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Suggestions for Partition/QoS configuration
by thomas.hartmann＠th-ht.de 05 Apr '24

05 Apr '24

Hi, we're testing possible slurm configurations on a test system right now. Eventually, it is going to serve ~1000 users. We're going to have some users who are going to run lots of short jobs (a couple of minutes to ~4h) and some users that run jobs that are going to run for days or weeks. I want to avoid a situation in which a group of users basically saturates the whole cluster with jobs that run for a week or two and nobody could run any short jobs anymore. I also would like to favor short jobs, because they make the whole cluster feel more dynamic and agile for everybody. On the other hand, I would like to make the most of the ressources, i.e. when nobody is sending short jobs, long jobs could run on all the nodes. My idea was to basically have three partitions: 1. PartitionName=short MaxTime=04:00:00 State=UP Nodes=node[01-99] PriorityTier=100 2. PartitionName=long_safe MaxTime=14-00:00:00 State=UP Nodes=node[01-50] PriorityTier=100 3. PartitionName=long_preempt MaxTime=14-00:00:00 State=UP Nodes=nodes[01-99] PriorityTier=40 PreemptMode=requeue and then use the JobSubmitPlugin "all_partitions" so that all jobs get submitted to all partitions by default. This way, a short job ends up in the `short` partition and is able to use all nodes. A long job ends up using the `long_safe` partition until for the first 50 nodes. These jobs are not going to be preempted. Remaining long jobs use the `long_preempt` queue. So they run on the remaining nodes as long as there are no higher prio short (or long) jobs in the queue. So, the cluster could be saturated with long running jobs but if short jobs are submitted and the user has a high enough fair share, some of the long jobs would get preempted and the short ones would run. This scenario works fine.... BUT the long jobs seem to be playing pingpong on the `long_preempt` partition because as soon as they run, they stop accruing AGE priority unlike still queued jobs. As soon as a queued job, albeit by the same user, "overtakes" a running one, it preempts the running one, stops accruing age and so on.... So, is there maybe a cleverer way to do this? Thanks a lot! Thomas

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Re: [EXT] Re: SLURM configuration help
by Renfro, Michael 05 Apr '24

