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Slurm User Guide for Great Lakes

Slurm is a combined batch scheduler and resource manager that allows users to run their jobs on the University of Michigan’s high performance computing (HPC) clusters. This document describes the process for submitting and running jobs under the Slurm Workload Manager on the Great Lakes cluster.

The Batch Scheduler and Resource Manager

The batch scheduler and resource manager work together to run jobs on an HPC cluster. The batch scheduler, sometimes called a workload manager, is responsible for finding and allocating the resources that fulfill the job’s request at the soonest available time.  When a job is scheduled to run, the scheduler instructs the resource manager to launch the application(s) across the job’s allocated resources.  This is also known as “running the job”.

The user can specify conditions for scheduling the job. One condition is the completion (successful or unsuccessful) of an earlier submitted job.  Other conditions include the availability of a specific license or access to a specific hardware accelerator.

Computing Resources

An HPC cluster is made up of a number of compute nodes, each with a complement of processors, memory and GPUs. The user submits jobs that specify the application(s) they want to run along with a description of the computing resources needed to run the application(s).

Login Resources

Users interact with an HPC cluster through login nodes. Login nodes are a place where users can login, edit files, view job results and submit new jobs. Login nodes are a shared resource and should not be used to run application workloads.

Jobs and Job Steps

A job is an allocation of resources assigned to an individual user for a specified amount of time. Job steps are sets of (possibly parallel) tasks within a job. When a job runs, the scheduler selects and allocates resources to the job. The invocation of the application happens within the batch script, or at the command line for interactive and jobs.

When an application is launched using srun, it runs within a “job step”. The srun command causes the simultaneous launching of multiple tasks of a single application. Arguments to srun specify the number of tasks to launch as well as the resources (nodes, CPUs, memory, and GPUs) on which to launch the tasks.

Multiple srun commands can be invoked sequentially, or in parallel by backgrounding them. Furthermore, the resources specified on any srun command can be less than the total resources that were allocated to the job, but the total resources of all concurrently executing srun commands cannot exceed that total.

Batch Jobs

The sbatch command is used to submit a batch script to Slurm. It is designed to reject the job at submission time if there are requests or constraints that Slurm cannot fulfill as specified. This gives the user the opportunity to examine the job request and resubmit it with the necessary corrections. To submit a batch script simply run sbatch <scriptName>

$ sbatch myJob.sh

Submitting a Job in One Line

If you wish to submit a job without needing a separate script, you can use sbatch --wrap=<command string>.  This will wrap the specified command in a simple “sh” shell script, which is then submitted to the Slurm controller.

Anatomy of a Batch Job

The batch job script is composed of three main components:

• The interpreter used to execute the script
• #SBATCH directives that convey submission options
• The application(s) to execute along with its input arguments and options

An Example Slurm job

#!/bin/bash
# The interpreter used to execute the script

#“#SBATCH” directives that convey submission options:

#SBATCH --job-name=example_job
#SBATCH --mail-user=uniqname@umich.edu
#SBATCH --mail-type=BEGIN,END
#SBATCH --cpus-per-task=1
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem-per-cpu=1000m 
#SBATCH --time=10:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --output=/home/%u/%x-%j.log

# The application(s) to execute along with its input arguments and options:

/bin/hostname
sleep 60

Common Job Submission Options

Please note that if your job is set to utilize more than one node, make sure your code is MPI enabled in order to run across these nodes. You can use any of srun, mpirun or mpiexec commands to start your MPI job.

Interactive Jobs

An interactive job is a job that returns a command line prompt (instead of running a script) when the job runs. Interactive jobs are useful when debugging or interacting with an application. The salloc command is used to submit an interactive job to Slurm. When the job starts, a command line prompt will appear on one of the compute nodes assigned to the job. From here commands can be executed using the resources allocated on the local node.

