This page is intended for the user who is already familiar with the concepts of job scheduling and job scripts, and who wants guidance on submitting jobs to UofC RCS clusters.
If you have not worked on a large shared computer cluster before, you should probably read What is a scheduler? first.
If you are coming to Slurm from PBS/Torque, SGE, LSF, or LoadLeveler, you might find this table of corresponding commands useful.
sbatch to submit jobs
The command to submit a job is
$ sbatch simple_job.sh Submitted batch job 123456
A minimal Slurm job script looks like this: Template:File
On general-purpose (GP) clusters this job reserves 1 core and 256MB of memory for 15 minutes. On Niagara this job reserves the whole node with all its memory.
Directives (or "options") in the job script are prefixed with
#SBATCH and must precede all executable commands. All available directives are described on the sbatch page. Compute Canada policies require that you supply at least a time limit (
--time) for each job. You may also need to supply an account name (
--account). See Accounts and projects below.
You can also specify directives as command-line arguments to
sbatch. So for example,
$ sbatch --time=00:30:00 simple_job.sh
will submit the above job script with a time limit of 30 minutes. The acceptable time formats include "minutes", "minutes:seconds", "hours:minutes:seconds", "days-hours", "days-hours:minutes" and "days-hours:minutes:seconds". Please note that the time limit will strongly affect how quickly the job is started, since longer jobs are eligible to run on fewer nodes.
Please be cautious if you use a script to submit multiple Slurm jobs in a short time. Submitting thousands of jobs at a time can cause Slurm to become unresponsive to other users. Consider using an array job instead, or use
sleep to space out calls to
sbatch by one second or more.
Memory may be requested with
--mem-per-cpu (memory per core) or
--mem (memory per node). On general-purpose (GP) clusters a default memory amount of 256 MB per core will be allocated unless you make some other request. At Niagara only whole nodes are allocated along with all available memory, so a memory specification is not required there.
A common source of confusion comes from the fact that some memory on a node is not available to the job (reserved for the OS, etc). The effect of this is that each node-type has a maximum amount available to jobs - for instance, nominally "128G" nodes are typically configured to permit 125G of memory to user jobs. If you request more memory than a node-type provides, your job will be constrained to run on higher-memory nodes, which may be fewer in number.
Adding to this confusion, Slurm interprets K, M, G, etc., as binary prefixes, so
--mem=125G is equivalent to
--mem=128000M. See the "available memory" column in the "Node characteristics" table for each GP cluster for the Slurm specification of the maximum memory you can request on each node: Béluga, Cedar, Graham.
sq to list jobs
The general command for checking the status of Slurm jobs is
squeue, but by default it supplies information about all jobs in the system, not just your own. You can use the shorter
sq to list only your own jobs:
$ sq JOBID USER ACCOUNT NAME ST TIME_LEFT NODES CPUS GRES MIN_MEM NODELIST (REASON) 123456 smithj def-smithj simple_j R 0:03 1 1 (null) 4G cdr234 (None) 123457 smithj def-smithj bigger_j PD 2-00:00:00 1 16 (null) 16G (Priority)
The ST column of the output shows the status of each job. The two most common states are "PD" for "pending" or "R" for "running".
If you want to know more about the output of
squeue, or learn how to change the output, see the online manual page for squeue.
sq is a Compute Canada customization.
Do not run
squeue from a script or program at high frequency, e.g., every few seconds. Responding to
squeue adds load to Slurm, and may interfere with its performance or correct operation. See Email notification below for a much better way to learn when your job starts or ends.
Where does the output go?
By default the output is placed in a file named "slurm-", suffixed with the job ID number and ".out", e.g.
slurm-123456.out, in the directory from which the job was submitted.
You can use
--output to specify a different name or location.
Certain replacement symbols can be used in the filename, e.g.
%j will be replaced
by the job ID number. See sbatch for a complete list.
The following sample script sets a job name (which appears in
squeue output) and sends the output to a file with a name constructed from the job name (%x) and the job ID number (%j).
Error output will normally appear in the same file as standard output, just as it would if you were typing commands interactively. If you want to send the standard error channel (stderr) to a separate file, use
Accounts and projects
Every job must have an associated account name corresponding to a Compute Canada Resource Allocation Project (RAP).
If you try to submit a job with
sbatch and receive one of these messages:
You are associated with multiple _cpu allocations... Please specify one of the following accounts to submit this job: You are associated with multiple _gpu allocations... Please specify one of the following accounts to submit this job:
then you have more than one valid account, and you will have to specify one
To find out which account name corresponds
to a given Resource Allocation Project, log in to CCDB
and click on "My Account -> Account Details". You will see a list of all the projects
you are a member of. The string you should use with the
a given project is under the column Group Name. Note that a Resource
Allocation Project may only apply to a specific cluster (or set of clusters) and therefore
may not be transferable from one cluster to another.
