SLURM/JobSubmission
Job Submission
SLURM offers a variety of ways to run jobs. It is important to understand the different options available and how to request the resources required for a job in order for it to run successfully. All job submission should be done from submit nodes; any computational code should be run in a job allocation on compute nodes. The following commands outline how to allocate resources on the compute nodes and submit processes to be run on the allocated nodes.
The cluster that everyone with a UMIACS account has access to is Nexus. Please visit the Nexus page for instructions on how to connect to your assigned submit nodes.
Computationally intensive processes run on submission nodes will be terminated. Please submit jobs to be scheduled on compute nodes for this purpose.
For details on how SLURM decides how to schedule jobs when multiple jobs are waiting in a scheduler's queue, please see SLURM/Priority.
srun
The srun command is used to run a process on the compute nodes in the cluster. If you pass it a normal shell command (or command that executes a script), it will submit a job to run that shell command/script on a compute node and then return. srun accepts many command line options to specify the resources required by the command passed to it. Some common command line arguments are listed below and full documentation of all available options is available in the man page for srun, which can be accessed by running man srun.
$ srun --qos=default --mem=100mb --time=1:00:00 bash -c 'echo "Hello World from" `hostname`' Hello World from tron33.umiacs.umd.edu
It is important to understand that srun is an interactive command. By default input to srun is broadcast to all compute nodes running your process and output from the compute nodes is redirected to srun. This behavior can be changed; however, srun will always wait for the command passed to finish before exiting, so if you start a long running process and end your terminal session, your process will stop running on the compute nodes and your job will end. To run a non-interactive submission that will remain running after you logout, you will need to wrap your srun commands in a batch script and submit it with sbatch.
Common srun Arguments
--job-name=<JOBNAME>Requests your job be named <JOBNAME>--mem=1gRequests 1GB of memory for your job, if no unit is given MB is assumed--ntasks=2Requests 2 "tasks" which map to cores on a CPU for your job; if passed to srun, runs the given command concurrently on each core--nodes=2Requests 2 nodes be allocated to your job; if passed to srun, runs the given command concurrently on each node--nodelist=<NODENAME>Requests to run your job on the <NODENAME> node--time=dd-hh:mm:ssRequests your job run for dd days, hh hours, mm minutes, and ss seconds--error=<ERRNAME>Redirects stderr for your job to the <ERRNAME> file--partition=<PARTITIONNAME>Requests your job run in the <PARTITIONNAME> partition--qos=<QOSNAME>defaultRequests your job run with the <QOSNAME> QOS, to see the available QOS options on a cluster, runshow_qos--account=<ACCOUNTNAME>Requests your job runs under the <ACCOUNTNAME> Slurm account, different accounts have different available partitions/QOS--output=<OUTNAME>Redirects stdout for your job to the <OUTNAME> file--requeueRequests your job be automatically requeued if it is preempted--exclusiveRequests your job be the only one running on the node(s) it is assigned to. This requires that your job be allocated all of the resources on the node(s). The scheduler does not automatically give your job all of the node's/nodes' resources, however, so if you need more than the default, you still need to request these with--ntasksand--mem
Interactive Shell Sessions
An interactive shell session on a compute node can be useful for debugging or developing code that isn't ready to be run as a batch job. To get an interactive shell on a node, use srun with the --pty argument to invoke a shell:
$ srun --pty --qos=default --mem=1g --time=01:00:00 bash $ hostname tron33.umiacs.umd.edu
Please do not leave interactive shells running for long periods of time when you are not working. This blocks resources from being used by everyone else.
salloc
The salloc command can also be used to request resources be allocated without needing a batch script. Running salloc with a list of resources will allocate the resources you requested, create a job, and drop you into a subshell with the environment variables necessary to run commands in the newly created job allocation. When your time is up or you exit the subshell, your job allocation will be relinquished.
