 23 Jul '25

23 Jul '25

Since upgrading slurm to 25.05.0 (22.05.9 -> 23.11.11 -> 25.05.0) some jobs requesting --gres=gpu:reqcount GPUs are allocated less than reqcount GPUs if some of the node's GPUs are already in use by other jobs. We have a node - let's call it ares-c02-06 - with 2 GPUs. Consider the following test script: > #!/bin/bash > #SBATCH --nodes=1 > #SBATCH --ntasks-per-node=1 > #SBATCH --time=1-00:00:00 > > echo "CUDA_VISIBLE_DEVICES: " $CUDA_VISIBLE_DEVICES > echo "SLURM_JOB_GPUS: " $SLURM_JOB_GPUS > echo "SLURM_GPUS_ON_NODE: " $SLURM_GPUS_ON_NODE > sleep 10d Submit a job to the node: > > sbatch *--gres=gpu:1* --nodelist=ares-c02-06 job.sh > > Submitted batch job 1950559 > The job starts. Now submit the script again, asking for 2 GPUs > sbatch*--gres=gpu:2* --nodelist=ares-c02-06 job.sh > > Submitted batch job 1950567 This second job should not start as the ressources are not available. Surprisingly, _both jobs are running_ > $ squeue -w ares-c02-06 > JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) > 1950567 gpuai job.sh jan.gmys R 1:55 1 ares-c02-06 > 1950559 gpuai job.sh jan.gmys R 2:58 1 ares-c02-06 In the second job - 1950567 - AllocTRES shows gres/gpu=1, instead of the requested gres/gpu=2. > # sacct -j 1950567,1950559 -X -o jobid%10,reqtres%45,alloctres%60 > JobID ReqTRES AllocTRES > ---------- --------------------------------------------- ------------------------------------------------------------ > 1950559 billing=1,cpu=1,gres/gpu=1,mem=4G,node=1 billing=1,cpu=1,gres/gpu:l40s=1,gres/gpu=1,mem=4G,node=1 > 1950567 billing=1,cpu=1,*gres/gpu=2*,mem=4G,node=1 billing=1,cpu=1,gres/gpu:l40s=1,*gres/gpu=1*,mem=4G,node=1 The output of both jobs > $ cat slurm-1950559.out > CUDA_VISIBLE_DEVICES: 0 > SLURM_JOB_GPUS: 0 > SLURM_GPUS_ON_NODE: 1 > > $ cat slurm-1950567.out > CUDA_VISIBLE_DEVICES: 0 > SLURM_JOB_GPUS: 1 > SLURM_GPUS_ON_NODE: 1 CUDA_VISIBLE_DEVICES is set to 0 for both jobs. SLURM_JOB_GPUS is 0 resp. 1. *Environment: * - RHEL 9.1 - slurm 25.05.0 - The GRES configuration seems fine, AutoDetect is off : > # /usr/sbin/slurmd -G --conf-server hpc-slurm.cluster.hpc -v > [2025-07-22T16:44:05.548] GRES: Global*AutoDetect=off*(4) > [2025-07-22T16:44:05.548] GRES: _set_gres_device_desc : /dev/nvidia0 major 195, > minor 0 > [2025-07-22T16:44:05.548] GRES: _set_gres_device_desc : /dev/nvidia1 major 195, > minor 1 > [2025-07-22T16:44:05.548] GRES: gpu device number 0(/dev/nvidia0):c 195:0 rwm > [2025-07-22T16:44:05.548] GRES: gpu device number 1(/dev/nvidia1):c 195:1 rwm > [2025-07-22T16:44:05.548] Gres Name=gpu Type=L40S Count=2 Index=0 ID=7696487 > File=/dev/nvidia[0-1] Links=(null) > Flags=HAS_FILE,HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT and 'scontrol show node' > NodeName=ares-c02-06 Arch=x86_64 CoresPerSocket=24 > CPUAlloc=0 CPUEfctv=48 CPUTot=48 CPULoad=0.00 > AvailableFeatures=(null) > ActiveFeatures=(null) > Gres=gpu:L40S:2 > NodeAddr=ares-c02-06 NodeHostName=ares-c02-06 Version=25.05.0 > OS=Linux 5.14.0-162.6.1.el9_1.x86_64 #1 SMP PREEMPT_DYNAMIC Fri Sep 30 > 07:36:03 EDT 2022 > RealMemory=386000 AllocMem=0 FreeMem=363069 Sockets=2 Boards=1 > State=IDLE+RESERVED+PLANNED ThreadsPerCore=1 TmpDisk=0 Weight=1 Owner=N/A > MCS_label=N/A > Partitions=gpuai > BootTime=2025-07-04T10:26:27 SlurmdStartTime=2025-07-22T14:48:54 > LastBusyTime=2025-07-22T16:26:36 ResumeAfterTime=None > CfgTRES=cpu=48,mem=386000M,billing=48,gres/gpu=2 > AllocTRES= *Debug notes :* - When using the --gpus option instead of --gres everything works as expected : the second job is PENDING (Ressources) - Tried both, ConstrainDevices=on/off in cgroup.conf, same result - The same is happening on other multi-GPU nodes of the cluster - When the --gres=gpu:2 job is submitted first, i.e. when all GPUs are taken, the second (--gres=gpu:1) job waits correctly. - When both GPUs are free, the --gres=gpu:2 job, correctly gets both GPUs : CUDA_VISIBLE_DEVICES: 0,1 - It worked in slurm 22.05.9 (we recently upgraded in two steps -> 23.11.11 -> 25.05.0) - The only viable workaround I see for the moment is to intercept --gres and --gpus-per-node (don't even have it in job_desc I think!! :-/) options in job_submit.lua and force users to use the --gpus option, which seems to works fine. Anyone experienced similar issues? Any idea how to solve this would be highly appreciated. Jan == -- Jan Gmys Ingénieur de recherche Support HPC/IA pour la plateforme MesoNET Mésocentre de Calcul Scientifique Intensif de l'Université de Lille

