- Basic Job Submission
- Your Job Script
- Choosing a Queue
- Specifying how long the job will run
- Specifying node and core requirements
- Specifying memory requirements
- Requesting nodes with specific features
- Requesting nodes with specific CPUs
- Using InfiniBand Nodes
- Using GPUs
- Specifying the amount/type of scratch space needed
- Specifying the account to be charged
- Specifying email options
- Specifying output options
- Specifying which shell to run the job in
- Specifying the directory to run the job in
- Specifying whether or not other jobs can be on the same node
- Specifying a reservation
Basic Job Submission
The Deepthought HPC clusters use a batch scheduling system called Slurm to handle the queuing, scheduling, and execution of jobs. This scheduler is used in many recent HPC clusters throughout the world. This page will attempt to discuss the Slurm commands for submitting jobs, and how to specify the job requirements. For users familiar with PBS/Torque or Maui/Torque or Moab/Torque based clusters, we have a document which translates commonly used commands from those scheduler systems into their Slurm equivalents..
Users generally submit jobs by writing a job script file and submitting
the job to Slurm with
sbatch command. The
sbatch command takes a
number of options (some of which can be omitted or defaulted). These
options define various requirements of the job, which are used by the
scheduler to figure out what is needed to run your job, and to schedule
it to run as soon as possible, subject to the constraints on the system,
usage policies, and considering the other users of the cluster. It is also
possible to submit an
interactive job, but that is usually most useful for debugging purposes.
The options to
sbatch can be given on the command line,
or in most cases inside the job script. When given inside the job script,
the option is placed alone on a line starting with
(you must include a space after the SBATCH). These
SHOULD come before any non-comment/non-blank line in the script --- any
#SBATCH lines AFTER a non-comment/non-blank line in the
script might get ignored by the scheduler. See the
examples page for examples.
# at the start of these lines means they will be ignored
by the shell; i.e. only the sbatch command will read them.
Your Job Script
The most basic parameter given to the
sbatch command is
the script to run. This obviously must be given on command line, not
inside the script file. This job script must start with a
line which specifies the shell under which the script is to run. I.e.,
the very first line of your script should generally be either
bashshell, respectively. This must be the first line, and no spaces before the name of the shell. This line is typically followed by a bunch of
#SBATCHlines specifying the job requirements (these will be discussed below), and then the actual commands that you wish to have executed when the job is started on the compute nodes.
There are many options you can give to
sbatch either on
the command line or using
#SBATCH lines within your script
file. Other parts of this page discuss the more common ones. It is
strongly recommended that you include at least the following directives
- how long the job will run
- the node and core requirements
- the memory requirements
- whether or not other jobs can be on the same node (for the Deepthought clusters)
- the partition to run in (for the MARCC/Bluecrab cluster)
#SBATCH lines should come BEFORE any non-blank/non-comment
lines in your script file. Any
#SBATCH lines which come
after non-blank/non-comment lines might get ignored by the scheduler.
If your default shell
tcsh (on the Deepthought clusters, that
will be your default shell unless you explicitly
changed it), and you
are submitting a
bash job script (i.e., the first line
of your job script is
#!/bin/bash), on the Deepthought
clusters it is strongly recommended that the first command after
#SBATCH lines is
This will properly set up your environment on these clusters, including
defining the module command.
It is also recommended that after that line, you include
commands to set up your environment for any
software packages you wish to use.
The remainder of the file should be the commands to run to do the calculations you want. After you submit the job, the job will wait for a while in the queue until resources are available. Once resources are available, the scheduler will run this script on the first node assigned to your job. If your job involves multiple nodes (or even multiple cores on the same node), it is this script's responsibility to launch all the tasks for the job. When the script exits, the job is considered to have finished. More information can be found in the section on Running parallel codes and in the examples section.
Do NOT run jobs from your home directory; the home directories are not optimized for intensive I/O. You have a lustre directory at
Note: If your script does not end with a
end-of-line (EOL) character, the last line of your script will usually
be ignored by the shell when it is run. This can often happen if you
transfer files from Windows (which uses different EOL characters) to Unix,
and can sometimes be quite confusing, as you submit you job, it runs, and
finishes almost immediately without errors, and there is seemingly no output because the
last line of your job script, which got ignored, is the command to
actually do the calculation. Although you can use a command like
dos2unix to fix the script file, it is usually easiest
to just remember to add a couple blank lines to the end of your file.
This doesn't actually fix the problem, as the script does not
end with a proper Unix EOL, but the last line, which gets ignored, is
now blank, and you won't care that it got ignored.
Sometimes your job script might need specific information about job
parameters specified to sbatch. To facilitate this, the Slurm scheduler will
make a number of these values available in environmental variables. These
are detailed in the man page for sbatch, and also in this page
listing the more commonly used variables.
