Quick Start User Guide


Slurm is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters. Slurm requires no kernel modifications for its operation and is relatively self-contained. As a cluster workload manager, Slurm has three key functions. First, it allocates exclusive and/or non-exclusive access to resources (compute nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (normally a parallel job) on the set of allocated nodes. Finally, it arbitrates contention for resources by managing a queue of pending work.


As depicted in Figure 1, Slurm consists of a slurmd daemon running on each compute node and a central slurmctld daemon running on a management node (with optional fail-over twin). The slurmd daemons provide fault-tolerant hierarchical communications. The user commands include: sacct, sacctmgr, salloc, sattach, sbatch, sbcast, scancel, scontrol, scrontab, sdiag, sh5util, sinfo, sprio, squeue, sreport, srun, sshare, sstat, strigger and sview. All of the commands can run anywhere in the cluster.

Figure 1. Slurm components

The entities managed by these Slurm daemons, shown in Figure 2, include nodes, the compute resource in Slurm, partitions, which group nodes into logical (possibly overlapping) sets, jobs, or allocations of resources assigned to a user for a specified amount of time, and job steps, which are sets of (possibly parallel) tasks within a job. The partitions can be considered job queues, each of which has an assortment of constraints such as job size limit, job time limit, users permitted to use it, etc. Priority-ordered jobs are allocated nodes within a partition until the resources (nodes, processors, memory, etc.) within that partition are exhausted. Once a job is assigned a set of nodes, the user is able to initiate parallel work in the form of job steps in any configuration within the allocation. For instance, a single job step may be started that utilizes all nodes allocated to the job, or several job steps may independently use a portion of the allocation.

Figure 2. Slurm entities


Man pages exist for all Slurm daemons, commands, and API functions. The command option --help also provides a brief summary of options. Note that the command options are all case sensitive.

sacct is used to report job or job step accounting information about active or completed jobs.

salloc is used to allocate resources for a job in real time. Typically this is used to allocate resources and spawn a shell. The shell is then used to execute srun commands to launch parallel tasks.

sattach is used to attach standard input, output, and error plus signal capabilities to a currently running job or job step. One can attach to and detach from jobs multiple times.

sbatch is used to submit a job script for later execution. The script will typically contain one or more srun commands to launch parallel tasks.

sbcast is used to transfer a file from local disk to local disk on the nodes allocated to a job. This can be used to effectively use diskless compute nodes or provide improved performance relative to a shared file system.

scancel is used to cancel a pending or running job or job step. It can also be used to send an arbitrary signal to all processes associated with a running job or job step.

scontrol is the administrative tool used to view and/or modify Slurm state. Note that many scontrol commands can only be executed as user root.

sinfo reports the state of partitions and nodes managed by Slurm. It has a wide variety of filtering, sorting, and formatting options.

sprio is used to display a detailed view of the components affecting a job's priority.

squeue reports the state of jobs or job steps. It has a wide variety of filtering, sorting, and formatting options. By default, it reports the running jobs in priority order and then the pending jobs in priority order.

srun is used to submit a job for execution or initiate job steps in real time. srun has a wide variety of options to specify resource requirements, including: minimum and maximum node count, processor count, specific nodes to use or not use, and specific node characteristics (so much memory, disk space, certain required features, etc.). A job can contain multiple job steps executing sequentially or in parallel on independent or shared resources within the job's node allocation.

sshare displays detailed information about fairshare usage on the cluster. Note that this is only viable when using the priority/multifactor plugin.

sstat is used to get information about the resources utilized by a running job or job step.

strigger is used to set, get or view event triggers. Event triggers include things such as nodes going down or jobs approaching their time limit.

sview is a graphical user interface to get and update state information for jobs, partitions, and nodes managed by Slurm.


