See Cluster job schedulers for a description of the different use-cases of a cluster job-scheduler.
Running an interactive job¶
You can start a new interactive job on your Flight Compute cluster by using the
srun command; the scheduler will search for an available compute node, and provide you with an interactive login shell on the node if one is available.
In the above example, the
srun command is used together with two options:
--pty option executes the task in pseudo terminal mode, allowing the session to act like a standard terminal session. The
/bin/bash option is the command that you wish to run - here the default Linux shell, BASH.
srun command can also be executed from an interactive desktop session; the job-scheduler will automatically find an available compute node to launch the job on. Applications launched from within the
srun session are executed on the assigned cluster compute node.
The Slurm scheduler does not automatically set up your session to allow you to run graphical applications inside an interactive session. Once your interactive session has started, you must run the following command before running a graphical application:
When you’ve finished running your application in your interactive session, simply type
logout, or press Ctrl+D to exit the interactive job.
If the job-scheduler could not satisfy the resource you’ve requested for your interactive job (e.g. all your available compute nodes are busy running other jobs), it will report back after a few seconds with an error:
[[email protected](scooby) ~]$ srun --pty /bin/bash srun: job 20 queued and waiting for resources
Submitting a batch job¶
Batch (or non-interactive) jobs allow users to leverage one of the main benefits of having a cluster scheduler; jobs can be queued up with instructions on how to run them and then executed across the cluster while the user does something else. Users submit jobs as scripts, which include instructions on how to run the job - the output of the job (stdout and stderr in Linux terminology) is written to a file on disk for review later on. You can write a batch job that does anything that can be typed on the command-line.
We’ll start with a basic example - the following script is written in bash (the default Linux command-line interpreter). You can create the script yourself using the Nano command-line editor - use the command
nano simplejobscript.sh to create a new file, then type in the contents below. The script does nothing more than print some messages to the screen (the echo lines), and sleeps for 120 seconds. We’ve saved the script to a file called
simplejobscript.sh - the
.sh extension helps to remind us that this is a shell script, but adding a filename extension isn’t strictly necessary for Linux.
#!/bin/bash -l echo "Starting running on host $HOSTNAME" sleep 120 echo "Finished running - goodbye from $HOSTNAME"
We use the
-l option to bash on the first line of the script to request a login session. This ensures that environment modules can be loaded as required as part of your script.
We can execute that script directly on the login node by using the command
bash simplejobscript.sh - after a couple of minutes, we get the following output:
Started running on host login1 Finished running - goodbye from login1
To submit your job script to the cluster job scheduler, use the command
sbatch simplejobscript.sh. The job scheduler should immediately report the job-ID for your job; your job-ID is unique for your current Alces Flight Compute cluster - it will never be repeated once used.
[[email protected](scooby) ~]$ sbatch simplejobscript.sh Submitted batch job 21 [[email protected](scooby) ~]$ ls clusterware-setup-sshkey.log simplejobscript.sh slurm-21.out [[email protected](scooby) ~]$ cat slurm-21.out Starting running on host ip-10-75-1-50 Finished running - goodbye from ip-10-75-1-50
Viewing and controlling queued jobs¶
Once your job has been submitted, use the
squeue command to view the status of the job queue. If you have available compute nodes, your job should be shown in the
R (running) state; if your compute nodes are busy, or you’ve launched an auto-scaling cluster and currently have no running nodes, your job may be shown in the
PD (pending) state until compute nodes are available to run it. If a job is in
PD state - the reason for being unable to run will be displayed in the
NODELIST(REASON) column of the
[[email protected](scooby) ~]$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 41 all simplejo alces R 0:03 1 ip-10-75-1-50 42 all simplejo alces R 0:00 1 ip-10-75-1-50
You can keep running the
squeue command until your job finishes running and disappears from the queue. The output of your batch job will be stored in a file for you to look at. The default location to store the output file is your home directory. You can use the Linux
more command to view your output file:
[[email protected](scooby) ~]$ more slurm-42.out Starting running on host ip-10-75-1-50 Finished running - goodbye from ip-10-75-1-50
Your job runs on whatever node the scheduler can find which is available for use - you can try submitting a bunch of jobs at the same time, and using the
squeue command to see where they run. The scheduler is likely to spread them around over different nodes (if you have multiple nodes). The login node is not included in your cluster for scheduling purposes - jobs submitted to the scheduler will only be run on your cluster compute nodes. You can use the
scancel <job-ID> command to delete a job you’ve submitted, whether it’s running or still in the queued state.