These can be useful in many cases, e.g. if you are running a program on a single node
which needs to be passed an argument with the number of threads to run, you probably
want to give it
$SLURM_NTASKS for this value to ensure that the number
you pass it always equals the number of cores requested from Slurm. This can help
avoid issues if you change the number of cores requested in a later run, as you only
change things in one place now.
Choosing a Queue/Partition
On the Deepthought clusters, you generally should not be specifying a partition. The only time you should be specifying a partition on these clusters is if you want to run your job:
- in the debug partition. This partition is intended for quick turn around for small debugging jobs, but has a 15 minute maximum walltime so is not suitable for production.
- in the scavenger partition. The scavenger partition is an ultra-low priority.
In all other cases on the Deepthought clusters, the scheduler will automatically place your job in the correct partition, usually based on the allocation account you are charging against.
The debug partition on the Deepthought clusters is for short, debugging jobs. It is intended to allow quick turn-around for the debugging process, but not for running production jobs. As such, it has a severely limited run-time limit (15 minutes).
The scavenger partition does not charge against your allocation, but it is very low priority (all other jobs will cut in front of it in the pending queue) and even once your job starts, any other job not also in the low-priority partition can knock your job off a node it wants after your job started running. As such, these jobs need to do some form of checkpointing in order to make progress in the snatches of CPU time they get allocated. If you do not know what checkpointing is or how to do it, this partition is NOT for you. Since no allocation is charged, this partition does not get a priority increase if you charge against your hight-priority allocation.
To specify either of the above partitions, you just give the sbatch
--partition=PART argument, or equivalently
-p PART argument, replacing PART with the
name of the partition. E.g., to submit a job to the
debug partition, you could add to your
command the arguments
-p debug. Similarly, to submit a job
to the scavenger partition, you could add
sbatch. In either case, you can either append the
partition flag to the end of the command line, or add it near the top of your
job script with a
#SBATCH prefix, e.g. for the debug partition
#SBATCH -p debug
On the MARCC/Bluecrab cluster, you generally will need to specify a partition as on this cluster partitions are used to classify job requirements. A complete list of partitions on Bluecrab can be found on the MARCC/Bluecrab website.
In general, on MARCC/Bluecrab, jobs requiring more than
one node (24 cores on normal nodes, 48 cores on large memory nodes) should
be submitted to the
parallel partition. Jobs requiring the
large memory nodes (1024 GB) should be submitted to the
partition, and jobs requiring GPUs to the
gpu partition. Jobs
requiring one node (or a fraction thereof) should generally be submitted
Note that jobs submitted to the
on the MARCC/Bluecrab cluster
will be forced into --exclusive mode (even if you
explicitly specify --share or --oversubscribe,
this will be overridden).
Jobs submitted to the other partitions will default to being in
--share or --oversubscribe mode
(although you can override that
if you really want to). Most partitions are limited to one week of wall time.
The MARCC/Bluecrab cluster also provides a preemptible
To specify any of the MARCC/Bluecrab partitions, you
should either give a
-p PART option in the
command line, or include an
#SBATCH -p PART
Specifying the Amount of Time Your Job Will Run
When submitting a job, it is very important to specify the amount of time you expect your job to take. If you specify a time that is too short, your job will be terminated by the scheduler before it completes. So you should always add a buffer to account for variability in run times; you do not want your job to be killed when it is 99.9% complete. However, if you specify a time that is too long, you may run the risk of having your job sit in the queue for longer than it should, as the scheduler attempts to find available resources on which to run your job. See the section on job scheduling for more information on the scheduling process and advice regarding the setting of walltime limits. See the section on Quality of Service levels for more information on the walltime limits on the Deepthought clusters.
In general, on the Deepthought clusters, all users can run
jobs up to 3 days in length, and members of contributing units can run jobs
up to 14 days in length. On the
MARCC/Bluecrab cluster, all users
can run jobs up to a week in length.
To specify your estimated runtime, use the
parameter to sbatch. This value TIME can in any of the following
M:S(M minutes, S seconds)
H:M:S(H hours, M minutes, S seconds)
D-H(D days, H hours)
D-H:M(D days, H hours, M minutes)
D-H:M:S(D days, H hours, M minutes, S seconds)
NOTE: If you do not specify a walltime, the default walltime on the Deepthought HPC clusters is 15 minutes. I.e., your job will be killed after 15 minutes. Since that is not likely to be sufficient for it to complete, specify a reasonable walltime. This greatly aids the scheduler in making the best utilization of resources.
The following example specifies a walltime of 60 seconds, which should be more than enough for the job to complete.