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Reserve memory for OS
by John Hearns 17 Jul '25

17 Jul '25

Someone remind me please what is the SLurm node parameter to reserve an amount of memory for the OS? Thanks

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Integrating Fabric Modules with SLURM – Best Practices for HPC Networking?
by xoyeyor178＠coderdir.com 15 Jul '25

15 Jul '25

Our team is exploring ways to optimize our HPC cluster’s network performance, particularly for multi-node SLURM workloads. We’re considering Network Devices Expansion Modules Fabric Modules https://serverorbit.com/network-devices/network-expansion-modules/fabric-mo… to enhance scalability and reduce latency between compute nodes. Has anyone successfully deployed Fabric Modules (e.g., Cisco Nexus, Arista, or Mellanox solutions) in a SLURM environment? Specifically: Interconnect Strategies – Any tips for configuring Fabric Modules to handle SLURM’s bursty traffic patterns? Performance Gains – Measurable improvements in job throughput or MPI communication? Troubleshooting – Known conflicts with SLURM’s network topology detection?

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Slurm-wlm depends on X11?
by Jesse Hayward 10 Jul '25

10 Jul '25

Hello group, hopefully a quick one: I am trying to install the suite of standard slurm packages (slurm, munge, slurmdbd, slurm-wlm-basic-plugins) on a compute node. Ubuntu 22.04.05 LTS just upgraded. *However, these packages now bring down x11 (libx11-6 and all of that other garbage in Ubuntu land). * Does slurm actually depend on x11 now? Or do I need to take a look at my apt config and see what's going on over there, instead. Thanks! Jess -- Jesse Hayward Systems Administrator for High Performance Computing Vassar College 845.437.7521 CIS 207

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Is DefCpuPerGPU affected by --ntasks-per-gpu?
by Milad Alizadeh 10 Jul '25

10 Jul '25

I have defined DefCpuPerGPU for a partition but when a job specifies --ntasks-per-gpu then DefCpuPerGPU is ignored and I only get 1 cpu: ``` #SBATCH --gpus=1 #SBATCH --ntasks-per-gpu=1 ``` Is this the expected behaviour? The man page for DefCpuPerGPU says: "This value is used only if the job didn't specify --cpus-per-task and --cpus-per-gpu" which I do not specify, at least not directly. If yes is there a way to set something like DefCpuPerTask?

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Setting memory by assigned node with a plugin
by laddaoui＠telecom-paris.fr 09 Jul '25

09 Jul '25

Hello everyone, I'm writing a job_submit/lua plugin to set the memory allocated to a job depending on which node is assigned by Slurm. However, it appears that the node where the job will run is not available at this step of the submission process. Would a SPANK plugin be able to access this information and modify the memory allocation accordingly? Or is there another approach you would recommend for dynamically setting memory based on the assigned node? Any insights would be greatly appreciated. Best regards, Nacereddine

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Slurm versions 25.05.1 and 24.11.6 are now available
by Marshall Garey 08 Jul '25

08 Jul '25

We are pleased to announce the availability of Slurm versions 25.05.1 and 24.11.6. Changes in 25.05 include the following: * Fix many issues with the TLS Certificate Manager introduced in 25.05 * Optimize account deletion * Fix a bug when reordering the association hierarchy * Fix some issues that cause daemon crashes * Fix a variety of memory leaks Changes in 24.11 include the following: * Fix some issues that cause daemons to crash * Fix some race conditions on shutdown that cause daemons to crash or hang The full list of changes are available in the CHANGELOG for each version: https://github.com/SchedMD/slurm/blob/slurm-25.05/CHANGELOG/slurm-25.05.md https://github.com/SchedMD/slurm/blob/slurm-24.11/CHANGELOG/slurm-24.11.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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Energy profile data not recorded in InfluxDB or HDF5
by Xand Meaden 02 Jul '25