05 Apr '24

Yep, from your scontrol show node output: CfgTRES=cpu=64,mem=2052077M,billing=64 AllocTRES=cpu=1,mem=2052077M The running job (77) has allocated 1 CPU and all the memory on the node. That’s probably due to the partition using the default DefMemPerCPU value [1], which is unlimited. Since all our nodes are shared, and our workloads vary widely, we set our DefMemPerCPU value to something considerably lower than mem_in_node/cores_in_node . That way, most jobs will leave some memory available by default, and other jobs can use that extra memory as long as CPUs are available. [1] https://slurm.schedmd.com/slurm.conf.html#OPT_DefMemPerCPU From: Alison Peterson <apeterson5(a)sdsu.edu> Date: Thursday, April 4, 2024 at 11:58 AM To: Renfro, Michael <Renfro(a)tntech.edu> Subject: Re: [EXT] Re: [slurm-users] SLURM configuration help External Email Warning This email originated from outside the university. Please use caution when opening attachments, clicking links, or responding to requests. ________________________________ Here is the info: sma@cusco:/data/work/sma-scratch/tohoku_wOcean$ scontrol show node cusco NodeName=cusco Arch=x86_64 CoresPerSocket=32 CPUAlloc=1 CPUTot=64 CPULoad=0.02 AvailableFeatures=(null) ActiveFeatures=(null) Gres=gpu:4 NodeAddr=cusco NodeHostName=cusco Version=19.05.5 OS=Linux 5.4.0-172-generic #190-Ubuntu SMP Fri Feb 2 23:24:22 UTC 2024 RealMemory=2052077 AllocMem=2052077 FreeMem=1995947 Sockets=2 Boards=1 State=MIXED ThreadsPerCore=1 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A Partitions=mainpart BootTime=2024-03-01T17:06:26 SlurmdStartTime=2024-03-01T17:06:53 CfgTRES=cpu=64,mem=2052077M,billing=64 AllocTRES=cpu=1,mem=2052077M CapWatts=n/a CurrentWatts=0 AveWatts=0 ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s sma@cusco:/data/work/sma-scratch/tohoku_wOcean$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 78 mainpart CF1090_w sma PD 0:00 1 (Resources) 77 mainpart CF0000_w sma R 0:26 1 cusco sma@cusco:/data/work/sma-scratch/tohoku_wOcean$ scontrol show job 78 JobId=78 JobName=CF1090_wOcean500m.shell UserId=sma(1008) GroupId=myfault(1001) MCS_label=N/A Priority=4294901720 Nice=0 Account=(null) QOS=(null) JobState=PENDING Reason=Resources Dependency=(null) Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0 RunTime=00:00:00 TimeLimit=01:00:00 TimeMin=N/A SubmitTime=2024-04-04T09:55:34 EligibleTime=2024-04-04T09:55:34 AccrueTime=2024-04-04T09:55:34 StartTime=2024-04-04T10:55:28 EndTime=2024-04-04T11:55:28 Deadline=N/A SuspendTime=None SecsPreSuspend=0 LastSchedEval=2024-04-04T09:55:58 Partition=mainpart AllocNode:Sid=newcusco:2450574 ReqNodeList=(null) ExcNodeList=(null) NodeList=(null) SchedNodeList=cusco NumNodes=1 NumCPUs=1 NumTasks=1 CPUs/Task=1 ReqB:S:C:T=0:0:*:* TRES=cpu=1,node=1,billing=1 Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=* MinCPUsNode=1 MinMemoryNode=0 MinTmpDiskNode=0 Features=(null) DelayBoot=00:00:00 OverSubscribe=YES Contiguous=0 Licenses=(null) Network=(null) Command=/data/work/sma-scratch/tohoku_wOcean/CF1090_wOcean500m.shell WorkDir=/data/work/sma-scratch/tohoku_wOcean StdErr=/data/work/sma-scratch/tohoku_wOcean/slurm-78.out StdIn=/dev/null StdOut=/data/work/sma-scratch/tohoku_wOcean/slurm-78.out Power= On Thu, Apr 4, 2024 at 8:57 AM Renfro, Michael <Renfro(a)tntech.edu<mailto:Renfro@tntech.edu>> wrote: What does “scontrol show node cusco” and “scontrol show job PENDING_JOB_ID” show? On one job we currently have that’s pending due to Resources, that job has requested 90 CPUs and 180 GB of memory as seen in its ReqTRES= value, but the node it wants to run on only has 37 CPUs available (seen by comparing its CfgTRES= and AllocTRES= values). From: Alison Peterson via slurm-users <slurm-users(a)lists.schedmd.com<mailto:slurm-users@lists.schedmd.com>> Date: Thursday, April 4, 2024 at 10:43 AM To: slurm-users(a)lists.schedmd.com<mailto:slurm-users@lists.schedmd.com> <slurm-users(a)lists.schedmd.com<mailto:slurm-users@lists.schedmd.com>> Subject: [slurm-users] SLURM configuration help External Email Warning This email originated from outside the university. Please use caution when opening attachments, clicking links, or responding to requests. ________________________________ I am writing to seek assistance with a critical issue on our single-node system managed by Slurm. Our jobs are queued and marked as awaiting resources, but they are not starting despite seeming availability. I'm new with SLURM and my only experience was a class on installing it so I have no experience, running it or using it. Issue Summary: Main Problem: Jobs submitted only one run and the second says NODELIST(REASON) (Resources). I've checked that our single node has enough RAM (2TB) and CPU's (64) available. # COMPUTE NODES NodeName=cusco CPUs=64 Sockets=2 CoresPerSocket=32 ThreadsPerCore=1 RealMemory=2052077 Gres=gpu:1,gpu:1,gpu:1,gpu:1 PartitionName=mainpart Default=YES MinNodes=1 DefaultTime=00:60:00 MaxTime=UNLIMITED AllowAccounts=ALL Nodes=ALL State=UP OverSubscribe=Force System Details: We have a single-node setup with Slurm as the workload manager. The node appears to have sufficient resources for the queued jobs. Troubleshooting Performed: Configuration Checks: I have verified all Slurm configurations and the system's resource availability, which should not be limiting job execution. Service Status: The Slurm daemon slurmdbd is active and running without any reported issues. System resource monitoring shows no shortages that would prevent job initiation. Any guidance and help will be deeply appreciated! -- Alison Peterson IT Research Support Analyst Information Technology apeterson5(a)sdsu.edu<mailto:mfarley@sdsu.edu> O: 619-594-3364 San Diego State University | SDSU.edu<http://sdsu.edu/> 5500 Campanile Drive | San Diego, CA 92182-8080 Error! Filename not specified. -- Alison Peterson IT Research Support Analyst Information Technology apeterson5(a)sdsu.edu<mailto:mfarley@sdsu.edu> O: 619-594-3364 San Diego State University | SDSU.edu<http://sdsu.edu/> 5500 Campanile Drive | San Diego, CA 92182-8080 [Image removed by sender.]