First we determine what partitions exist on the system, what nodes they include, and general system state. This information is provided by the sinfo command. In the example below we find there are two partitions: debug and batch. The * following the name debug indicates this is the default partition for submitted jobs. We see that both partitions are in an UP state. Some configurations may include partitions for larger jobs that are DOWN except on weekends or at night. The information about each partition may be split over more than one line so that nodes in different states can be identified. In this case, the two nodes adev[1-2] are down. The * following the state down indicate the nodes are not responding. Note the use of a concise expression for node name specification with a common prefix adev and numeric ranges or specific numbers identified. This format allows for very large clusters to be easily managed. The sinfo command has many options to easily let you view the information of interest to you in whatever format you prefer. See the man page for more information.

adev0: sinfo
debug*       up      30:00     2  down* adev[1-2]
debug*       up      30:00     3   idle adev[3-5]
batch        up      30:00     3  down* adev[6,13,15]
batch        up      30:00     3  alloc adev[7-8,14]
batch        up      30:00     4   idle adev[9-12]

Next we determine what jobs exist on the system using the squeue command. The ST field is job state. Two jobs are in a running state (R is an abbreviation for Running) while one job is in a pending state (PD is an abbreviation for Pending). The TIME field shows how long the jobs have run for using the format days-hours:minutes:seconds. The NODELIST(REASON) field indicates where the job is running or the reason it is still pending. Typical reasons for pending jobs are Resources (waiting for resources to become available) and Priority (queued behind a higher priority job). The squeue command has many options to easily let you view the information of interest to you in whatever format you prefer. See the man page for more information.

adev0: squeue
65646     batch  chem  mike  R 24:19     2 adev[7-8]
65647     batch   bio  joan  R  0:09     1 adev14
65648     batch  math  phil PD  0:00     6 (Resources)

The scontrol command can be used to report more detailed information about nodes, partitions, jobs, job steps, and configuration. It can also be used by system administrators to make configuration changes. A couple of examples are shown below. See the man page for more information.

adev0: scontrol show partition
PartitionName=debug TotalNodes=5 TotalCPUs=40 RootOnly=NO
   Default=YES OverSubscribe=FORCE:4 PriorityTier=1 State=UP
   MaxTime=00:30:00 Hidden=NO
   MinNodes=1 MaxNodes=26 DisableRootJobs=NO AllowGroups=ALL
   Nodes=adev[1-5] NodeIndices=0-4

PartitionName=batch TotalNodes=10 TotalCPUs=80 RootOnly=NO
   Default=NO OverSubscribe=FORCE:4 PriorityTier=1 State=UP
   MaxTime=16:00:00 Hidden=NO
   MinNodes=1 MaxNodes=26 DisableRootJobs=NO AllowGroups=ALL
   Nodes=adev[6-15] NodeIndices=5-14

adev0: scontrol show node adev1
NodeName=adev1 State=DOWN* CPUs=8 AllocCPUs=0
   RealMemory=4000 TmpDisk=0
   Sockets=2 Cores=4 Threads=1 Weight=1 Features=intel
   Reason=Not responding [slurm@06/02-14:01:24]

65648     batch  math  phil PD  0:00     6 (Resources)
adev0: scontrol show job
JobId=65672 UserId=phil(5136) GroupId=phil(5136)
   Priority=4294901603 Partition=batch BatchFlag=1
   AllocNode:Sid=adev0:16726 TimeLimit=00:10:00 ExitCode=0:0
   StartTime=06/02-15:27:11 EndTime=06/02-15:37:11
   JobState=PENDING NodeList=(null) NodeListIndices=
   NumCPUs=24 ReqNodes=1 ReqS:C:T=1-65535:1-65535:1-65535
   OverSubscribe=1 Contiguous=0 CPUs/task=0 Licenses=(null)
   MinCPUs=1 MinSockets=1 MinCores=1 MinThreads=1
   MinMemory=0 MinTmpDisk=0 Features=(null)
   Dependency=(null) Account=(null) Requeue=1
   Reason=None Network=(null)
   ReqNodeList=(null) ReqNodeListIndices=
   ExcNodeList=(null) ExcNodeListIndices=
   SubmitTime=06/02-15:27:11 SuspendTime=None PreSusTime=0

It is possible to create a resource allocation and launch the tasks for a job step in a single command line using the srun command. Depending upon the MPI implementation used, MPI jobs may also be launched in this manner. See the MPI section for more MPI-specific information. In this example we execute /bin/hostname on three nodes (-N3) and include task numbers on the output (-l). The default partition will be used. One task per node will be used by default. Note that the srun command has many options available to control what resource are allocated and how tasks are distributed across those resources.