#SBATCH -n 1 #SBATCH -t 0:60 hostname
Specifying Node and Core Requirements
Slurm provides many options for specifying your node and core requirements,
and we only cover the basics here. More details can be found at the
official Slurm site.
Also see the man pages
From your job's perspective, of most concern is the number of CPU cores and how they are distributed over the nodes Jobs generally use a combination of MPI tasks and/or multithreading for their parallelism. We let N represent the number of MPI tasks, and M represent the number of threads needed by the job. Most jobs then fall into one of these categories:
- sequential jobs: these jobs will run on a single CPU core (and therefore a single node). In this case N = M = 1
- shared memory parallel jobs : These jobs use some sort of multithreading (e.g. OpenMP ), and so require M CPU cores on a single node. Here M depends on the code and/or the problem being solved, and N=1.
- Pure MPI jobs : jobs require a certain number of CPU cores (one for each MPI task ) but they can be spread out over multiple nodes (and the job generally does not care how they are spread over the nodes). In this case, M=1 and N depends on the code/problem being solved.
- Hybrid MPI jobs : these jobs use MPI, but each MPI task uses multithreading . For these jobs, if N is the number of MPI tasks, and M is the number of threads for each task, you want N x M CPU cores, but each set of M cores must reside on the same node. Here both N and M depend on the code and the problem, and will be greater than 1.
sbatch and related commands provide three options for
controlling this behavior:
--ntasks=N: This sets the number of MPI tasks for the job, which should be one for the sequential and pure multithreaded cases, and for the MPI cases it should be set to the number of MPI tasks desired.
--cpus-per-task=M: This sets the number of CPU cores to use for each task. All the CPU cores for a given task will be allocated on the same node (although cores from more than one tasks might also be allocated on the same node, as long as all the cores from both tasks fit on the same node). This defaults to one. For sequential jobs and pure MPI jobs, this should be set to one. For pure multithreaded jobs, this should be set to the number of threads desired. Similarly, for hybrid MPI jobs, this should be set to the number of threads desired per MPI task.
--nodes=MINNODES[-MAXNODES]: This specifies the number of nodes to use. This flag is generally not needed, and we recommend that you do not use this option and instead use the
--cpus-per-taskoptions, and let the schedule work out how many nodes are needed. Using it properly requires a good knowledge of the hardware available on the cluster, and using it improperly can result in your job wasting resources, being overcharged, and/or spending excessive time waiting in the queue. If you use it, you can give a range on the number of nodes to use. If MAXNODES is omitted, it will default to the value of MINNODES. But in general, it is best to omit this entirely and just give
--cpus-per-task, and the scheduler will allocate just enough nodes to satisfy those specifications.
So for a sequential job you could use the arguments
#SBATCH -n 1 #SBATCH -c 1
We also provide a sequential examplewith a complete job submission script and a line-by-line explanation.
Similarly, for multithreaded case, e.g. where you require 12 cores on a single node, you could use the arguments
#SBATCH -n 1 #SBATCH -c 1
#SBATCH --ntasks=12 myjob
The above might allocate 12 cores on a single node for your job or distribute the tasks over several nodes.
If you are concerned about how your cores are allocated, you can also give
NUMNODESDESC can be of the form
MINNODES-MAXNODES. In the former case,
MAXNODES is set to the
same as MINNODES. The scheduler will attempt to allocate
and MAXNODES (inclusive) nodes to your job. So for the above example
--ntasks=12), we might have
- all cores assigned on the same node if
-N 1is given.
- the cores split among two nodes if
-N 2is given. You might get an even split, 6 cores each node, or an assymetric split, e.g. 4 on one node and 8 on the other. But you will get two distinct nodes.
- either of the two above cases if
-N 1-2is given.
If you only specify the number of
nodes (i.e. only the
parameter), you will be assigned (and charged for) all cores on the assigned nodes.
If you only specify a single node (
In general, for distributed memory (e.g. MPI) jobs, we recommend that most
users just specify the
-n parameter and let Slurm figure out how to best divide the cores among
the nodes unless you have specific requirements.
Of course, for shared memory (e.g. OpenMP or multithreaded) jobs, you need
--nodes=1 to ensure that all of the cores assigned to
you are on the same node.
If you are requesting more than one core but less than the all the cores on the node on the Deepthought clusters, you should consider using the
On the MARCC/Bluecrab cluster, if you are requesting more
than one node, you must use the
sbatch command has a large number of other options
allowing you to specify node and CPU requirements for a wide variety of
cases; the above is just the basics. More detail can be found reading the
Specifying Memory Requirements
If you want to request a specific amount of memory for your job, try something like the following:
#!/bin/sh #SBATCH -N 2 #SBATCH --mem=1024 myjob
This example requests a two nodes with at least 1 GB (1024 MB) of memory total
each. Note that the
specifies the memory on a per node basis.