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SLURM configuration help
by Alison Peterson 05 Apr '24

05 Apr '24

I am writing to seek assistance with a critical issue on our single-node system managed by Slurm. Our jobs are queued and marked as awaiting resources, but they are not starting despite seeming availability. I'm new with SLURM and my only experience was a class on installing it so I have no experience, running it or using it. Issue Summary: Main Problem: Jobs submitted only one run and the second says *NODELIST(REASON) (Resources*). I've checked that our single node has enough RAM (2TB) and CPU's (64) available. # COMPUTE NODES NodeName=cusco CPUs=64 Sockets=2 CoresPerSocket=32 ThreadsPerCore=1 RealMemory=2052077 Gres=gpu:1,gpu:1,gpu:1,gpu:1 PartitionName=mainpart Default=YES MinNodes=1 DefaultTime=00:60:00 MaxTime=UNLIMITED AllowAccounts=ALL Nodes=ALL State=UP OverSubscribe=Force System Details: We have a single-node setup with Slurm as the workload manager. The node appears to have sufficient resources for the queued jobs. Troubleshooting Performed: Configuration Checks: I have verified all Slurm configurations and the system's resource availability, which should not be limiting job execution. Service Status: The Slurm daemon slurmdbd is active and running without any reported issues. System resource monitoring shows no shortages that would prevent job initiation. Any guidance and help will be deeply appreciated! -- *Alison Peterson* IT Research Support Analyst *Information Technology* apeterson5(a)sdsu.edu <mfarley(a)sdsu.edu> O: 619-594-3364 *San Diego State University | SDSU.edu <http://sdsu.edu/>* 5500 Campanile Drive | San Diego, CA 92182-8080

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Slurm 23.11 - Unknown system variable 'wsrep_on'
by Russell Jones 05 Apr '24

05 Apr '24

Hi all, I am working on upgrading a Slurm cluster from 20 -> 23. I was successfully able to upgrade to 22, however now that I am trying to go from 22 to 23, starting slurmdbd results in the following error being logged: error: mysql_query failed: 1193 Unknown system variable 'wsrep_on' When trying to start slurmctld, I get: [2024-04-02T15:09:52.439] Couldn't find tres gres/gpumem in the database, creating. [2024-04-02T15:09:52.439] Couldn't find tres gres/gpuutil in the database, creating. [2024-04-02T15:09:52.440] fatal: Problem adding tres to the database, can't continue until database is able to make new tres Any ideas what could be causing these errors? Is MariaDB 5.5 still officially supported?

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scrun: Failed to run the container due to GID mapping configuration
by Toshiki Sonoda (Fujitsu) 04 Apr '24