[[email protected](scooby) ~]$ sbatch simplejobscript.sh Submitted batch job 46 [[email protected](scooby) ~]$ sbatch simplejobscript.sh Submitted batch job 47 [[email protected](scooby) ~]$ sbatch simplejobscript.sh Submitted batch job 48 [[email protected](scooby) ~]$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 43 all simplejo alces R 0:04 1 ip-10-75-1-50 44 all simplejo alces R 0:04 1 ip-10-75-1-50 45 all simplejo alces R 0:04 1 ip-10-75-1-152 46 all simplejo alces R 0:04 1 ip-10-75-1-152 47 all simplejo alces R 0:04 1 ip-10-75-1-163 48 all simplejo alces R 0:04 1 ip-10-75-1-163 [[email protected](scooby) ~]$ scancel 47 [[email protected](scooby) ~]$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 43 all simplejo alces R 0:11 1 ip-10-75-1-50 44 all simplejo alces R 0:11 1 ip-10-75-1-50 45 all simplejo alces R 0:11 1 ip-10-75-1-152 46 all simplejo alces R 0:11 1 ip-10-75-1-152 48 all simplejo alces R 0:11 1 ip-10-75-1-163
Viewing compute host status¶
Users can use the
sinfo -Nl command to view the status of compute node hosts in your Flight Compute cluster.
[[email protected](scooby) ~]$ sinfo -Nl Fri Aug 26 14:46:34 2016 NODELIST NODES PARTITION STATE CPUS S:C:T MEMORY TMP_DISK WEIGHT AVAIL_FE REASON ip-10-75-1-50 1 all* idle 2 2:1:1 3602 20462 1 (null) none ip-10-75-1-152 1 all* idle 2 2:1:1 3602 20462 1 (null) none ip-10-75-1-163 1 all* idle 2 2:1:1 3602 20462 1 (null) none ip-10-75-1-203 1 all* idle 2 2:1:1 3602 20462 1 (null) none ip-10-75-1-208 1 all* idle 2 2:1:1 3602 20462 1 (null) none ip-10-75-1-240 1 all* idle 2 2:1:1 3602 20462 1 (null) none ip-10-75-1-246 1 all* idle 2 2:1:1 3602 20462 1 (null) none
sinfo output will show (from left-to-right):
- The hostname of your compute nodes
- The number of nodes in the list
- The node partition the node belongs to
- Current usage of the node - if no jobs are running, the state will be listed as
idle. If a job is running, the state will be listed as
- The detected number of CPUs (including hyper-threaded cores)
- The number of sockets, cores and threads per node
- The amount of memory in MB per node
- The amount of disk space in MB available to the /tmp partition per node
- The scheduler weighting
In order to promote efficient usage of your cluster, the job-scheduler automatically sets a number of default resources for your jobs when you submit them. These defaults must be overridden by users to help the scheduler understand how you want it to run your job - if we don’t include any instructions to the scheduler, then our job will take the defaults shown below:
- Number of CPU cores for your job:
- Number of nodes for your job: the default behavior is to allocate enough nodes to satisfy the requirements of the number of CPUs requested
You can view all default resource limits by running the following command:
[root@login1(slurm) ~]# scontrol show config | grep Def CpuFreqDef = Unknown DefMemPerNode = UNLIMITED MpiDefault = none SallocDefaultCommand = (null)
This documentation will explain how to change these limits to suit the jobs that you want to run. You can also disable these limits if you prefer to control resource allocation manually by yourself.
In order to promote efficient usage of the cluster - the job-scheduler is automatically configured with default run-time limits for jobs. These defaults can be overridden by users to help the scheduler understand how you want it to run your job. If we don’t include any instructions to the scheduler then the default limits are applied to a job.