If you want to request a specific amount of memory on a per-core basis, use the following:
#!/bin/sh #SBATCH --ntasks=8 #SBATCH --mem-per-cpu=1024 myjob
This requests 8 cores, with at least 1 GB (1024 MB) per core.
NOTE: for both
the specified memory size must be in MB.
You should also note that node selection does not count memory used by the operating system, etc. So a node which nominally has 8 GB of RAM might only show 7995 MB available. So if your job specified a requirement of 8192 MB, it would not be able to use that node. So a bit of care should be used in choosing the memory requirements; going a little bit under multiples of GBs may be advisible.
On the MARCC/Bluecrab cluster, jobs requiring more than
128 GB/node (or about 5.3 GB/core if using all cores on the node), need
to be submitted to the
These jobs are restricted to a single node.
Requesting Nodes with Specific features or resources
Sometimes your job requires nodes with specific features or resources. I.e., some jobs require the higher interconnect speeds afforded by infiniband, or maybe your job will make use of GPUs for processing. Such requirements need to be told to the scheduler to ensure you get assigned appropriate nodes.
In slurm, we break this situations into two cases:
- features: This refers to something which can be present or not on a system, and if it is present, it is available to all processes on the system. (Obviously, if it is not present, it is not available to any processes on the system). A simply boolean present or not present. E.g., the presence of an infiniband adapter, or whether the processors on the system support the SSSE3 instruction set.
- resource: This refers to something which not only is present (or not), but has an amount attached to it. Unlike features, resources have a quantity, both in terms of what is present on the node, but also in terms of what is being consumed by jobs running on the node. I.e, a system can have 0, 1, or 2 GPUs. In addition, a job running on a 2 GPU system might consume 0, 1, or 2 of the GPUs.
You can see which nodes support which features and resources with
sinfo. By default, this information is not shown. Features
can be shown by using the
sinfo --Node --long options; resources
require additional fields be specified in the
see both, once can use: (a line is printed for each node/partition combination,
so we give "-p scavenger" to only see the nodes in the scavenger partition;
without that most nodes would appear in triplicate
because they belong to the scavenger, standard, and high-priority partitions):
login-1> sinfo -N -p scavenger --format="%N %5T %.4c %.8z %.8m %.8d %25f %10G" NODELIST STATE CPUS S:C:T MEMORY TMP_DISK AVAIL_FEATURES GRES compute-a20-0 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) compute-a20-1 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) compute-a20-2 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) compute-a20-3 idle 20 2:10:1 128000 750000 rhel8,intel,xeon_e5-2680v (null) compute-a20-4 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) ... compute-a20-30 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) compute-a20-31 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) compute-a20-33 idle 16 2:8:1 64000 340000 rhel8,intel,xeon_e5-2670 (null) compute-b17-0 mixed 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v gpu:2 compute-b17-1 mixed 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v gpu:2 ... compute-b18-14 mixed 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v gpu:2 compute-b18-15 mixed 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v gpu:2 compute-b18-16 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) compute-b18-17 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) ... compute-b23-0 idle 40 4:10:1 1020000 750000 rhel6,intel,xeon_e5-4640v (null) compute-b23-1 mixed 40 4:10:1 1020000 750000 rhel6,intel,xeon_e5-4640v (null) compute-b23-2 idle 40 4:10:1 1020000 750000 rhel8,intel,xeon_e5-4640v (null) compute-b23-3 mixed 40 4:10:1 1020000 750000 rhel6,intel,xeon_e5-4640v (null) compute-b23-4 mixed 40 4:10:1 1020000 750000 rhel6,intel,xeon_e5-4640v (null) compute-b24-0 alloc 20 2:10:1 128000 750000 rhel6,intel,xeon_e5-2680v (null) ...
To request a specific feature, use the
sbatch. In its simplest form, you just give
is the name of the feature you are requesting. E.g., to request a node
running Red Hat Enterprise Linux 8, (the
you would use something like:
#!/bin/tcsh #SBATCH -t 15:00 #SBATCH --ntasks=8 #SBATCH --constraint="rhel8" #It is recommended that you add the exact version of the #compiler and MPI library used when you compiled the code #to improve long-term reproducibility module load gcc module load openmpi mpirun mycode
To run the same job on RHEL6 (assuming the code will run on RHEL6), you would use something like:
#!/bin/tcsh #SBATCH -t 15:00 #SBATCH --ntasks=8 #SBATCH --constraint="rhel6" #It is recommended that you add the exact version of the #compiler and MPI library used when you compiled the code #to improve long-term reproducibility module load gcc module load openmpi mpirun mycode
As of 1 Sep 2020, jobs not specifying any constraints will have the constrain "rhel6" automatically added, so that an old job script will run as expected. However, at some point (likely late Oct or early Nov) we will switch the default to be "rhel8".