In the illustration below, jobs submitted with
--account=def-rdickson will be accounted against RAP wnp-003-aa.
If you plan to use one account consistently for all jobs, once you have determined the right account name you may find it convenient to set the following three environment variables in your
export SLURM_ACCOUNT=def-someuser export SBATCH_ACCOUNT=$SLURM_ACCOUNT export SALLOC_ACCOUNT=$SLURM_ACCOUNT
Slurm will use the value of
SBATCH_ACCOUNT in place of the
--account directive in the job script. Note that even if you supply an account name inside the job script, the environment variable takes priority. In order to override the environment variable you must supply an account name as a command-line argument to
SLURM_ACCOUNT plays the same role as
SBATCH_ACCOUNT, but for the
srun command instead of
sbatch. The same idea holds for
Examples of job scripts
A serial job is a job which only requests a single core. It is the simplest type of job. The "simple_job.sh" which appears above in "Use sbatch to submit jobs" is an example.
Also known as a task array, an array job is a way to submit a whole set of jobs with one command. The individual jobs in the array are distinguished by an environment variable,
$SLURM_ARRAY_TASK_ID, which is set to a different value for each instance of the job. The following example will create 10 tasks, with values of
$SLURM_ARRAY_TASK_ID ranging from 1 to 10:
Threaded or OpenMP job
This example script launches a single process with eight CPU cores. Bear in mind that for an application to use OpenMP it must be compiled with the appropriate flag, e.g.
gcc -fopenmp ... or
icc -openmp ...
This example script launches four MPI processes, each with 1024 MB of memory. The run time is limited to 5 minutes.
Large MPI jobs, specifically those which can efficiently use whole nodes, should use
--ntasks-per-node instead of
--ntasks. Hybrid MPI/threaded jobs are also possible. For more on these and other options relating to distributed parallel jobs, see Advanced MPI scheduling.
For more on writing and running parallel programs with OpenMP, see OpenMP.
There are many options involved in requesting GPUs because
- the GPU-equipped nodes at Cedar and Graham have different configurations,
- there are two different configurations at Cedar, and
- there are different policies for the different Cedar GPU nodes.
Please see Using GPUs with Slurm for a discussion and examples of how to schedule various job types on the available GPU resources.
Though batch submission is the most common and most efficient way to take advantage of our clusters, interactive jobs are also supported. These can be useful for things like:
- Data exploration at the command line
- Interactive "console tools" like R and iPython
- Significant software development, debugging, or compiling
You can start an interactive session on a compute node with salloc. In the following example we request two tasks, which corresponds to two CPU cores, for an hour:
$ salloc --time=1:0:0 --ntasks=2 --account=def-someuser salloc: Granted job allocation 1234567 $ ... # do some work $ exit # terminate the allocation salloc: Relinquishing job allocation 1234567
It is also possible to run graphical programs interactively on a compute node by adding the --x11 flag to your salloc command. In order for this to work, you must first connect to the cluster with X11 forwarding enabled (see the SSH page for instructions on how to do that).
By default squeue will show all the jobs the scheduler is managing at the moment. It will run much faster if you ask only about your own jobs with
$ squeue -u $USER
You can also use the utility
sq to do the same thing with less typing.
You can show only running jobs, or only pending jobs:
$ squeue -u <username> -t RUNNING $ squeue -u <username> -t PENDING
You can show detailed information for a specific job with scontrol:
$ scontrol show job -dd <jobid>
Do not run
squeue from a script or program at high frequency, e.g., every few seconds. Responding to
squeue adds load to Slurm, and may interfere with its performance or correct operation.
You can ask to be notified by email of certain job conditions by supplying options to sbatch:
#SBATCH --mail-user=<email_address> #SBATCH --mail-type=BEGIN #SBATCH --mail-type=END #SBATCH --mail-type=FAIL #SBATCH --mail-type=REQUEUE #SBATCH --mail-type=ALL
Output from a non-interactive Slurm job is normally buffered, which means that there is usually a delay between when data is written by the job and when you can see the output on a login node. Depending on the application you are running and the load on the filesystem, this delay can range from less than a second to many minutes, or until the job completes.
There are methods to reduce or eliminate the buffering, but we do not recommend using them because buffering is vital to preserving the overall performance of the filesystem. If you need to monitor the output from a job in "real time", we recommend you run an interactive job as described above.