Job instructions can be provided in two ways; they are:
- On the command line, as parameters to your
sruncommand. For example, you can set the name of your job using the
--job-name=[name] | -J [name]option:
- In your job script, by including scheduler directives at the top of your job script - you can achieve the same effect as providing options with the
sruncommands. Create an example job script or modify your existing script to include a scheduler directive to use a specified job name:
#!/bin/bash -l #SBATCH --job-name=mytestjob echo "Starting running on host $HOSTNAME" sleep 120 echo "Finished running - goodbye from $HOSTNAME"
Including job scheduler instructions in your job-scripts is often the most convenient method of working for batch jobs - follow the guidelines below for the best experience:
- Lines in your script that include job-scheduler directives must start with
#SBATCHat the beginning of the line
- You can have multiple lines starting with
#SBATCHin your job-script, with normal script lines in-between
- You can put multiple instructions separated by a space on a single line starting with
- The scheduler will parse the script from top to bottom and set instructions in order; if you set the same parameter twice, the second value will be used.
- Instructions are parsed at job submission time, before the job itself has actually run. This means you can’t, for example, tell the scheduler to put your job output in a directory that you create in the job-script itself - the directory will not exist when the job starts running, and your job will fail with an error.
- You can use dynamic variables in your instructions (see below)
Dynamic scheduler variables¶
Your cluster job scheduler automatically creates a number of pseudo environment variables which are available to your job-scripts when they are running on cluster compute nodes, along with standard Linux variables. Useful values include the following:
$HOMEThe location of your home-directory
$USERThe Linux username of the submitting user
$HOSTNAMEThe Linux hostname of the compute node running the job
%a / $SLURM_ARRAY_TASK_IDJob array ID (index) number. The
%asubstitution should only be used in your job scheduler directives
%A / $SLURM_ARRAY_JOB_IDJob allocation number for an array job. The
%Asubstitution should only be used in your job scheduler directives
%j / $SLURM_JOBIDJob allocation number. The
%jsubstitution should only be used in your job scheduler directives
Simple scheduler instruction examples¶
Here are some commonly used scheduler instructions, along with some example of their usage:
Setting output file location¶
To set the output file location for your job, use the
-o [file_name] | --output=[file_name] option - both standard-out and standard-error from your job-script, including any output generated by applications launched by your job-script will be saved in the filename you specify.
By default, the scheduler stores data relative to your home-directory - but to avoid confusion, we recommend specifying a full path to the filename to be used. Although Linux can support several jobs writing to the same output file, the result is likely to be garbled - it’s common practice to include something unique about the job (e.g. it’s job-ID) in the output filename to make sure your job’s output is clear and easy to read.
The directory used to store your job output file must exist and be writable by your user before you submit your job to the scheduler. Your job may fail to run if the scheduler cannot create the output file in the directory requested.
The following example uses the
--output=[file_name] instruction to set the output file location:
#!/bin/bash -l #SBATCH --job-name=myjob --output=output.%j echo "Starting running on host $HOSTNAME" sleep 120 echo "Finished running - goodbye from $HOSTNAME"
In the above example, assuming the job was submitted as the
alces user and was given the job-ID number
24, the scheduler will save the output data from the job in the filename
Setting working directory for your job¶
By default, jobs are executed from your home-directory on the cluster (i.e.
~). You can include
cd commands in your job-script to change to different directories; alternatively, you can provide an instruction to the scheduler to change to a different directory to run your job. The available options are:
-D | --workdir=[dir_name]- instruct the job scheduler to move into the directory specified before starting to run the job on a compute node
The directory specified must exist and be accessible by the compute node in order for the job you submitted to run.
Waiting for a previous job before running¶
You can instruct the scheduler to wait for an existing job to finish before starting to run the job you are submitting with the
-d [state:job_id] | --depend=[state:job_id] option. For example, to wait until the job with ID 75 has finished before starting the job, you could use the following syntax:
[[email protected](scooby) ~]$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 75 all myjob alces R 0:01 1 ip-10-75-1-50 [[email protected](scooby) ~]$ sbatch --dependency=afterok:75 mytestjob.sh Submitted batch job 76 [[email protected](scooby) ~]$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 76 all myjob alces PD 0:00 1 (Dependency) 75 all myjob alces R 0:15 1 ip-10-75-1-50
Running task array jobs¶
A common workload is having a large number of jobs to run which basically do the same thing, aside perhaps from having different input data. You could generate a job-script for each of them and submit it, but that’s not very convenient - especially if you have many hundreds or thousands of tasks to complete. Such jobs are known as task arrays - an embarrassingly parallel job will often fit into this category.