You should start adding the
--constraint option can get rather more complicated, as
Slurm allows multiple constraints to be given, with the constraints either
ANDed or ORed. You can request that only a subset (e.g. 2 out of 4) nodes
need to have the constraint, or that either of two features are acceptable,
but all nodes assigned must have the same feature. If you need that level
of complexity, please see the
man page for sbatch (
Resources are requested with the
sbatch. The usage is
RESOURCE_LIST is a comma delimitted list of resource
names, optionally followed by a colon and a count. The resources specified
are required on each node assigned to the job. E.g., to request 3 nodes and
2 GPUs on each node (for 6 GPUs total) on the Deepthought2 cluster,
one would use something like:
#!/bin/tcsh #SBATCH -t 15:00 #SBATCH -N 3 #SBATCH --gres=gpu:2 cd /lustre/payerle ./run_my_gpu_code
To get a list of available resources as defined in a cluster, you can
use the command
sbatch --gres=help temp.sh. NOTE:
that temp.sh must be an existing submit script; the basic validation of the
script occurs before the
--gres=help gets evaluated. When
--gres=help is given, the script will not be submitted.
On the Division of IT maintained clusters, the following resources are available:
|gpu||Deepthought2||Node has GPUs||Currently all Deepthought2 GPUs are NVIDIA Tesla K20m
(2 GPUs per node)
|gpu:p100||Juggernaut||Node has NVIDIA Pascal P100 GPUs||1 node (2 GPUs per node)|
|gpu:v100||Juggernaut||Node has NVIDIA Volta V100 GPUs||1 node (4 GPUs per node)|
Similarly, the following features are available on the UMD DIT maintained clusters:
|rhel6||Deepthought2||Node is running RHEL6|
|rhel7||Juggernaut||Node is running RHEL7|
|Node is running RHEL8|
|amd||Juggernaut||Node has AMD CPUs|
|Node has Intel CPUs|
|epyc_7702||Juggernaut||Node has AMD Epyc 7702 CPUs||Zen2 architecture in "green" partition|
|xeon_6142||Juggernaut||Node has Intel Xeon 6142 CPUs||Skylake architecture, very limited number (p100 GPU node)|
|xeon_6148||Juggernaut||Node has Intel Xeon 6148 CPUs||Skylake architecture, most Intel CPUs on "green" partition|
|xeon_6248||Juggernaut||Node has Intel Xeon 6248 CPUs||Casecadelake architecture, very limited number|
|xeon_e5_2670||Deepthought2||Node has Intel Xeon E5-2670 CPUs||SandyBridge-EP architecture, very limitted number|
|xeon_e5_2680v2||Deepthought2||Node has Intel Xeon E5-2680v2 CPUs||IvyBridge architecture, most compute nodes|
|xeon_e5_2680v4||Juggernaut||Node has Intel Xeon E5-2680v4 CPUs||Broadwell architecture, most compute nodes in "blue" partition|
|xeon_e5_4640v2||Deepthought2||Node has Intel Xeon E5-4640v2 CPUs||IvyBridge architecture, used by the large memory nodes|
In the above tables, the clusters column indicates which clustes the feature/resource is available on.
Requesting nodes with specific CPUs
As our clusters are growing, the clusters are becoming less homogeneous. While that might not matter for some workloads, for others you might need a specific CPU architecture.
We have provided features on all of our nodes to help facilitate the specification of the desired CPU architecture. This can be done at various levels, ranging from the most general (whether to use AMD or Intel based cpus with the features "amd" or "intel") to very specific (selecting a specific CPU model).
You can instruct the
sbatch command to use a particular
subset of nodes matching the desired CPU architectures by adding
--constraint flag, as
discussed in the section on 'features'.
A listing of the various CPUs you can select from can be
found in the table listing the allowed features
Using InfiniBand Nodes
All nodes on the Deepthought2 cluster have FDR (54 Gb/s) infiniband. You do not need to request InfiniBand nodes, all the nodes have it.
Although all nodes on Deepthought2
have InfiniBand, the network topology is such that there is 2:1 blocking in
the bandwidth when going between the rack top switches. If your job cannot
fit within a single rack (typically 56 nodes or 1120 cores), you cannot really avoid that.
For smaller jobs, you can specify
--switches=1, then your job will
be allocated nodes that are all connected to the same switch, avoiding the blocking issue.
You can also use
--switches=1@MAXTIME, which limits the
amount of time your job will wait as pending for nodes all on the same switch
to become available; after that time, it will accept nodes spread across more
than one switch.