[user@gl-login1 ~]$ salloc --account=test
salloc: Granted job allocation 23764789
salloc: Waiting for resource configuration
salloc: Nodes gl3032 are ready for job
[user@gl3032 ~ 3]$ hostname
gl3032.arc-ts.umich.edu
[user@gl3032 ~]$ 

Jobs submitted with salloc will be assigned the cluster default values of 1 CPU and 768MB of memory. If the account is not specified, your default account will be used. If additional resources are required, they can be requested as options to the salloc command. The following example job is assigned 2 nodes with 4 CPUS and 4GB of memory each:

[user@gl-login1 ~ 1]$ salloc --account=test --nodes=2 --ntasks-per-node=4 --mem-per-cpu=1GB --cpus-per-task=1
salloc: Granted job allocation 23765418
salloc: Waiting for resource configuration
salloc: Nodes gl[3041-3042] are ready for job
[user@gl3041 ~ 3]$ srun hostname
gl3041.arc-ts.umich.edu
gl3041.arc-ts.umich.edu
gl3041.arc-ts.umich.edu
gl3041.arc-ts.umich.edu
gl3042.arc-ts.umich.edu
gl3042.arc-ts.umich.edu
gl3042.arc-ts.umich.edu
gl3042.arc-ts.umich.edu

In the above example srun is used within the job from the first compute node to run a command once for every task in the job on the assigned resources. srun can be used to run on a subset of the resources assigned to the job. See the srun man page for more details.

GPU and Large Memory Jobs

Jobs can request GPUs with the job submission options --partition=gpu and a count option from the table below. All counts can be represented by gputype:number or just a number (default type will be used). Available GPU types can be found with the command sinfo -O gres -p <partition>. GPUs can be requested in both Batch and Interactive jobs.

Jobs can request nodes with large amounts of RAM with --partition=largemem.

Job Dependencies

You may want to run a set of jobs sequentially, so that the second job runs only after the first one has completed. This can be accomplished using Slurm’s job dependencies options. For example, if you have two jobs, Job1.sh and Job2.sh, you can utilize job dependencies as in the example below.

[user@gl-login1]$ sbatch Job1.sh
123213

[user@gl-login1]$ sbatch --dependency=afterany:123213 Job2.sh
123214

The flag --dependency=afterany:123213 tells the batch system to start the second job only after completion of the first job. afterany indicates that Job2 will run regardless of the exit status of Job1, i.e. regardless of whether the batch system thinks Job1 completed successfully or unsuccessfully.

Once job 123213 completes, job 123214 will be released by the batch system and then will run as the appropriate nodes become available.

Exit status: The exit status of a job is the exit status of the last command that was run in the batch script. An exit status of ‘0’ means that the batch system thinks the job completed successfully. It does not necessarily mean that all commands in the batch script completed successfully.

There are several options for the –dependency flag that depend on the status of Job1:

Making several jobs depend on the completion of a single job is done in the example below:

[user@gl-login1]$ sbatch Job1.sh 
13205 
[user@gl-login1]$ sbatch --dependency=afterany:13205 Job2.sh 
13206 
[user@gl-login1]$ sbatch --dependency=afterany:13205 Job3.sh 
13207 
[user@gl-login1]$ squeue -u $USER -S S,i,M -o "%12i %15j %4t %30E" 
JOBID        NAME            ST   DEPENDENCY                    
13205        Job1.bat        R                                  
13206        Job2.bat        PD   afterany:13205                
13207        Job3.bat        PD   afterany:13205

Making a job depend on the completion of several other jobs: example below.