$ salloc --qos=default -N 1 --mem=2g --time=01:00:00 salloc: Granted job allocation 159 $ srun /usr/bin/hostname tron33.umiacs.umd.edu $ exit exit salloc: Relinquishing job allocation 159
Please note that any commands not invoked with srun will be run locally on the submit node. Please be careful when using salloc.
sbatch
The sbatch command allows you to write a batch script to be submitted and run non-interactively on the compute nodes. To run a simple Hello World command on the compute nodes you could write a file, helloWorld.sh with the following contents:
#!/bin/bash srun bash -c 'echo Hello World from `hostname`'
Then you need to submit the script with sbatch and request resources:
$ sbatch --qos=default --mem=1g --time=1:00:00 helloWorld.sh Submitted batch job 121
SLURM will return a job number that you can use to check the status of your job with squeue:
$ squeue
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
121 tron helloWor username R 0:01 1 tron32
Advanced Batch Scripts
You can also write a batch script with all of your resources/options defined in the script itself. This is useful for jobs that need to be run tens/hundreds/thousands of times. You can then handle any necessary environment setup and run commands on the resources you requested by invoking commands with srun. The srun commands can also be more complex and be told to only use portions of your entire job allocation - each of these distinct srun commands makes up one "job step". The batch script will be run on the first node allocated as part of your job allocation and each job step will be run on whatever resources you tell them to.
In the following example, we have a batch job that will request 2 nodes in the cluster. We then load a specific version of Python into our environment and submit two job steps, each one using one node. Since srun blocks until the command finishes by default, we use the '&' operator to background the process so that both job steps can run at once; however, this means that we then need to use the wait command to block processing until all background processes have finished.
#!/bin/bash # Lines that begin with #SBATCH specify commands to be used by SLURM for scheduling. These MUST be above any non-#SBATCH lines in the script to take effect. #SBATCH --job-name=helloWorld # set job name #SBATCH --output=helloWorld.out.%j # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. #SBATCH --error=helloWorld.err.%j # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. #SBATCH --time=00:05:00 # how long you would like your job to run; format=dd-hh:mm:ss #SBATCH --account=nexus # set account, this determines which partitions and QOSes are available for your job #SBATCH --partition=tron # set partition, this determines what nodes are available for your job #SBATCH --qos=default # set QOS, this determines how many resources can be requested within your job, and for how long #SBATCH --nodes=2 # number of nodes to allocate for your job #SBATCH --ntasks=4 # request 4 cpu cores be reserved for your job total #SBATCH --ntasks-per-node=2 # request 2 cpu cores be reserved per node #SBATCH --mem=1g # memory required by job; if unit is not specified MB will be assumed. for multi-node jobs, this argument allocates this much memory *per node* srun --nodes=1 --mem=512m bash -c "hostname; python3 --version" & # use srun to invoke commands within your job; using an '&' srun --nodes=1 --mem=512m bash -c "hostname; python3 --version" & # will background the process allowing them to run concurrently wait # wait for any background processes to complete # Once the end of the batch script is reached, your job allocation will be revoked.
Another useful thing to know is that you can pass additional arguments into your sbatch scripts on the command line and reference them as ${1} for the first argument and so on.
More Examples
scancel
The scancel command can be used to cancel job allocations or job steps that are no longer needed. It can be passed individual job IDs or an option to delete all of your jobs or jobs that meet certain criteria.
scancel 255cancel job 255scancel 255.3cancel job step 3 of job 255scancel --user username --partition=troncancel all jobs for username in the tron partition
Identifying Resources and Features
The sinfo command can show you additional features of nodes in the cluster but you need to ask it to show some non-default options using a command like sinfo -o "%40N %8c %8m %35f %35G".