04 Apr '24

Dear All, We set up scrun (slurm 23.11.5) integrated with rootless podman, referring to the official documentation. https://slurm.schedmd.com/containers.html#podman-scrun https://slurm.schedmd.com/scrun.html#SECTION_Example-%3CB%3Escrun.lua%3C/B%… However, runc/crun prints the error message and fails to run the container. Looking at the error message from runc, it seems that there is a problem with the GID mapping configuration. - runc [hoge@slm-master hoge]$ podman run centos echo "Hello World" time="2024-04-03T02:39:36Z" level=error msg="runc run failed: cannot specify gid=5 mount option for rootless container: User namespaces enabled, but no group mapping found." srun: error: slm-worker: task 0: Exited with exit code 1 - crun [hoge@slm-master hoge]$ podman run centos echo "Hello World" srun: error: slm-worker: task 0: Exited with exit code 1 mount `devpts` to `dev/pts`: Invalid argument When we removed the process of linux.gidMappings configuration from scrun.lua, runc failed to run the container due to the different error, but crun succeeded. - scrun.lua (The process of linux.gidMappings configuration) ... snip ... -- Provide default group map as root if one not provided -- mappings fail with build??? if (true or config["linux"]["gidMappings"] == nil) then config["linux"]["gidMappings"] = {{containerID=process_group_id, hostID=math.floor(group_id), size=1}} end ... snip ... - runc [hoge@slm-master hoge]$ podman run centos echo "Hello World" time="2024-04-03T02:37:18Z" level=error msg="runc run failed: User namespaces enabled, but no gid mappings found." srun: error: slm-worker: task 0: Exited with exit code 1 - crun [hoge@slm-master hoge]$ podman run centos echo "Hello World" Hello World Therefore, we would like to know the following two questions. - The reason for the failure to run the container - Required or recommended configuration to run the container Best Regards, Toshiki Sonoda

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File-less NVIDIA GeForce 4070 Ti being removed from GRES list
by Shooktija S N 03 Apr '24

03 Apr '24

Hi, I am trying to set up Slurm (version 22.05) on a 3 node cluster each having an NVIDIA GeForce RTX 4070 Ti GPU. I tried to follow along with the GRES setup tutorial on the Schedmd website and added the following (Gres=gpu:RTX4070TI:1) to the Node configuration in /etc/slurm/slurm.conf: NodeName=server[1-3] RealMemory=128636 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 State=UNKNOWN Gres=gpu:RTX4070TI:1 I do not have a gres.conf. However, I see this line at the debug log level in /var/log/slurmd.log: [2024-04-02T15:57:19.022] debug: Removing file-less GPU gpu:RTX4070TI from final GRES list What other configs are necessary for Slurm to work with my GPU? More information: OS: Proxmox VE 8.1.4 Kernel: 6.5.13 CPU: AMD EPYC 7662 Memory: 128636MiB /etc/slurm/slurm.conf that's shared by all the 3 nodes without the comment lines: ClusterName=DlabCluster SlurmctldHost=server1 GresTypes=gpu ProctrackType=proctrack/linuxproc ReturnToService=1 SlurmctldPidFile=/var/run/slurmctld.pid SlurmctldPort=6817 SlurmdPidFile=/var/run/slurmd.pid SlurmdPort=6818 SlurmdSpoolDir=/var/spool/slurmd SlurmUser=root StateSaveLocation=/var/spool/slurmctld TaskPlugin=task/affinity,task/cgroup InactiveLimit=0 KillWait=30 MinJobAge=300 SlurmctldTimeout=120 SlurmdTimeout=300 Waittime=0 SchedulerType=sched/backfill SelectType=select/cons_tres JobCompType=jobcomp/none JobAcctGatherFrequency=30 SlurmctldDebug=debug SlurmctldLogFile=/var/log/slurmctld.log SlurmdDebug=debug SlurmdLogFile=/var/log/slurmd.log NodeName=server[1-3] RealMemory=128636 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 State=UNKNOWN Gres=gpu:RTX4070TI:1 PartitionName=mainPartition Nodes=ALL Default=YES MaxTime=INFINITE State=UP

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PMIxDirectConn Setting
by efweber＠uwe.nsa.gov 28 Mar '24

28 Mar '24

Hi, The documentation for mpi.conf says: PMIxDirectConn={true|false} Disable direct launching of tasks. Default is "true". I'm finding this a bit confusing. Does true really mean that Direct Connection is disabled? I would have thought true meant enabled. Thanks

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Incorrect hyperthreading with Slurm 23.11
by Guillaume COCHARD 28 Mar '24