[user@gl-login1]$ sbatch Job1.sh
13201
[user@gl-login1]$ sbatch Job2.sh
13202
[user@gl-login1]$ sbatch --dependency=afterany:13201,13202 Job3.sh
13203
[user@gl-login1]$ squeue -u $USER -S S,i,M -o "%12i %15j %4t %30E"
JOBID        NAME            ST   DEPENDENCY                    
13201        Job1.sh         R                                  
13202        Job2.sh         R                                  
13203        Job3.sh         PD   afterany:13201,afterany:13202

Chaining jobs is most easily done by submitting the second dependent job from within the first job. Example batch script:

#!/bin/bash

cd /data/mydir
run_some_command
sbatch --dependency=afterany:$SLURM_JOB_ID  my_second_job

Job dependencies documentation adapted from https://hpc.nih.gov/docs/userguide.html#depend

Job Arrays

Job arrays are multiple jobs to be executed with identical parameters. Job arrays are submitted with -a <indices> or --array=<indices>. The indices specification identifies what array index values should be used. Multiple values may be specified using a comma separated list and/or a range of values with a “-” separator: --array=0-15 or --array=0,6,16-32.

A step function can also be specified with a suffix containing a colon and number. For example,--array=0-15:4 is equivalent to --array=0,4,8,12.
A  maximum  number  of  simultaneously running tasks from the job array may be specified using a “%” separator. For example --array=0-15%4 will limit the number of simultaneously running tasks from this job array to 4. The minimum index value is 0. The maximum value is 499999.

To receive mail alerts for each individual array task, --mail-type=ARRAY_TASKS should be added to the Slurm job script. Unless this option is specified, mail notifications on job BEGIN, END and FAIL apply to a job array as a whole rather than generating individual email messages for each task in the job array.

Execution Environment

For each job type above, the user has the ability to define the execution environment. This includes environment variable definitions as well as shell limits (bash ulimit or csh limit). sbatch and salloc provide the --export option to convey specific environment variables to the execution environment. sbatch and salloc provide the --propagate option to convey specific shell limits to the execution environment. By default Slurm does not source the files ~./bashrc or ~/.profile when requesting resources via sbatch (although it does when running srun / salloc ).  So, if you have a standard environment that you have set in either of these files or your current shell then you can do one of the following:

1. Add the command #SBATCH --get-user-env to your job script (i.e. the module environment is propagated).
2. Source the configuration file in your job script:

< #SBATCH statements >
source ~/.bashrc

Note: You may want to remove the influence of any other current environment variables by adding #SBATCH --export=NONE to the script. This removes all set/exported variables and then acts as if #SBATCH --get-user-env has been added (module environment is propagated).

Environment Variables

Slurm recognizes and provides a number of environment variables.

The first category of environment variables are those that Slurm inserts into the job’s execution environment. These convey to the job script and application information such as job ID (SLURM_JOB_ID) and task ID (SLURM_PROCID). For the complete list, see the “OUTPUT ENVIRONMENT VARIABLES” section under the sbatch, salloc, and srun man pages.

The next category of environment variables are those use user can set in their environment to convey default options for every job they submit. These include options such as the wall clock limit. For the complete list, see the “INPUT ENVIRONMENT VARIABLES” section under the sbatch, salloc, and srun man pages.

Finally, Slurm allows the user to customize the behavior and output of some commands using environment variables. For example, one can specify certain fields for the squeue command to display by setting the SQUEUE_FORMAT variable in the environment from which you invoke squeue.

Commonly Used Environment Variables

Job Output

Slurm merges the job’s standard error and output by default and saves it to an output file with a name that includes the job ID (slurm-<job_ID>.out for normal jobs and "slurm-<job_ID_index.out for arrays"). You can specify your own output and error files to the sbatch command using the -o /file/to/output and -e /file/to/error options respectively. If both standard out and error should go to the same file, only specify -o /file/to/output Slurm will append the job’s output to the specified file(s). If you want the output to overwrite any existing files, add the --open-mode=truncate option. The files are written as soon as output is created. It does not spool on the compute node and then get copied to the final location after the job ends. If not specified in the job submission, standard output and error are combined and written into a file in the working directory from which the job was submitted.

For example if I submit job 93 from my home directory, the job output and error will be written to my home directory in a file called slurm-93.out. The file appears while the job is still running.

