ARCHER2 scheduler: Slurm
Overview
Teaching: 40 min
Exercises: 20 minQuestions
How do I write job submission scripts?
How do I control jobs?
How do I find out what resources are available?
Objectives
Understand the use of the basic Slurm commands.
Know what components make up and ARCHER2 scheduler.
Know where to look for further help on the scheduler.
ARCHER2 uses the Slurm job submission system, or scheduler, to manage resources and how they are made available to users. The main commands you will use with Slurm on ARCHER2 are:
sinfo: Query the current state of nodessbatch: Submit non-interactive (batch) jobs to the schedulersqueue: List jobs in the queuescancel: Cancel a jobsalloc: Submit interactive jobs to the schedulersrun: Used within a batch job script or interactive job session to start a parallel program
Full documentation on Slurm on ARCHER2 can be found in the Running Jobs on ARCHER2 section of the ARCHER2 documentations.
Finding out what resources are available: sinfo
The sinfo command shows the current state of the compute nodes known to the scheduler:
auser@uan01:~> sinfo
PARTITION AVAIL TIMELIMIT NODES STATE NODELIST
standard up 1-00:00:00 231 alloc nid[001001,001005,001010-001015,001018-001020,001022-001028,001034-001044,001048-001049,001051-001060,001062-001067,001071-001072,001075-001091,001093-001108,001110,001112,001115-001124,001139-001143,001150-001153,001155-001158,001161-001162,001164-001169,001200-001202,001246-001249,001256-001261,001319-001325,001327-001333,001348-001349,001367-001368,001463-001467,001469-001474,001540-001545,001547-001551,001646,001648-001651,001665-001668,001710-001718,001720-001721,001724-001728,001748-001750,001756,001818-001824,001826-001831,001920-001931,001945-001949,001951-001954]
standard up 1-00:00:00 669 idle nid[001000,001002-001004,001007-001009,001030-001032,001073,001126-001137,001145-001148,001154,001170,001175-001199,001204-001226,001229-001240,001245,001263-001272,001274-001286,001288-001317,001334-001335,001337-001346,001352-001365,001370-001383,001396-001434,001436-001461,001475-001477,001479-001489,001491-001504,001506-001519,001522-001538,001553-001580,001582-001613,001615-001641,001653-001663,001670-001688,001690-001708,001730-001746,001752-001755,001758-001781,001783-001809,001832,001840-001890,001893-001902,001904-001918,001933-001943,001956-001968,001970-002013,002015-002023]
There is a row for each node state and partition combination. The default output shows the following columns:
PARTITION- The system partitionAVAIL- The status of the partition -upin normal operationTIMELIMIT- Maximum runtime asdays-hours:minutes:seconds: on ARCHER2, these are set using QoS (Quality of Service) rather than on partitionsNODES- The number of nodes in the partition/state combinationSTATE- The state of the listed nodes (more information below)NODELIST- A list of the nodes in the partition/state combination
The nodes can be in many different states, the most common you will see are:
idle- Nodes that are not currently allocated to jobsalloc- Nodes currently allocated to jobsdraining- Nodes draining and will not run further jobs until released by the systems teamdown- Node unavailablefail- Node is in fail state and not available for jobsreserved- Node is in an advanced reservation and is not generally availablemaint- Node is in a maintenance reservation and is not generally available
If you prefer to see the state of individual nodes, you can use the sinfo -N -l command.
Lots to look at!