adev0: srun -N3 -l /bin/hostname
0: adev3
1: adev4
2: adev5

This variation on the previous example executes /bin/hostname in four tasks (-n4). One processor per task will be used by default (note that we don't specify a node count).

adev0: srun -n4 -l /bin/hostname
0: adev3
1: adev3
2: adev3
3: adev3

One common mode of operation is to submit a script for later execution. In this example the script name is my.script and we explicitly use the nodes adev9 and adev10 (-w "adev[9-10]", note the use of a node range expression). We also explicitly state that the subsequent job steps will spawn four tasks each, which will ensure that our allocation contains at least four processors (one processor per task to be launched). The output will appear in the file my.stdout ("-o my.stdout"). This script contains a timelimit for the job embedded within itself. Other options can be supplied as desired by using a prefix of "#SBATCH" followed by the option at the beginning of the script (before any commands to be executed in the script). Options supplied on the command line would override any options specified within the script. Note that my.script contains the command /bin/hostname that executed on the first node in the allocation (where the script runs) plus two job steps initiated using the srun command and executed sequentially.

adev0: cat my.script
#SBATCH --time=1
srun -l /bin/hostname
srun -l /bin/pwd

adev0: sbatch -n4 -w "adev[9-10]" -o my.stdout my.script
sbatch: Submitted batch job 469

adev0: cat my.stdout
0: adev9
1: adev9
2: adev10
3: adev10
0: /home/jette
1: /home/jette
2: /home/jette
3: /home/jette

The final mode of operation is to create a resource allocation and spawn job steps within that allocation. The salloc command is used to create a resource allocation and typically start a shell within that allocation. One or more job steps would typically be executed within that allocation using the srun command to launch the tasks (depending upon the type of MPI being used, the launch mechanism may differ, see MPI details below). Finally the shell created by salloc would be terminated using the exit command. Slurm does not automatically migrate executable or data files to the nodes allocated to a job. Either the files must exists on local disk or in some global file system (e.g. NFS or Lustre). We provide the tool sbcast to transfer files to local storage on allocated nodes using Slurm's hierarchical communications. In this example we use sbcast to transfer the executable program a.out to /tmp/joe.a.out on local storage of the allocated nodes. After executing the program, we delete it from local storage

tux0: salloc -N1024 bash
$ sbcast a.out /tmp/joe.a.out
Granted job allocation 471
$ srun /tmp/joe.a.out
Result is 3.14159
$ srun rm /tmp/joe.a.out
$ exit
salloc: Relinquishing job allocation 471

In this example, we submit a batch job, get its status, and cancel it.

adev0: sbatch test
srun: jobid 473 submitted

adev0: squeue
  473 batch     test jill R  00:00 1     adev9

adev0: scancel 473

adev0: squeue

Best Practices, Large Job Counts

Consider putting related work into a single Slurm job with multiple job steps both for performance reasons and ease of management. Each Slurm job can contain a multitude of job steps and the overhead in Slurm for managing job steps is much lower than that of individual jobs.

Job arrays are an efficient mechanism of managing a collection of batch jobs with identical resource requirements. Most Slurm commands can manage job arrays either as individual elements (tasks) or as a single entity (e.g. delete an entire job array in a single command).


MPI use depends upon the type of MPI being used. There are three fundamentally different modes of operation used by these various MPI implementations.

  1. Slurm directly launches the tasks and performs initialization of communications through the PMI2 or PMIx APIs. (Supported by most modern MPI implementations.)
  2. Slurm creates a resource allocation for the job and then mpirun launches tasks using Slurm's infrastructure (older versions of OpenMPI).
  3. Slurm creates a resource allocation for the job and then mpirun launches tasks using some mechanism other than Slurm, such as SSH or RSH. These tasks are initiated outside of Slurm's monitoring or control. Slurm's epilog should be configured to purge these tasks when the job's allocation is relinquished. The use of pam_slurm_adopt is also strongly recommended.

Links to instructions for using several varieties of MPI with Slurm are provided below.

Last modified 29 June 2021