[user@gl-login1 ~]$ sbatch test.sh
Submitted batch job 93 
[user@gl-login1 ~]$ ll slurm-93.out
-rw-r–r– 1 user hpcstaff 122 Jun 7 15:28 slurm-93.out 
[user@gl-login1 ~]$ squeue 
JOBID PARTITION NAME    USER ST TIME NODES NODELIST(REASON) 
93    standard  example user R  0:04 1     gl3160

If you submit from a working directory which is not a shared filesystem, your output will only be available locally on the compute node and will need to be copied to another location after the job completes. /home, /scratch, and /nfs are all networked filesystems which are available on the login nodes and all compute nodes.

For example if I submit a job from /tmp on the login node, the output will be in /tmp on the compute node.

[user@gl-login1 tmp]$ pwd
/tmp
[user@gl-login1 tmp]$ sbatch /home/user/test.sh
Submitted batch job 98
[user@gl-login1 tmp]$ squeue
JOBID PARTITION     NAME     USER ST  TIME  NODES NODELIST(REASON)
98    standard      example  user R   0:03  1     gl3160
[user@gl-login1 tmp]$ ssh gl3160
[user@gl3160 ~]$ ll /tmp/slurm-98.out
-rw-r–r– 1 user hpcstaff 78 Jun 7 15:46 /tmp/slurm-98.out

Serial vs. Parallel jobs

Parallel jobs launch applications that are comprised of many processes (aka tasks) that communicate with each other, typically over a high speed switch. Serial jobs launch one or more tasks that work independently on separate problems.

Parallel applications must be launched by the srun command. Serial applications can use srun to launch them, but it is not required in one node allocations.

Job Partitions

A cluster is often highly utilized and may not be able to run a job when it is submitted. When this occurs, the job is placed in a partition. Specific compute node resources are defined for every job partition. The Slurm partition is synonymous with the term queue.

Each partition can be configured with a set of limits which specify the requirements for every job that can run in that partition. These limits include job size, wall clock limits, and the users who are allowed to run in that partition.

The Great Lakes cluster has the “standard” (used for most production jobs, 14 day max walltime), “largemem” (used for jobs that require large amounts of RAM, 14 day max), “gpu” (used for GPU-intensive tasks, 14 day max), “debug” (only to verify/debug jobs, 1 day max) and “viz” (used for visualization jobs, 1 day max) partitions.

Commands related to partitions include:

Job Status

Most of a job’s specifications can be seen by invoking scontrol show job <jobID>.  More details about the job can be written to a file by using  scontrol write batch_script <jobID> output.txt. If no output file is specified, the script will be written to slurm<jobID>.sh.

Slurm captures and reports the exit code of the job script (sbatch jobs) as well as the signal that caused the job’s termination when a signal caused a job’s termination.

A job’s record remains in Slurm’s memory for 30 minutes after it completes.  scontrol show job will return “Invalid job id specified” for a job that completed more than 30 minutes ago.  At that point, one must invoke the sacct command to retrieve the job’s record from the Slurm database.

Modifying a Batch Job

Many of the batch job specifications can be modified after a batch job is submitted and before it runs.  Typical fields that can be modified include the job size (number of nodes), partition (queue), and wall clock limit.  Job specifications cannot be modified by the user once the job enters the Running state.

Beside displaying a job’s specifications, the scontrol command is used to modify them.  Examples:

Holding and Releasing a Batch Job

If a user’s job is in the pending state waiting to be scheduled, the user can prevent the job from being scheduled by invoking the scontrol hold <jobID> command to place the job into a Held state. Jobs in the held state do not accrue any job priority based on queue wait time.  Once the user is ready for the job to become a candidate for scheduling once again, they can release the job using the scontrol release <jobID> command.