Warning! The
sinfo -N -lcommand will produce a lot of output as there are over 5000 individual nodes on ARCHER2!
auser@uan01:~> sinfo -N -l
Thu Oct 29 21:22:08 2020
NODELIST NODES PARTITION STATE CPUS S:C:T MEMORY TMP_DISK WEIGHT AVAIL_FE REASON
nid001000 1 standard idle 256 2:64:2 256000 0 1 (null) none
nid001001 1 standard allocated 256 2:64:2 256000 0 1 (null) none
nid001002 1 standard idle 256 2:64:2 256000 0 1 (null) none
nid001003 1 standard idle 256 2:64:2 256000 0 1 (null) none
nid001004 1 standard idle 256 2:64:2 256000 0 1 (null) none
nid001005 1 standard allocated 256 2:64:2 256000 0 1 (null) none
nid001006 1 standard down* 256 2:64:2 256000 0 1 (null) Not responding
nid001007 1 standard idle 256 2:64:2 256000 0 1 (null) none
nid001008 1 standard idle 256 2:64:2 256000 0 1 (null) none
nid001009 1 standard idle 256 2:64:2 256000 0 1 (null) none
nid001010 1 standard allocated 256 2:64:2 256000 0 1 (null) none
nid001011 1 standard allocated 256 2:64:2 256000 0 1 (null) none
nid001012 1 standard allocated 256 2:64:2 256000 0 1 (null) none
nid001013 1 standard allocated 256 2:64:2 256000 0 1 (null) none
nid001014 1 standard allocated 256 2:64:2 256000 0 1 (null) none
nid001015 1 standard allocated 256 2:64:2 256000 0 1 (null) none
...lots of output trimmed...
Explore a compute node
Let’s look at the resources available on the compute nodes where your jobs will actually run. Try running this command to see the name, CPUs and memory available on the worker nodes (the instructors will give you the ID of the compute node to use):
[auser@uan01:~> sinfo -n nid001000 -o "%n %c %m"This should display the resources available for a standard node. Can you use
sinfoto find out the range of node IDs for the high memory nodes?Solution
The high memory nodes have IDs
nid000001-nid000004. You can get this by using:auser@uan01:~> sinfo -N -l -S "-m" | lessThe
-S "-m"option tellssinfoto print the node list sorted by decreasing memory per node. This output is then piped intolessso we can examine the output a page at a time without it scrolling off the screen.
Using batch job submission scripts
Header section: #SBATCH
As for most other scheduler systems, job submission scripts in Slurm consist of a header section with the
shell specification and options to the submission command (sbatch in this case) followed by the body of
the script that actually runs the commands you want. In the header section, options to sbatch should
be prepended with #SBATCH.
Here is a simple example script that runs the xthi program, which shows process and thread placement, across
two nodes.
#!/bin/bash
#SBATCH --job-name=my_mpi_job
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=128
#SBATCH --cpus-per-task=1
#SBATCH --time=0:10:0
#SBATCH --account=ta016
#SBATCH --partition=standard
#SBATCH --qos=standard
# This will restore the appropriate programming environment
# (in this case, the GNU PE)
module load epcc-job-env
# Now load the "xthi" package
module load xthi
export OMP_NUM_THREADS=1
# Load modules, etc.
# srun to launch the executable
srun --distribution=block:block --hint=nomultithread xthi
The options shown here are:
--job-name=my_mpi_job- Set the name for the job that will be displayed in Slurm output--nodes=2- Select two nodes for this job--ntasks-per-node=128- Set 128 parallel processes per node (usually corresponds to MPI ranks)--cpus-per-task=1- Number of cores to allocate per parallel process--time=0:10:0- Set 10 minutes maximum walltime for this job--account=ta016- Charge the job to theta016budget
We will discuss the srun command further below.
Submitting jobs using sbatch
You use the sbatch command to submit job submission scripts to the scheduler. For example, if the
above script was saved in a file called test_job.slurm, you would submit it with:
auser@uan01:~> sbatch test_job.slurm
Submitted batch job 23996
Slurm reports back with the job ID for the job you have submitted
What are the default for
sbatchoptions?If you do not specify job options, what are the defaults for Slurm on ARCHER2? Submit jobs to find out what the defaults are for:
- Budget (or Account) the job is charged to?
- Tasks per node?
- Number of nodes?
- Walltime? (This one is hard!)