Get a short summary of the CPU- and memory-efficiency of a job with
$ seff 12345678 Job ID: 12345678 Cluster: cedar User/Group: jsmith/jsmith State: COMPLETED (exit code 0) Cores: 1 CPU Utilized: 02:48:58 CPU Efficiency: 99.72% of 02:49:26 core-walltime Job Wall-clock time: 02:49:26 Memory Utilized: 213.85 MB Memory Efficiency: 0.17% of 125.00 GB
Find more detailed information about a completed job with sacct, and optionally, control what it prints using
$ sacct -j <jobid> $ sacct -j <jobid> --format=JobID,JobName,MaxRSS,Elapsed
The output from
sacct typically includes records labelled
.ext+, and possibly
.0, .1, .2, ....
The batch step (
.bat+) is your submission script - for many jobs that's where the main part of the work is done and where the resources are consumed.
If you use
srun in your submission script, that would create a
.0 step that would consume most of the resources.
The extern (
.ext+) step is basically prologue and epilogue and normally doesn't consume any significant resources.
If a node fails while running a job, the job may be restarted.
sacct will normally show you only the record for the last (presumably successful) run. If you wish to see all records related to a given job, add the
Use the MaxRSS accounting field to determine how much memory a job needed. The value returned will be the largest resident set size for any of the tasks. If you want to know which task and node this occurred on, print the MaxRSSTask and MaxRSSNode fields also.
Attaching to a running job
It is possible to connect to the node running a job and execute new processes there. You might want to do this for troubleshooting or to monitor the progress of a job.
Suppose you want to run the utility
nvidia-smi to monitor GPU usage on a node where you have a job running. The following command runs
watch on the node assigned to the given job, which in turn runs
nvidia-smi every 30 seconds, displaying the output on your terminal.
$ srun --jobid 123456 --pty watch -n 30 nvidia-smi
It is possible to launch multiple monitoring commands using
tmux. The following command launches
nvidia-smi in separate panes to monitor the activity on a node assigned to the given job.
$ srun --jobid 123456 --pty tmux new-session -d 'htop -u $USER' \; split-window -h 'watch nvidia-smi' \; attach
Processes launched with
srun share the resources with the job specified. You should therefore be careful not to launch processes that would use a significant portion of the resources allocated for the job. Using too much memory, for example, might result in the job being killed; using too many CPU cycles will slow down the job.
srun commands shown above work only to monitor a job submitted with
sbatch. To monitor an interactive job, create multiple panes with
tmux and start each process in its own pane.
Use scancel with the job ID to cancel a job:
$ scancel <jobid>
You can also use it to cancel all your jobs, or all your pending jobs:
$ scancel -u $USER $ scancel -t PENDING -u $USER
Resubmitting jobs for long running computations
When a computation is going to require a long time to complete, so long that it cannot be done within the time limits on the system, the application you are running must support checkpointing. The application should be able to save its state to a file, called a checkpoint file, and then it should be able to restart and continue the computation from that saved state.
For many users restarting a calculation will be rare and may be done manually, but some workflows require frequent restarts. In this case some kind of automation technique may be employed.
Here are two recommended methods of automatic restarting:
- Using SLURM job arrays.
- Resubmitting from the end of the job script.
Restarting using job arrays
--array=1-100%10 syntax mentioned earlier one can submit a collection of identical jobs with the condition that only one job of them will run at any given time.
The script should be written to ensure that the last checkpoint is always used for the next job. The number of restarts is fixed by the
Consider, for example, a molecular dynamics simulations that has to be run for 1 000 000 steps, and such simulation does not fit into the time limit on the cluster. We can split the simulation into 10 smaller jobs of 100 000 steps, one after another.
An example of using a job array to restart a simulation: Template:File
Resubmission from the job script
In this case one submits a job that runs the first chunk of the calculation and saves a checkpoint. Once the chunk is done but before the allocated run-time of the job has elapsed, the script checks if the end of the calculation has been reached. If the calculation is not yet finished, the script submits a copy of itself to continue working.
An example of a job script with resubmission: Template:File
Please note: The test to determine whether to submit a followup job, abbreviated as
work_should_continue in the above example, should be a positive test. There may be a temptation to test for a stopping condition (e.g. is some convergence criterion met?) and submit a new job if the condition is not detected. But if some error arises that you didn't foresee, the stopping condition might never be met and your chain of jobs may continue indefinitely, doing nothing useful.
There are certain differences in the job scheduling policies from one Compute Canada cluster to another and these are summarized by tab in the following section:
<tabs> <tab name="Cedar"> Jobs may not be submitted from directories on the /home filesystem on Cedar. This is to reduce the load on that filesystem and improve the responsiveness for interactive work. If the command readlink -f $(pwd) | cut -d/ -f2 returns home, you are not permitted to submit jobs from that directory. Transfer the files from that directory either to a /project or /scratch directory and submit the job from there.