02 Jul '25

Hi, Environment: Slurm 23.11.5, Ubuntu Linux 22.04 I'm attempting to setup accounting of job energy data, collected from IPMI (with acct_gather_energy/ipmi) and then written to InfluxDB (with acct_gather_profile/influxdb), using this documentation: https://slurm.schedmd.com/acct_gather.conf.html This is successfully working for non-energy data. However, no Energy data appears in the InfluxDB. If I switch to using HDF5 instead, there is also no Energy data recorded there. Here's the config being used: root@erc-hpc-comp06t:/etc/slurm# grep Acct /etc/slurm/slurm.conf JobAcctGatherFrequency=30 JobAcctGatherType=jobacct_gather/cgroup AcctGatherEnergyType=acct_gather_energy/ipmi AcctGatherProfileType=acct_gather_profile/influxdb root@erc-hpc-comp06t:/etc/slurm# cat acct_gather.conf # Energy accounting gatherer configuration EnergyIPMIFrequency=60 EnergyIPMICalcAdjustment=yes EnergyIPMIUsername=xx EnergyIPMIPassword=xxxxx # InfluxDB configuration - record Energy and Task data by default ProfileInfluxDBDefault=Energy,Task # Using stunnel to reach InfluxDB over TLS ProfileInfluxDBHost=localhost:8086 ProfileInfluxDBDatabase=slurmpr_hpc_test ProfileInfluxDBUser=slurmpr_hpc_test_rw ProfileInfluxDBPass=xx ProfileInfluxDBRTPolicy=autogen The Task data is recorded just fine. And I can see that the IPMI plugin is working: root@erc-hpc-comp06t:/etc/slurm# scontrol show node erc-hpc-comp06t | grep CurrentWatts CurrentWatts=85 AveWatts=84 Does anyone have this working? I really hope I'm missing something obvious here! Thanks, Xand -- Xand Meaden | Principal Research Infrastructure Engineer e-Research | King's College London

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sbatch strange behavior with different --nodelist (-w) options
by Xinghong He 01 Jul '25

01 Jul '25

Dear Community, I'm seeing strange behavior from sbatch with different --nodelist (-w) options on my two node cluster. Here are my test scripts: *~/slurm$ cat mpirun.slm#!/bin/bash#SBATCH --job-name=mpirun_2x1#SBATCH --nodes=2#SBATCH --ntasks-per-node=1#SBATCH --exclusivesource /usr/mpi/gcc/openmpi-4.1.7a1/bin/mpivars.shmpirun ./a.sh* *~/slurm$ cat a.sh#!/bin/bashecho "`uname -n` OMPI_COMM_WORLD_RANK = $OMPI_COMM_WORLD_RANK SLURM_NODEID = $SLURM_NODEID SLURM_JOB_NODELIST = $SLURM_JOB_NODELIST"* If I do not specify any -w option or if I include both nodes in -w option, I get expected results. *~/slurm$ sbatch mpirun.slmSubmitted batch job 71~/slurm$ cat slurm-71.outstd-199 OMPI_COMM_WORLD_RANK = 0 SLURM_NODEID = 0 SLURM_JOB_NODELIST = std-[199,271]std-271 OMPI_COMM_WORLD_RANK = 1 SLURM_NODEID = 0 SLURM_JOB_NODELIST = std-[199,271]* However, if I specify only one node in -w option, but still want two nodes, I always get one expected result and one unexpected result. The unexpected one will dispatch both MPI tasks to the same node. This one is expected - running two MPI tasks across nodes. *~/slurm$ sbatch -w std-199 mpirun.slmSubmitted batch job 72~/slurm$ cat slurm-72.outstd-199 OMPI_COMM_WORLD_RANK = 0 SLURM_NODEID = 0 SLURM_JOB_NODELIST = std-[199,271]std-271 OMPI_COMM_WORLD_RANK = 1 SLURM_NODEID = 0 SLURM_JOB_NODELIST = std-[199,271]* This one if unexpected - ends up running two MPI tasks on the same node, though SLURM_JOB_NODELIST also gives the correct two nodes. *~/slurm$ sbatch -w std-271 mpirun.slmSubmitted batch job 73ubuntu@bright-anchovy-controller:~/slurm$ cat slurm-73.outstd-199 OMPI_COMM_WORLD_RANK = 0 SLURM_NODEID = 0 SLURM_JOB_NODELIST = std-[199,271]std-199 OMPI_COMM_WORLD_RANK = 1 SLURM_NODEID = 0 SLURM_JOB_NODELIST = std-[199,271]* I first saw the problem on a larger cluster where I needed to specify both -w and -x options to include and exclude nodes. I then narrowed it down to a two node cluster. I tried adding options like --hostfile or --rankfile, or -npernode, all does not change how the tasks are dispatched to nodes. The problem is repeatable. Here are the tested systems: Slurm 23.02.5 on Ubuntu 22.04.5 LTS Slurm 24.05.1 on Ubuntu 22.04.4 LTS How to make the last one work. I.e., requesting *-w std-271* and make it run on two nodes. I'd appreciate any help! Regards, Xinghong

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