28 Mar '24

Hello, We have upgraded our cluster to Slurm 23.11.1 then, a few weeks later, to 23.11.4. Since then, Slurm doesn't detect hyperthreaded CPUs. We have downgraded our test cluster, the issue is not present with Slurm 22.05 (we had skipped Slurm 23.02). For example, we are working with this node: $ slurmd -C NodeName=node03 CPUs=40 Boards=1 SocketsPerBoard=2 CoresPerSocket=10 ThreadsPerCore=2 RealMemory=128215 It is defined like this in slurm.conf: SelectTypeParameters=CR_CPU_Memory TaskPlugin=task/cgroup,task/affinity NodeName=node03 CPUs=40 RealMemory=150000 Feature=htc MemSpecLimit=5000 NodeSet=htc Feature=htc PartitionName=htc Default=YES MinNodes=0 MaxNodes=1 Nodes=htc DefMemPerCPU=1000 State=UP LLN=Yes MaxMemPerNode=142000 So no oversubscribing, 20 cores and 40 CPUs thanks to hyperthreading. Until the updgrade, Slurm was allocating those 40 CPUs: when launching 40 jobs of 1 CPU, each of those job would use one 1 CPU. This is the expected behavior. Since the upgrade, we can still launch those 40 jobs, but only the first half of the CPUs will be used (CPUs 0 to 19 according to htop). Each of those CPUs is used by 2 jobs, and the second half of the CPUs (#20 to 39) stay completely idle. When launching 40 stress processes directly in the node without using Slurm all the CPUs are used. When allocating a specific CPU with srun, it works until CPU #19 and then an error occurs even if the allocation includes all the CPUs of the node: #SBATCH --ntasks=1 #SBATCH --cpus-per-task=40 # Works for 0 to 19 srun --cpu-bind=v,map_cpu:19 stress.py # Doesn't work (20 to 39) srun --cpu-bind=v,map_cpu:20 stress.py # Output: srun: error: CPU binding outside of job step allocation, allocated CPUs are: 0x00000FFFFF. srun: error: Task launch for StepId=57194.0 failed on node node03: Unable to satisfy cpu bind request srun: error: Application launch failed: Unable to satisfy cpu bind request srun: Job step aborted This behaviour concerns all our nodes, some of which have been restarted recently and others have not. This causes the jobs to be frequently interrupted, augmenting the difference between the system real time and user+system times and making the jobs slower. We have been peering the documentation but, from what we understand, our configuration seems correct. In particular, as advised by the documentation[1], we don't set up ThreadsPerCore in slurm.conf. Are we missing something, or is there a regression or a change in Slurm configuration since the version 23.11? Thank you, Guillaume [1] : https://slurm.schedmd.com/slurm.conf.html#OPT_ThreadsPerCore

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FairShare priority questions
by Long, Daniel S. 27 Mar '24

27 Mar '24

Hi, I’m trying to set up multifactor priority on our cluster and am having some trouble getting it to behave the way I’d like. My main issues seem to revolve around FairShare. We have multiple projects on our cluster and multiple users in those projects (and some users are in multiple projects, of course). I would like the FairShare to be based only on the project associated with the job; if user A and user B both submit jobs on project C, the FairShare should be identical. However, it looks like the FairShare is based on both the project and the user. Is there a way to get the behavior I’m looking for? Thanks for any help you can provide.

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Slurm releases move to a six-month cycle
by Tim Wickberg 27 Mar '24

27 Mar '24

Slurm major releases are moving to a six month release cycle. This change starts with the upcoming Slurm 24.05 release this May. Slurm 24.11 will follow in November 2024. Major releases then continue every May and November in 2025 and beyond. There are two main goals of this change: - Faster delivery of newer features and functionality for customers. - "Predictable" release timing, especially for those sites that would prefer to upgrade during an annual system maintenance window. SchedMD will be adjusting our handling of backwards-compatibility within Slurm itself, and how SchedMD's support services will handle older releases. For the 24.05 release, Slurm will still only support upgrading from (and mixed-version operations with) the prior two releases (23.11, 23.02). Starting with 24.11, Slurm will start supporting upgrades from the prior three releases (24.05, 23.11, 23.02). SchedMD's Slurm Support has been built around an 18-month cycle. This 18-month cycle has traditionally covered the current stable release, plus one prior major releases. With the increase in release frequency this support window will now cover to the current stable release, plus two prior major releases. The blog post version of this announcement includes a table that outlines the updated support lifecycle: https://www.schedmd.com/slurm-releases-move-to-a-six-month-cycle/ - Tim -- Tim Wickberg Chief Technology Officer, SchedMD LLC Commercial Slurm Development and Support

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