$ sinfo -o "%40N %10c %10m %40f %32G" NODELIST CPUS MEMORY AVAIL_FEATURES GRES cbcb[00-21] 32 2061175 rhel8,x86_64,Zen,EPYC-7313 (null) cbcb22,cmlcpu[00,06-07],legacy20 24+ 384270+ rhel8,x86_64,Xeon,E5-2680 (null) cbcb26 128 513243 rhel8,x86_64,Zen,EPYC-7763,Ampere gpu:rtxa5000:7 cbcb27 64 255167 rhel8,x86_64,Zen,EPYC-7513,Ampere gpu:rtxa6000:8 cbcb[28-29] 32 771166 rhel8,x86_64,Zen,EPYC-9124,Ada gpu:rtx6000ada:8 legacy00 48 125940 rhel8,x86_64,Zen,EPYC-7402 (null) cbcb[23-24],legacy[34-35],twist05 24 255150 rhel8,x86_64,Xeon,E5-2650 (null) cbcb25 24 255278 rhel8,x86_64,Xeon,E5-2650,Pascal,Turing gpu:rtx2080ti:1,gpu:gtx1080ti:1 legacy[01-11,13-19,22-28,30] 12+ 61804+ rhel8,x86_64,Xeon,E5-2620 (null) legacy21 8 61746 rhel8,x86_64,Xeon,E5-2623 (null) legacy31 8 61727 rhel8,x86_64,Xeon,E5-1660 (null) tron[46-61] 48 255232 rhel8,x86_64,Zen,EPYC-7352,Ampere gpu:rtxa5000:8 tron[06-09,12-15,21] 16 126214+ rhel8,x86_64,Zen,EPYC-7302P,Ampere gpu:rtxa4000:4 tron[10-11,16-20,34] 16 126217 rhel8,x86_64,Zen,EPYC-7313P,Ampere gpu:rtxa4000:4 tron[22-33,35-44] 16 126214+ rhel8,x86_64,Zen,EPYC-7302,Ampere gpu:rtxa4000:4 clip13,cml30,vulcan[29-30,32,45] 32 255218+ rhel8,x86_64,Zen,EPYC-7313,Ampere gpu:rtxa6000:8 cml[17-28],gammagpu05 32 255225+ rhel8,x86_64,Zen,EPYC-7282,Ampere gpu:rtxa4000:8 cml12 32 383038 rhel8,x86_64,Xeon,4216,Turing,Ampere gpu:rtx2080ti:7,gpu:rtxa4000:1 cml31 32 384094 rhel8,x86_64,Zen,EPYC-9124,Ampere,Hopper gpu:a100:1,gpu:h100-nvl:1 cml32 64 512999 rhel8,x86_64,Zen,EPYC-7543,Ampere gpu:a100:4 cml33 64 1029019 rhel8,x86_64,Xeon,6448Y,Hopper gpu:h100-sxm:4 cmlcpu[01-04] 20 384271 rhel8,x86_64,Xeon,E5-2660 (null) cml[00,15-16] 32 351530+ rhel8,x86_64,Xeon,4216,Turing gpu:rtx2080ti:7 cml01 32 383030 rhel8,x86_64,Xeon,4216,Turing gpu:rtx2080ti:6 cml[02-11,13],tron[62-63,65-66,68-69] 32 351770+ rhel8,x86_64,Xeon,4216,Turing gpu:rtx2080ti:8 clip12,gammagpu[10-17] 16 126203+ rhel8,x86_64,Zen,EPYC-7313,Ampere gpu:rtxa6000:4 gammagpu[18-21] 32 254883 rhel8,x86_64,Xeon,6526Y,Ada gpu:l40s:4 gammagpu00 32 255233 rhel8,x86_64,Zen,EPYC-7302,Ampere gpu:rtxa5000:8 legacygpu06 20 255249 rhel8,x86_64,Xeon,E5-2699,Maxwell gpu:gtxtitanx:4 legacygpu[01-02,07] 20 255249+ rhel8,x86_64,Xeon,E5-2650,Maxwell gpu:gtxtitanx:4 legacygpu05 44 513193 rhel8,x86_64,Xeon,E5-2699,Pascal gpu:gtx1080ti:4 legacygpu00 20 255249 rhel8,x86_64,Xeon,E5-2650,Pascal gpu:titanxp:4 legacygpu[03-04] 16 255268 rhel8,x86_64,Xeon,E5-2630,Maxwell gpu:gtxtitanx:2 mbrc[00-01] 20 189498 rhel8,x86_64,Xeon,4114,Turing gpu:rtx2080ti:8 clip03 20 126243 rhel8,x86_64,Xeon,E5-2630,Pascal,Turing gpu:rtx2080ti:1,gpu:gtx1080ti:2 clip04 32 255233 rhel8,x86_64,Zen,EPYC-7302,Ampere gpu:rtx3090:4 clip[05-06] 24 126216 rhel8,x86_64,Zen,EPYC-7352,Ampere gpu:rtxa6000:2 clip09 32 383043 rhel8,x86_64,Xeon,6130,Pascal,Turing gpu:rtx2080ti:5,gpu:gtx1080ti:3 clip10 44 1029404 rhel8,x86_64,Xeon,E5-2699 (null) clip11 16 126217 rhel8,x86_64,Zen,EPYC-7313,Ampere gpu:rtxa4000:4 quics00 128 1545009 