Signalling and Cancelling a Batch Job

Pending jobs can be cancelled (withdrawn from the queue) using the scancel command (scancel <jobID>).  The scancel command can also be used to terminate a running job.  The default behavior is to issue the job a SIGTERM, wait 30 seconds, and if processes from the job continue to run, issue a SIGKILL command.

The -s option of the scancel command (scancel -s <signal> <jobID>) allows the user to issue any signal to a running job.

Common Job Commands

Job States

The basic job states are these:

• Pending – the job is in the queue, waiting to be scheduled
• Held – the job was submitted, but was put in the held state (ineligible to run)
• Running – the job has been granted an allocation.  If it’s a batch job, the batch script has been run
• Complete – the job has completed successfully
• Timeout – the job was terminated for running longer than its wall clock limit
• Preempted – the running job was terminated to reassign its resources to a higher QoS job
• Failed – the job terminated with a non-zero status
• Node Fail – the job terminated after a compute node reported a problem

For the complete list, see the “JOB STATE CODES” section under the squeue man page.

Pending Reasons

A pending job can remain pending for a number of reasons:

• Dependency – the pending job is waiting for another job to complete
• Priority – the job is not high enough in the queue
• Resources – the job is high in the queue, but there are not enough resources to satisfy the job’s request
• Partition Down – the queue is currently closed to running any new jobs

For the complete list, see the “JOB REASON CODES” section under the squeue man page.

Displaying Computing Resources

As stated above, computing resources are nodes, CPUs, memory, and generic resources like GPUs.  The resources of each compute node can be seen by running the scontrol show node command.  The characteristics of each partition can be seen by running the scontrol show partition command.  Finally, a load summary report for each partition can be seen by running sinfo.

To show a summary of cluster resources on a per partition basis:

[user@gl-login1 ~]$ sinfo
PARTITION     AVAIL    TIMELIMIT    NODES STATE   NODELIST
standard      up       14-00:00:00  24    idle    gl31[60-83]
gpu           up       14-00:00:00  2     idle    gl10[18-19]
largemem      up       14-00:00:00  3     idle    gl000[0-3]

[user@gl-login1 ~]$ sstate
———————————————————————————————————————
Node    AllocCPU TotalCPU PercentUsedCPU  CPULoad AllocMem TotalMem PercentUsedMem NodeState
———————————————————————————————————————
gl3160  0        36       0.00            0.03    0        192000   0.00           IDLE
gl3160  0        36       0.00            0.04    0        192000   0.00           IDLE
...

In this example the user “user” has access to submit workloads to the accounts support and hpcstaff on the Great Lakes cluster. To show associations for the current user:

[user@gl-login1 ~]$ sacctmgr show assoc user=$USER

Cluster      Account  User  Partition  ...
———————————————————————————————————————
greatlakes   support  user  1    
greatlakes   hpcstaff user  1

Job Statistics and Accounting

The sreport command provides aggregated usage reports by user and account over a specified period. Examples:

By user: sreport -T billing cluster AccountUtilizationByUser Start=2017-01-01 End=2017-12-31

By account: sreport -T billing cluster UserUtilizationByAccount Start=2017-01-01 End=2017-12-31

For all of the sreport options see the sreport man page.

Time Remaining in an Application

If a running application overruns its wall clock limit, all its work could be lost. To prevent such an outcome, applications have two means for discovering the time remaining in the application.

The first means is to use the sbatch --signal=<sig_num>[@<sig_time>] option to request a signal (like USR1 or USR2) at sig_time number of seconds before the allocation expires. The application must register a signal handler for the requested signal in order to to receive it. The handler takes the necessary steps to write a checkpoint file and terminate gracefully.

The second means is for the application to issue a library call to retrieve its remaining time periodically. When the library call returns a remaining time below a certain threshold, the application can take the necessary steps to write a checkpoint file and terminate gracefully.

Slurm offers the slurm_get_rem_time() library call that returns the time remaining. On some systems, the yogrt library (man yogrt) is also available to provide the time remaining.