Although originally designed to driving high end graphics displays, it turns out the graphical processing units (GPUs) are very good at number crunching, for certain types of problems. The Deepthought2 cluster has 40 nodes each with 2 Nvidia Tesla K20m GPU cards, each providing over 2000 cores.
Although there are a lot of cores present in the GPU, they are not compatible with the standard Intel x86 architecture, and codes need to be written especially for these cards, using the CUDA platform . Some applications support for CUDA already, although even in those cases you need to use versions that were built to support CUDA.
See the section on CUDA for more information on using and compiling CUDA and OpenCL programs. See the section on software supporting GPUs for more information on currently installed software which supports GPU processing.
To request GPUs for your job on the Deepthought2 cluster,
you need to give
N specifies the number of GPUs per node that
you are requesting. N defaults to 1 in the first form, and since
we have at most 2 GPUs per node, the only other viable option is N=2.
E.g, to request 4 nodes, requesting 1 GPU on each, you could use something
#!/bin/tcsh #SBATCH -t 15:00 #SBATCH -N 4 #SBATCH --gres=gpu cd /lustre/payerle ./run_my_gpu_code
Currently, we do NOT directly charge for the use of GPUs. GPU based jobs are only charged for the CPUs they consume on the GPU node. Your job must use at least 1 CPU core. If your job runs in exclusive mode (which is the default for jobs using more than 1 CPU core), you will be charged for all CPU cores on the node. Otherwise, in "shared" mode, other jobs (CPU and/or GPU if there are GPUs you are not using) can run on the node while your job is running. This will reduce the cost of your job, but does increase risk (it is possible for the other jobs to effectively crash the node).
Currently, the GPU nodes on Deepthought2 have 2 GPUs each. It is
possible for two single GPU jobs to run on the same node in "shared" mode.
Slurm will set the environmental variable CUDA_VISIBLE_DEVICES to the
GPU(s) which it allocated to your job, e.g. to
0 if it assigned
you only the first GPU,
1 if it assigned only the second, or
0,1 if it assigned both. By default,
will use this variable
and will only use the specified GPU(s). So two single GPU CUDA jobs should be
able to coexist on the same node without interfering with each other.
(However, problems might occur if one of the jobs is not CUDA based, or if
the job does stuff it should not be doing.)
On the MARCC/Bluecrab cluster, to request GPUs for
your job you need to submit your job to the
partition. Again, you are only charged for the CPUs consumed, not
the GPU cores, but in this case you are charged for the entire node.
Specifying the Amount/Type of Scratch Space Needed
If your job requires more than a small amount (10GB) of local scratch space, it would be a good idea to specify how much you need when you submit the job so that the scheduler can assign appropriate nodes to you.
Almost all nodes on Deepthought2 currently have 750GB of scratch space, on Juggernaut at least 75GB of scratch space. Scratch space is currently mounted as /tmp. Scratch space will be cleared once your job completes.
The following example specifies a scratch space requirement of 5GB. Note however that if you do this, the scheduler will set a filesize limit of 5GB. If you then try to create a file larger than that, your job will automatically be killed, so be sure to specify a size large enough for your needs.
#!/bin/sh #SBATCH --ntasks=8 #SBATCH --tmp=5120 myjob
Note that the disk space size must be given in MB.
Specifying the account to be charged
All users of the cluster belong to at least one project associated with the cluster, and each project has at least one account its users can charge against. Projects which have contributed hardware to the cluster generally have a normal priority and a high priority account; other projects typically have only a normal priority account.
Jobs charged to the high-priority account take precedence over jobs charged to normal priority accounts, as well as low priority (e.g. scavenger queue) jobs. And normal priority jobs take precedence over low priority jobs. No job will preempt another job (i.e., kick it off a node once it starts execution) regardless of priority, with the exception of jobs in the scavenger queue, which will be preempted by any job with a higher priority.
To submit jobs to an account other than your default
(normal priority) account, use the
-A option to
login-1:~: sbatch -A test-hi test.sh Submitted batch job 4194
If no account is explicitly specified, your job will be charged
against your default account. You can view and/or change your default
account with the
sacctmgr command. The following example
shows how the user
payerle would change his default allocation
tptest using the
sacctmgr command; you should change the user and allocation
account names appropriately.
login-1:~: sacctmgr list user payerle User Def Acct Admin ---------- ---------- --------- payerle test None login-1:~/slurm-tests: sacctmgr modify user payerle set DefaultAccount=tptest Modified users... payerle Would you like to commit changes? (You have 30 seconds to decide) (N/y): y login-1:~/slurm-tests: login-1:~/slurm-tests: sacctmgr list user payerle User Def Acct Admin ---------- ---------- --------- payerle tptest None
If you belong to multiple projects, you should charge your jobs against
an account for the appropriate project (i.e. if your thesis advisor is
Prof. Smith, and you are doing work for Prof. Jones, thesis work should be
charged against one of Prof. Smith's accounts, and your work for Prof. Jones
against one of his accounts). If there are both high and normal priority
accounts in the project, you generally should be charging against the high
priority account. Exception: You should generally run jobs
debug partition against normal priority accounts, as jobs
debug partition do NOT get any increase in priority when
run against high priority accounts, since jobs in the
partition already run with increased priority.