Cedar has two distinct CPU architectures available: Broadwell and Skylake. Users requiring a specific architecture can request it when submitting a job using the
--constraint flag. Note that the names should be written all in lower-case,
An example job requesting the
skylake feature on Cedar:
#!/bin/bash #SBATCH --account=def-someuser #SBATCH --constraint=skylake #SBATCH --time=0:5:0 # Display CPU-specific information with 'lscpu'. # Skylake CPUs will have 'avx512f' in the 'Flags' section of the output. lscpu
Keep in mind that a job which would have obtained an entire node for itself by specifying for example #SBATCH --cpus-per-task=32 will now share the remaining 16 CPU cores with another job if it happens to use a Skylake node; if you wish to reserve the entire node you will need to request all 48 cores or add the #SBATCH --constraint=broadwell option to your job script.
If you are unsure if your job requires a specific architecture, do not use this option. Jobs that do not specify a CPU architecture can be scheduled on either Broadwell or Skylake nodes, and will therefore generally start earlier. </tab> <tab name="Niagara">
Scheduling is by node, so in multiples of 40-cores.
Your job's maximum walltime is 24 hours.
Jobs must write to your scratch or project directory (home is read-only on compute nodes).
Compute nodes have no internet access.
Move your data to Niagara before you submit your job.
Preparing a job script with a word processor instead of a text editor is a common cause of trouble. Best practice is to prepare your job script on the cluster using an editor such as nano, vim, or emacs. If you prefer to prepare or alter the script off-line, then:
- Windows users:
- Use a text editor such as Notepad or Notepad++.
- After uploading the script, use
dos2unixto change Windows end-of-line characters to Linux end-of-line characters.
- Mac users:
- Open a terminal window and use an editor such as nano, vim, or emacs.
Cancellation of jobs with dependency conditions which cannot be met
A job submitted with
--dependency=afterok:<jobid> is a "dependent job". A dependent job will wait for the parent job to be completed. If the parent job fails (that is, ends with a non-zero exit code) the dependent job can never be scheduled and so will be automatically cancelled. See sbatch for more on dependency.
Job cannot load a module
It is possible to see an error such as:
Lmod has detected the following error: These module(s) exist but cannot be
loaded as requested: "<module-name>/<version>" Try: "module spider <module-name>/<version>" to see how to load the module(s).
This can occur if the particular module has an unsatisfied prerequisite. For example
$ module load gcc $ module load quantumespresso/6.1 Lmod has detected the following error: These module(s) exist but cannot be loaded as requested: "quantumespresso/6.1" Try: "module spider quantumespresso/6.1" to see how to load the module(s). $ module spider quantumespresso/6.1 ----------------------------------------- quantumespresso: quantumespresso/6.1 ------------------------------------------ Description: Quantum ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials (both norm-conserving and ultrasoft). Properties: Chemistry libraries/apps / Logiciels de chimie You will need to load all module(s) on any one of the lines below before the "quantumespresso/6.1" module is available to load. nixpkgs/16.09 intel/2016.4 openmpi/2.1.1 Help: Description =========== Quantum ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials (both norm-conserving and ultrasoft). More information ================ - Homepage: http://www.pwscf.org/
In this case adding the line
module load nixpkgs/16.09 intel/2016.4 openmpi/2.1.1 to your job script before loading the "quantumespresso/6.1" will solve this problem.
Jobs inherit environment variables
By default a job will inherit the environment variables of the shell where the job was submitted. The module command, which is used to make various software packages available, changes and sets environment variables. Changes will propagate to any job submitted from the shell and thus could affect the job's ability to load modules if there are missing prerequisites. It is best to include the line
module purge in your job script before loading all the required modules to ensure a consistent state for each job submission and avoid changes made in your shell affecting your jobs.
Inheriting environment settings from the submitting shell can sometimes lead to hard-to-diagnose problems. If you wish to suppress this inheritance, use the
--export=none directive when submitting jobs.
Job hangs / no output
Sometimes a submitted job writes no output to the log file for an extended period of time, looking like it is hanging. A common reason for this is the aggressive buffering performed by the Slurm scheduler, which will aggregate many output lines before flushing them to the log file. Often the output file will only be written after the job completes. If you wish to monitor the progress of your submitted job as it runs, consider running an interactive job
Another option is to redirect output to a separate file. For example, in your submit file you may run your, e.g. python, job as
python myscript.py > mylog.txt. If you wish for the output to also be present in the Slurm log, you may also use the
tee command, as follows:
python myscript.py | tee mylog.txt.
Job status and priority
- For a discussion of how job priority is determined and how things like time limits may affect the scheduling of your jobs at Cedar and Graham, see Job scheduling policies.
- Comprehensive documentation is maintained by SchedMD, as well as some tutorials.
- sbatch command options
- There is also a "Rosetta stone" mapping commands and directives from PBS/Torque, SGE, LSF, and LoadLeveler, to SLURM.
- Here is a text tutorial from CÉCI, Belgium
- Here is a rather minimal text tutorial from Bright Computing