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Goal: List some common issues and how to resolve.

Common Questions

  • How do I know what number of nodes, cores, memory etc to ask for my jobs?

  • How do I find out whether a cluster/partition supports these resources?

  • How do I find out whether these resources are available on the cluster?

  • How long will I have to wait in the queue before my job starts? How busy is the cluster?

  • How do I monitor the progress of my job?

Common Questions: Suggestions

  • How do I know what number of nodes, cores, memory etc to ask for my jobs?

    • Understand your software and application. 

      • Read the docs – look at the help for commands/options.

      • Can it run multiple threads - use multi cores (OpenMP) / nodes (MPI)?

      • Can it use a GPU? Nvidia cuda.

      • Are their suggestions on data and memory requirements?

  • How do I find out whether a cluster/partition supports these resources?

  • How do I find out whether these resources are available on the cluster?

  • How long will I have to wait in the queue before my job starts? 

    • How busy is the cluster? 

    • Current Cluster utilization: Commands sinfo / arccjobs / pestat and OnDemand’s MedicineBow System Status page.

  • How do I monitor the progress of my job?

    • Slurm commands: squeue

Common Issues

  • Not defining the account and time options.

  • The account is the name of the project you are associated with. It is not your username.

  • Requesting combinations of resources that can not be satisfied: Medicine Hardware Summary Table

    • For example, you can not request 40 cores on a compute node with a max of 32.

    • Requesting too much memory, or too many GPU devices with respect to a partition.

  • My job is pending? Why? 

    • Because the resources are currently not available.

      • Be mindful of specialized nodes (such as our huge mem with 4T of RAM) we might only have a few of them.

    • Have you unnecessarily defined a specific partition (restricted yourself) that is busy? 

    • We only have a small number of GPUs.

    • This is a shared resource - sometimes you just have to be patient…

    • Check current cluster utilization.

    • What ever resources you are asking for are currently not available. Slurm will start you job when they become available.

    • We do empathize and under the frustration, but this is a shared resource, and sometimes we just have to be patient and wait in the queue.

  • Preemption: Users of an investment get priority on their hardware.

    • We have the non-investor partition.

Pending Jobs and Scheduled Cluster Maintenance

When we have schedule maintenance on the cluster, an announcement will go out and indicate the date/time that this is scheduled to start.

All jobs currently running on the cluster are allowed to finish, and we can aspect further jobs if Slurm can complete them before the maintenance starts.

Be conscious of: If an announcement goes out on a Monday that maintenance is to start on the following Friday, there is only a window of four days that any new job must complete within.

If you submit a job with a wall time of seven days, then this can not and will not be started since it can not complete before Friday.

You job will be accepted by Slurm, queued, have a status of pending, and automatically started once maintenance is completed and Slurm is restarted.

Required: Account and Walltime

Remember: By default you must define the project (account) you’re using and a walltime.

[salexan5@mblog2 ~]$ salloc
salloc: error: You didn't specify a project account (-A,--account). Please open a ticket at arcc-help@uwyo.edu for help.
salloc: error: Job submit/allocate failed: Invalid account or account/partition combination specified

[salexan5@mblog2 ~]$ salloc -A arcc
salloc: error: You didn't specify a walltime (-t, --time=) for the job. Please open a ticket at arcc-help@uwyo.edu for help.
salloc: error: Job submit/allocate failed: Requested time limit is invalid (missing or exceeds some limit)

[salexan5@mblog2 ~]$ salloc -t 10:00
salloc: error: You didn't specify a project account (-A,--account). Please open a ticket at arcc-help@uwyo.edu for help.
salloc: error: Job submit/allocate failed: Invalid account or account/partition combination specified

# The bare minimum:
[salexan5@mblog2 ~]$ salloc -A arcc -t 10:00
salloc: Granted job allocation 1250349
salloc: Nodes mbcpu-025 are ready for job

Correct Partitions

If you need to explicitly request a partition, the name must be correct:

[salexan5@mblog2 ~]$ salloc -A arcc -t 10:00 --partition=mb-l40
salloc: error: invalid partition specified: mb-l40
salloc: error: Job submit/allocate failed: Invalid partition name specified

Use the sinfo command to get a list of know partitions, as well as detailing their current use:

 Example: sinfo
[salexan5@mblog2 ~]$ sinfo
PARTITION    AVAIL  TIMELIMIT  NODES  STATE NODELIST
mb*             up 7-00:00:00      4    mix mbcpu-[008,010-011,025]
mb*             up 7-00:00:00      8  alloc mbcpu-[001-007,009]
mb*             up 7-00:00:00     13   idle mbcpu-[012-024]
mb-a30          up 7-00:00:00      8   idle mba30-[001-008]
mb-l40s         up 7-00:00:00      1    mix mbl40s-001
mb-l40s         up 7-00:00:00      5   idle mbl40s-[002-005,007]
mb-h100         up 7-00:00:00      1 drain$ mbh100-001
mb-h100         up 7-00:00:00      1  down$ mbh100-006
mb-h100         up 7-00:00:00      1  drain mbh100-002
mb-h100         up 7-00:00:00      3    mix mbh100-[003-005]
mb-a6000        up 7-00:00:00      1   idle mba6000-001
inv-arcc        up   infinite      1    mix mbcpu-025
inv-inbre       up   infinite      1   idle mbl40s-007
inv-ssheshap    up   infinite      1   idle mba6000-001
inv-wysbc       up   infinite      1  alloc mbcpu-001
inv-wysbc       up   infinite      1   idle mba30-001
inv-soc         up   infinite      1    mix mbl40s-001
inv-wildiris    up   infinite      5   idle wi[001-005]
non-investor    up 7-00:00:00      1 drain$ mbh100-001
non-investor    up 7-00:00:00      1  down$ mbh100-006
non-investor    up 7-00:00:00      1  drain mbh100-002
non-investor    up 7-00:00:00      6    mix mbcpu-[008,010-011],mbh100-[003-005]
non-investor    up 7-00:00:00      7  alloc mbcpu-[002-007,009]
non-investor    up 7-00:00:00     24   idle mba30-[002-008],mbcpu-[012-024],mbl40s-[002-005]
# Corrected:
[salexan5@mblog2 ~]$ salloc -A arcc -t 10:00 --partition=mb-l40s
salloc: Pending job allocation 1250907
salloc: job 1250907 queued and waiting for resources
salloc: job 1250907 has been allocated resources
salloc: Granted job allocation 1250907
salloc: Nodes mbl40s-001 are ready for job

Timeouts

Timeouts aren’t errors as such, just that the time you requested was not long enough to compete the computation.

The maximum allowed wall time is 7 days:

[arcc-t01@mblog2 ~]$ salloc -A arccanetrain -t 7-00:00:01
salloc: error: Job submit/allocate failed: Requested time limit is invalid (missing or exceeds some limit)

[arcc-t01@mblog2 ~]$ salloc -A arccanetrain -t 7-00:00:00
salloc: Granted job allocation 1251651
salloc: Nodes mbcpu-010 are ready for job

Do not request 7 days just because you can!

Wall time is considered when Slurm tries to allocate your job.

A job is more likely to be back filled (slotted onto the cluster) in busy times than pending jobs will longer wall times.

My Jobs Need to Run Longer than 7 Days

ARCC can provide users with wall times longer than 7 days.

Please contact use, but we require that you can demonstrate that you job can not be optimized, for example:

  • Can it run faster by using more cores, or even multiple nodes?

  • Can it utilize GPUs?

  • Can the job actually be divided up into sections that can be run concurrently across multiple jobs?

ARCC can provide assistance with trying to understand if a job can be optimized.

Requested node configuration is not available

This is caused because you’re trying to request a configuration that isn’t available, or requires more details: For example:

Too many cores on a node:

[salexan5@mblog2 ~]$ salloc -A arcc -t 10:00 -c 100
salloc: error: CPU count per node can not be satisfied
salloc: error: Job submit/allocate failed: Requested node configuration is not available

Must define a GPU enabled partition:

[salexan5@mblog2 ~]$ salloc -A arcc -t 10:00 --gres=gpu:1
salloc: error: Job submit/allocate failed: Requested node configuration is not available
salloc: Job allocation 1253677 has been revoked.

[salexan5@mblog2 ~]$ salloc -A arcc -t 10:00 --gres=gpu:1 --partition=mb-a30
salloc: Granted job allocation 1253691
salloc: Nodes mba30-001 are ready for job

OUT-OF-MEMORY: Segmentation Fault

Segmentation faults are typically caused by an application trying to access memory outside what has been allocated to the job.

Basically, you job is out of memory of what it requested.

Resolved: Request more memory using either the mem or mem-per-cpu .

My Job Stopped and Re-Started: Preemption

  • As discussed in the Intro to HPC workshop, we have a Condominium Model where if your job is running an a compute node that is part of another project’s hardware investment, your job can be preempted.

  • Your job will be stopped and automatically re-queued and when resources come available on the cluster, it will be restarted.

  • Further details can be found on our Slurm and Preemption page and how to use the non-investor partition to prevent this from happening.

Why Is My Job One-of-Many on a Compute Node?

When I run pestat, it appears that my job is one of many on a particular compute node.

[]$ pestat -n mbl40s-001
Select only nodes in hostlist=mbl40s-001
Hostname         Partition     Node Num_CPU  CPUload  Memsize  Freemem  Joblist
                              State Use/Tot  (15min)     (MB)     (MB)  JobID(JobArrayID) User ...
mbl40s-001         mb-l40s     mix   64  96   55.62*   765525   310618  1480439 hbalantr 1440260 vvarenth

As discussed in the Intro to HPC workshop when talking about Compute Nodes this is perfectly acceptable and one of the tasks that Slurm manages.

Remember: All jobs are independent and do not affect anyone else.

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Slurm: More Features 

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Intro to Job Scheduling

Use the following link to provide feedback on this training: https://forms.gle/bxhKoVaPns51Qhb99 or use the QR code below.

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