The above recommendations assume that there are sufficient funds available in your high priority account. If there do not appear to be sufficient funds to complete the job (and all currently running jobs that are being charged against that allocation), then the job will not start. The scheduler will NOT draw funds from the normal priority account to make up the difference. (The reverse also does not occur; if you attempt to run a job against the standard priority account but there are insufficient funds, the scheduler will NOT draw funds from the high priority account even if there are sufficient funds there.)
But in general, if you have both normal and high priority accounts, use the high priority account preferentially. The main reasons to charge a job against the normal priority account are:
- you are running it in the
- you have exceeded your monthly high priority allotment
This latter case is the whole reason for the dual account setup; you can effectively borrow SUs from the next month in the quarter (or the previous if you did not use them), but such "borrowed" SUs only run at normal priority.
For more information on accounts, including monitoring usage of your account, see the section Allocations and Account Management.
The scheduler can email you when certain events related to your job
occur, e.g. on start of execution, or when it completes. By default,
any such mail is sent to your
@umd.edu email address, but
you can specify otherwise with the
You can control when mail is sent with the
--mail-type=TYPE option. Valid options are:
- BEGIN: when the job starts to execute
- END: when the job completes
- FAIL: if and when the job fails
- REQUEUE: if and when the job is requeued.
- ALL: for all of the above cases.
You can give multiple
to have mail sent for multiple conditions. The following job script
will send mail to
email@example.com when the job starts
and when the job finishes:
#!/bin/tcsh #SBATCH --ntasks=24 #SBATCH --firstname.lastname@example.org #SBATCH --mail-type=BEGIN #SBATCH --mail-type=END start-long-hpc-job
NOTE: It is recommended that you use care with these options, especially if you are submitting a large number of jobs. Not only will you get a large amount of email, but it can cause issues with some email systems (e.g. GMail imposes limits on the number of emails you can receive in a given time period).
Specifying output options
By default, slurm will direct both the stdout and stderr streams for
your job to a file named
the directory where you submitted the
sbatch command. For
job arrays, the file will be
slurm-JOBNUMBER_ARRAYINDEX.out. In both
cases, JOBNUMBER is the number for the job.
You can override this with the
-o FILESPEC, for short) option. FILESPEC
is the name of the file to write to, but the following replacement symbols
%A: The master job allocation number for job arrays
master allocation number for the job array.
%a: The job array index number, only meaningful for job arrays.
%j: The job allocation number.
%N: The name of the first node in the job.
%u: Your username
Multiple replacements symbols are allowed in the same FILESPEC.
I.e., the default values are
slurm-%A_%a.out for simple and array jobs, respectively.
You can also use
-e FILESPEC) to have the stderr sent to a different
file from stdout. The same replacement symbols are allowed here.
If you use the
Specifying the shell to run in
Under Slurm, your job will be executed in whatever shell is specified
by the shebang
in the script file specifies. Note: this differs from
the case with Moab/Torque, where the job would run under your default shell
unless you gave an explicit
qsub to change
Thus, the following job script will be processed via the C-shell:
#!/bin/csh #SLURM --ntasks=16 #SLURM -t 00:15 setenv MYDIR /tmp/$USER ...
and the following job script will be processed with the Bourne again shell:
#!/bin/bash #SLURM --ntasks=16 #SLURM -t 00:15 . ~/.profile MYDIR="/tmp/$USER" export MYDIR ...
NOTE: If your default shell is
NOTE: If you wish to use a bourne style shell, we strongly recommend
Running Your Job in a Different Directory
The working directory in which your job runs will be the directory from
which you ran the
sbatch command, unless you specify
otherwise. The easiest way to change
this behavior is to add the appropriate
cd command before
any other commands in your job script.
Also note that if you are using MPI, you may also need to add either
-wdir option for
mpirun to specify the
The following example switches the working directory to
#!/bin/csh #SLURM -t 01:00 #SLURM --ntasks=24 module load openmpi cd /data/dt-raid5/bob/my_program mpirun -wd /data/dt-raid5/bob/my_program C alltoall
There is also a
--workingdir=DIR option that you can
(or add a
#SBATCH --workingdir=DIR line in your job
use of that method is not recommended. It should work for the Lustre file
system, but does not work well with any NFS file systems (since these get
automounted, using symlinks, and
sbatch appears to expand all symlinks, which breaks the automount mechanism).