Solution
You can get the answers to 1., 2., and 3. with the following script:
#!/bin/bash #SBATCH --job-name=my_mpi_job module load epcc-job-env echo "Nodes: $SLURM_JOB_NUM_NODES" echo "Tasks per node: $SLURM_NTASKS_PER_NODE" module load xthi export OMP_NUM_THREADS=1 srun --distribution=block:block --hint=nomultithread xthi(1) Budget: It depends. In most cases, you will have a default budget to which jobs will be charged. This is not always the case, though.
(2) Tasks per node: 256
(3) Number of nodes: 1
(4) Getting the default time limit is more difficult - we need to use
sacctto query the time limit set for the job. For example, if the job ID was “12345”, then we could query the time limit with:auser@uan01:~> sacct -o "TimeLimit" -j 12345Timelimit ---------- 00:10:00
Checking progress of your job with squeue
You use the squeue command to show the current state of the queues on ARCHER2. Without any options, it
will show all jobs in the queue:
auser@uan01:~> squeue
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
Cancelling jobs with scancel
You can use the scancel command to cancel jobs that are queued or running. When used on running jobs
it stops them immediately.
Getting notified
Slurm on ARCHER2 can also send e-mails to notify you when your job starts, ends, fails, etc. Can you find out how you would setup your job script to send you an e-mail when your job finishes and when it fails? Test your answer, does it work?
Solution
The option
--mail-type=END,FAILwill send mailings to you when the job ends or fails. You can also use the eventTIME_LIMITto notify you if a job reaches its walltime without finishing and the eventsTIME_LIMIT_50,TIME_LIMIT_80andTIME_LIMIT_90to notify you when your job is 50%, 80% and 90% of the way through the specified walltime.
Running parallel applications using srun
Once past the header section your script consists of standard shell commands required to run your job. These can be simple or complex depending on how you run your jobs but even the simplest job script usually contains commands to:
- Load the required software modules
- Set appropriate environment variables (you should always set
OMP_NUM_THREADS, even if you are not using OpenMP you should set this to1)
After this you will usually launch your parallel program using the srun command. At its simplest,
srun only needs 1 argument to specify the correct binding of processes to cores (it will use the
values supplied to sbatch to work out how many parallel processes to launch). In the example above,
our srun command simply looks like:
srun --distribution=block:block --hint=nomultithread xthi
Underpopulation of nodes
You may often want to underpopulate nodes on ARCHER2 to access more memory or more memory bandwidth per task. Can you state the
sbatchoptions you would use to runxthi:
- On 4 nodes with 64 tasks per node?
- On 8 nodes with 2 tasks per node, 1 task per socket?
- On 4 nodes with 32 tasks per node, ensuring an even distribution across the 8 NUMA regions on the node?
Once you have your answers run them in job scripts and check that the binding of tasks to nodes and cores output by
xthiis what you expect.Solution
--nodes=4 --ntasks-per-node=64--nodes=8 --ntasks-per-node=2 --ntasks-per-socket=1--nodes=4 --ntasks-per-node=32 --ntasks-per-socket=16 --cpus-per-task=4
Hybrid MPI and OpenMP jobs
When running hybrid MPI (with the individual tasks also known as ranks or processes) and OpenMP
(with multiple threads) jobs you need to leave free cores between the parallel tasks launched
using srun for the multiple OpenMP threads that will be associated with each MPI task.
As we saw above, you can use the options to sbatch to control how many parallel tasks are
placed on each compute node and can use the --cpus-per-task option to set the stride
between parallel tasks to the right value to accommodate the OpenMP threads - the value
for --cpus-per-task should usually be the same as that for OMP_NUM_THREADS. To ensure
you get the correct thread pinning, you also need to specify an additional OpenMP environment
variable and a couple of additional options to srun. Specifically:
- Set the
OMP_PLACESenvironment variable tocoreswithexport OMP_PLACES=coresin you job submission script - Add the
--hint=nomultithreadoption tosrun - Add the
--distribution=block:blockoption tosrun
As an example, consider the job script below that runs across 2 nodes with 8 MPI tasks per node and 16 OpenMP threads per MPI task (so all 256 cores across both nodes are used).