A convenient way to run such jobs on a cluster is to use a task array, using the
-a [array_spec] | --array=[array_spec] directive. Your job-script can then use the pseudo environment variables created by the scheduler to refer to data used by each task in the job. The following job-script uses the
%a variable to echo its current task ID to an output file:
#!/bin/bash -l #SBATCH --job-name=array #SBATCH -D $HOME/ #SBATCH --output=output.array.%A.%a #SBATCH --array=1-1000 echo "I am $SLURM_ARRAY_TASK_ID from job $SLURM_ARRAY_JOB_ID"
[[email protected](scooby) ~]$ sbatch arrayjob.sh Submitted batch job 77 [[email protected](scooby) ~]$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 77_[85-1000] all array alces PD 0:00 1 (Resources) 77_71 all array alces R 0:00 1 ip-10-75-1-163 77_72 all array alces R 0:00 1 ip-10-75-1-240 77_73 all array alces R 0:00 1 ip-10-75-1-163 77_74 all array alces R 0:00 1 ip-10-75-1-240 77_75 all array alces R 0:00 1 ip-10-75-1-246 77_76 all array alces R 0:00 1 ip-10-75-1-246 77_77 all array alces R 0:00 1 ip-10-75-1-208 77_78 all array alces R 0:00 1 ip-10-75-1-208 77_79 all array alces R 0:00 1 ip-10-75-1-152 77_80 all array alces R 0:00 1 ip-10-75-1-203 77_81 all array alces R 0:00 1 ip-10-75-1-50 77_82 all array alces R 0:00 1 ip-10-75-1-50 77_83 all array alces R 0:00 1 ip-10-75-1-152 77_84 all array alces R 0:00 1 ip-10-75-1-203
All tasks in an array job are given a job ID with the format
77_81 would be job number 77, array task 81.
Array jobs can easily be cancelled using the
scancel command - the following examples show various levels of control over an array job:
- Cancels all array tasks under the job ID
- Cancels array tasks
100-200under the job ID
- Cancels array task
5under the job ID
Requesting more resources¶
By default, jobs are constrained to the default set of resources - users can use scheduler instructions to request more resources for their jobs. The following documentation shows how these requests can be made.
Running multi-threaded jobs¶
If users want to use multiple cores on a compute node to run a multi-threaded application, they need to inform the scheduler - this allows jobs to use multiple cores without needing to rely on any interconnect. Using multiple CPU cores is achieved by specifying the
-n, --ntasks=<number> option in either your submission command or the scheduler directives in your job script. The
--ntasks option informs the scheduler of the number of cores you wish to reserve for use. If the parameter is omitted, the default
--ntasks=1 is assumed. You could specify the option
-n 4 to request 4 CPU cores for your job. Besides the number of tasks, you will need to add
--nodes=1 to your scheduler command or at the top of your job script with
#SBATCH --nodes=1, this will set the maximum number of nodes to be used to 1 and prevent the job selecting cores from multiple nodes.
If you request more cores than are available on a node in your cluster, the job will not run until a node capable of fulfilling your request becomes available. The scheduler will display the error in the output of the
Running Parallel (MPI) jobs¶
If users want to run parallel jobs via a messaging passing interface (MPI), they need to inform the scheduler - this allows jobs to be efficiently spread over compute nodes to get the best possible performance. Using multiple CPU cores across multiple nodes is achieved by specifying the
-N, --nodes=<minnodes[-maxnodes]> option - which requests a minimum (and optional maximum) number of nodes to allocate to the submitted job. If only the
minnodes count is specified - then this is used for both the minimum and maximum node count for the job.
You can request multiple cores over multiple nodes using a combination of scheduler directives either in your job submission command or within your job script. Some of the following examples demonstrate how you can obtain cores across different resources;
- Requests 16 cores across 2 compute nodes
- Requests all available cores of 2 compute nodes
- Requests 16 cores across any available compute nodes
For example, to use 64 CPU cores on the cluster for a single application, the instruction
--ntasks=64 can be used. The following example shows launching the Intel Message-passing MPI benchmark across 64 cores on your cluster. This application is launched via the OpenMPI
mpirun command - the number of threads and list of hosts are automatically assembled by the scheduler and passed to the MPI at runtime. This jobscript loads the
apps/imb module before launching the
application, which automatically loads the module for OpenMPI.