Specifying whether or not other jobs can be on the same node
sbatch command has the (mutually-exclusive) flags
--share) which control whether the
scheduler should allow multiple jobs to co-exist on the same nodes. This is
only an issue when the jobs individually do not consume all of the resources
on the node; e.g. consider a node with 8 cores and 8 GB of RAM. If one job
requests 2 cores and 4 GB, and a second job requests 4 cores and 3 GB, they
should both be able to fit comfortably on that node at the same time.
If both jobs have the shared flag set, then the scheduler is free to place
them on the same node at the same time. If either has the exclusive flag set,
however, then the scheduler should not put them on the same node; the job(s)
with the exclusive flag set will be given their own node.
If you are running jobs which contain sensitive information, you should ALWAYS submit jobs in exclusive mode to reduce exposure to security threats. By not allowing other jobs (i.e. other users) access to the nodes where your jobs are running, you reduce the exposure to security threats.
There can be a couple of problems with running jobs in shared mode. First, if your job is processing sensitive information, allowing other jobs (potentially owned by other users) to run on the same node(s) as your job increases your exposure to potential security threats/exploits. It is strongly recommended that jobs processing sensitive information always run in exclusive mode.
Also, there is the possibillity of potential interference between the jobs. First off, to optimize performance, we are not perfectly enforcing the core and memory usage of jobs, and it is possible for a job to "escape" its bounds. But even assuming the jobs keep within their requested CPU and memory limits, they still would be sharing IO bandwidth, particularly disk and network, and depending on the jobs this might cause significant performance degradation. On the other hand, it is wasteful to give a smaller job a node all to itself if it will not use all the resources on the node.
From the perspective of you, the user, this potential for interference between jobs means that your job might suffer from slower performance, or even worse, crash (or the node it is running on crash). While that might make submitting jobs in exclusive mode seem like the easy answer, that could significantly impact utilization of the cluster. In other words, if you submit a job in exclusive mode, we will have to charge you for all the cores on the node, not just the ones you asked to use, for the lifetime of your job (since no one else can use those cores). Thus, the funds in your allocation will be depleted faster.
The default behavior on the Deepthought2 cluster
is that for jobs specifying a single task (
-n 1 and do not change the default cpus-per-task setting),
i.e. serial (single core) jobs, will get the
share flag set,
unless you explicitly submit them with the
All other jobs by default have
exclusive flag set, unless you explicitly submit them with
share flag. Large parallel jobs typically consume all the
resources on the nodes they are assigned anyway, and so effectively run in
exclusive mode regardless. They also cost the most to rerun if one node
crashes. Serial jobs would pay the highest penalty in terms
of the charge for cores not being used, so having them in
makes sense. for jobs between those extremes, the policy is somewhat
conservative, but allows the user to choose for himself.
If you specify a single node (
It is strongly recommended that users of the
Deepthought2 cluster explicitly set the
--exclusive flags for jobs using more than one core and
not using the entire node. In general, you will probably want to
--share flag to reduce the amount charged to your
On the MARCC/Bluecrab cluster, this setting is set
automatically depending on the
partition chosen.. The
partitions always use
--exclusive mode (and will override
any setting you give). The others default to
(although you can explicitly override this if you so desire).
Specifying a reservation
On rare occasions, a reservation might be set up for a certain allocation account. This means that certain CPU cores/nodes have been reserved for a specific period of time for that allocation. This is not done often, and when it is done it is typically reserving some nodes for a class, during class hours only, so that students can launch jobs and get the results back while the class is still in session (and the instructor is still available to assist them with issues). Again, this is only done rarely, and you should have been informed (e.g. by your instructor) if that is the case. Most users do not have access to reservations and can safely ignore this section.
If you do have access to a reservation which is active, you can submit
jobs which can use the reserved resources by adding the following flag to
your sbatch command:
where RESERVATION is the name of the reservation (which should
have been provided to you, e.g. by your instructor). If you were not informed
of a reservation name, your allocations probably do not have reservations and
this section does not apply to you. The
--reservation=RESERVATION flag can either be given
as an explicit argument on the
sbatch command line, or as a
#SBATCH --reservation=RESERVATION line in your job
NOTE: to effectively use a reservation, the following conditions must hold:
- You must be charging the job to an allocation account that has access to the reservation. For class reservations, this typically means that you must be submitting the job from your class temporary login account, and charging it to the class allocation account.
- You must specify that the job should use the reservation, i.e. use the
--reservationflag described above.
- The reservation must be "active". Class reservations are typically only active during the hours the class meets, and often only on specific days that the class is meeting. If you submit a job specifying a reservation when the reservation is not active, instead of expediting things it will likely delay the job until the reservation becomes active.