#!/bin/bash
#SBATCH --job-name=my_hybrid_job
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=8
#SBATCH --cpus-per-task=16
#SBATCH --time=0:10:0
#SBATCH --account=ta016
#SBATCH --partition=standard
#SBATCH --qos=standard
# This will restore the appropriate programming environment
# (in this case, the GNU PE)
module load epcc-job-env
# Now load the "xthi" package
module load xthi
export OMP_NUM_THREADS=16
export OMP_PLACES=cores
# Load modules, etc.
# srun to launch the executable
srun --hint=nomultithread --distribution=block:block xthi
Each ARCHER2 compute node is made up of 8 NUMA (Non Uniform Memory Access) regions (4 per socket) with 16 cores in each region. Programs where the threads of a task span multiple NUMA regions are likely to be less efficient so we recommend using thread counts that fit well into the ARCHER2 compute node layout. Effectively, this means one of the following options for nodes where all cores are used:
- 8 MPI tasks per node and 16 OpenMP threads per task: equivalent to 1 MPI task per NUMA region
- 16 MPI tasks per node and 8 OpenMP threads per task: equivalent to 2 MPI tasks per NUMA region
- 32 MPI tasks per node and 4 OpenMP threads per task: equivalent to 4 MPI tasks per NUMA region
- 64 MPI tasks per node and 2 OpenMP threads per task: equivalent to 8 MPI tasks per NUMA region
STDOUT/STDERR from jobs
STDOUT and STDERR from jobs are, by default, written to a file called slurm-<jobid>.out in the
working directory for the job (unless the job script changes this, this will be the directory
where you submitted the job). So for a job with ID 12345 STDOUT and STDERR would be in
slurm-12345.out.
If you run into issues with your jobs, the Service Desk will often ask you to send your job submission script and the contents of this file to help debug the issue.
If you need to change the location of STDOUT and STDERR you can use the --output=<filename>
and the --error=<filename> options to sbatch to split the streams and output to the named
locations.
Other useful information
In this section we briefly introduce other scheduler topics that may be useful to users. We provide links to more information on these areas for people who may want to explore these areas more.
Interactive jobs: direct srun
Similar to the batch jobs covered above, users can also run interactive jobs using the srun
command directly. srun used in this way takes the same arguments as sbatch but, obviously, these are
specified on the command line rather than in a job submission script. As for srun within
a batch job, you should also provide the name of the executable you want to run.
For example, to execute xthi across all cores on two nodes (1 MPI task per core and no
OpenMP threading) within an interactive job you would issue the following commands:
auser@uan01:~> srun --partition=standard --qos=standard --nodes=2 --ntasks-per-node=128 --cpus-per-task=1 --time=0:10:0 --account=ta016 xthi
Node 0, hostname nid001030
Node 1, hostname nid001031
Node 0, rank 0, thread 0, (affinity = 0,128)
Node 0, rank 1, thread 0, (affinity = 16,144)
Node 0, rank 2, thread 0, (affinity = 32,160)
Node 0, rank 3, thread 0, (affinity = 48,176)
Node 0, rank 4, thread 0, (affinity = 64,192)
Node 0, rank 5, thread 0, (affinity = 80,208)
Node 0, rank 6, thread 0, (affinity = 96,224)
Node 0, rank 7, thread 0, (affinity = 112,240)
Node 0, rank 8, thread 0, (affinity = 1,129)
Node 0, rank 9, thread 0, (affinity = 17,145)
Node 0, rank 10, thread 0, (affinity = 33,161)
Node 0, rank 11, thread 0, (affinity = 49,177)
Node 0, rank 12, thread 0, (affinity = 65,193)
Node 0, rank 13, thread 0, (affinity = 81,209)
Node 0, rank 14, thread 0, (affinity = 97,225)
...long output trimmed...
Key Points
ARCHER2 uses the Slurm scheduler.
srunis used to launch parallel executables in batch job submission scripts.There are a number of different partitions (queues) available.