#!/bin/bash -l #SBATCH -n 64 #SBATCH --job-name=imb #SBATCH -D $HOME/ #SBATCH --output=imb.out.%j module load apps/imb mpirun --prefix $MPI_HOME \ IMB-MPI1
We can then submit the IMB job script to the scheduler, which will automatically determine which nodes to use:
[[email protected](scooby) ~]$ sbatch imb.sh Submitted batch job 1162 [[email protected](scooby) ~]$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 1162 all imb alces R 0:01 8 ip-10-75-1-[42,45,62,67,105,178,233,250] [[email protected](scooby) ~]$ cat imb.out.1162 #------------------------------------------------------------ # Intel (R) MPI Benchmarks 4.0, MPI-1 part #------------------------------------------------------------ # Date : Tue Aug 30 10:34:08 2016 # Machine : x86_64 # System : Linux # Release : 3.10.0-327.28.3.el7.x86_64 # Version : #1 SMP Thu Aug 18 19:05:49 UTC 2016 # MPI Version : 3.0 # MPI Thread Environment: #--------------------------------------------------- # Benchmarking PingPong # #processes = 2 # ( 62 additional processes waiting in MPI_Barrier) #--------------------------------------------------- #bytes #repetitions t[usec] Mbytes/sec 0 1000 3.17 0.00 1 1000 3.20 0.30 2 1000 3.18 0.60 4 1000 3.19 1.19 8 1000 3.26 2.34 16 1000 3.22 4.74 32 1000 3.22 9.47 64 1000 3.21 19.04 128 1000 3.22 37.92 256 1000 3.30 73.90 512 1000 3.41 143.15 1024 1000 3.55 275.36 2048 1000 3.75 521.04 4096 1000 10.09 387.14 8192 1000 11.12 702.51 16384 1000 12.06 1296.04 32768 1000 14.65 2133.32 65536 640 19.30 3238.72 131072 320 29.50 4236.83 262144 160 48.17 5189.77 524288 80 84.36 5926.88 1048576 40 157.40 6353.32 2097152 20 305.00 6557.31 4194304 10 675.20 5924.16
If you request more CPU cores than your cluster can accommodate, your job will wait in the queue. If you are using the Flight Compute auto-scaling feature, your job will start to run once enough new nodes have been launched.
Requesting more memory¶
In order to promote best use of the cluster scheduler - particularly in a shared environment, it is recommended to inform the scheduler the maximum required memory per submitted job. This helps the scheduler appropriately place jobs on the available nodes in the cluster.
You can specify the maximum amount of memory required per submitted job with the
--mem=<MB> option. This informs the scheduler of the memory required for the submitted job. Optionally - you can also request an amount of memory per CPU core rather than a total amount of memory required per job. To specify an amount of memory to allocate per core, use the
When running a job across multiple compute hosts, the
--mem=<MB> option informs the scheduler of the required memory per node
Requesting a longer runtime¶
In order to promote best-use of the cluster scheduler, particularly in a shared environment, it is recommend to inform the scheduler the amount of time the submitted job is expected to take. You can inform the cluster scheduler of the expected runtime using the
-t, --time=<time> option. For example - to submit a job that runs for 2 hours, the following example job script could be used:
#!/bin/bash -l #SBATCH --job-name=sleep #SBATCH -D $HOME/ #SBATCH --time=0-2:00 sleep 7200
You can then see any time limits assigned to running jobs using the command
[[email protected](scooby) ~]$ squeue --long Tue Aug 30 10:55:55 2016 JOBID PARTITION NAME USER STATE TIME TIME_LIMI NODES NODELIST(REASON) 1163 all sleep alces RUNNING 0:07 2:00:00 1 ip-10-75-1-42
This guide is a quick overview of some of the many available options of the SLURM cluster scheduler. For more information on the available options, you may wish to reference some of the following available documentation for the demonstrated SLURM commands;
- Use the
man squeuecommand to see a full list of scheduler queue instructions
- Use the
man sbatch/sruncommand to see a full list of scheduler submission instructions
- Online documentation for the SLURM scheduler is available here