rhel8,x86_64,Zen,EPYC-9534 (null) tron00 32 255233 rhel8,x86_64,Zen,EPYC-7302,Ampere gpu:rtxa6000:6 tron[01-03,05] 32 255233 rhel8,x86_64,Zen,EPYC-7302,Ampere gpu:rtxa6000:8 tron04 32 255233 rhel8,x86_64,Zen,EPYC-7302,Ampere gpu:rtxa6000:7 tron[64,67] 32 383028+ rhel8,x86_64,Xeon,4216,Turing,Ampere gpu:rtx2080ti:7,gpu:rtx3070:1 clip00 32 255276 rhel8,x86_64,Xeon,E5-2683,Pascal gpu:titanxpascal:3 clip01 32 255276 rhel8,x86_64,Xeon,E5-2683,Pascal gpu:titanxpascal:1,gpu:titanxp:2 clip02 20 126255 rhel8,x86_64,Xeon,E5-2630,Pascal gpu:gtx1080ti:3 clip07 8 255263 rhel8,x86_64,Xeon,E5-2623,Pascal gpu:gtx1080ti:3 oasis[00-39] 160 28114 rhel9,aarch64,Altra,Altra-80 (null) vulcan24 16 126216 rhel8,x86_64,Zen,EPYC-7282,Ampere gpu:rtxa6000:4 gammagpu[01-04,06-07,09],vulcan[33-37] 32 255215+ rhel8,x86_64,Zen,EPYC-7313,Ampere gpu:rtxa5000:8 vulcan[38-44] 32 255215 rhel8,x86_64,Zen,EPYC-7313,Ampere gpu:rtxa4000:8 brigid[16-17] 48 512897 rhel8,x86_64,Zen,EPYC-7443 (null) vulcan23 32 383030 rhel8,x86_64,Xeon,4612,Turing gpu:rtx2080ti:8 vulcan[27-28] 56 770093 rhel8,x86_64,Xeon,8280,Turing gpu:rtx2080ti:10 brigid[18-19] 20 61739 rhel8,x86_64,Xeon,E5-2640 (null) vulcan00 32 255259 rhel8,x86_64,Xeon,E5-2683,Pascal gpu:p6000:7,gpu:p100:1 vulcan[01-02,04,06-07] 32 255259 rhel8,x86_64,Xeon,E5-2683,Pascal gpu:p6000:8 vulcan[03,05] 32 255259 rhel8,x86_64,Xeon,E5-2683,Pascal gpu:p6000:7 clip08,vulcan[08-16,18,20-22,25] 32 255258+ rhel8,x86_64,Xeon,E5-2683,Pascal gpu:gtx1080ti:8 vulcan[17,19] 32 255259 rhel8,x86_64,Xeon,E5-2683,Pascal gpu:gtx1080ti:7 vulcan26 24 770126 rhel8,x86_64,Xeon,6146,Pascal gpu:titanxp:10
Note that all of the nodes shown by this may not necessarily be in a partition you are able to submit to.
You can identify further specific information about a node using scontrol with various flags.
There are also two command aliases developed by UMIACS staff to show various node information in aggregate. They are show_nodes and show_available_nodes.
show_nodes
The show_nodes command alias shows each node's name, number of CPUs, memory, {OS, CPU architecture, CPU type, GPU architecture (if the node has GPUs)} (as AVAIL_FEATURES), GRES (GPUs), and State. It essentially wraps the sinfo command with some pre-determined output format options and shows each node on its own line, in alphabetical order.
To only view nodes in a specific partition, append -p <partition name> to the command alias.
Examples
$ show_nodes NODELIST CPUS MEMORY AVAIL_FEATURES GRES STATE brigid16 48 512897 rhel8,x86_64,Zen,EPYC-7443 (null) idle brigid17 48 512897 rhel8,x86_64,Zen,EPYC-7443 (null) idle ... ... ... ... ... ... vulcan45 32 513250 rhel8,x86_64,Zen,EPYC-7313,Ampere gpu:rtxa6000:8 idle
(specific partition)
$ show_nodes -p tron NODELIST CPUS MEMORY AVAIL_FEATURES GRES STATE tron00 32 255233 rhel8,x86_64,Zen,EPYC-7302,Ampere gpu:rtxa6000:8 idle tron01 32 255233 rhel8,x86_64,Zen,EPYC-7302,Ampere gpu:rtxa6000:8 idle ... ... ... ... ... ... tron69 32 383030 rhel8,x86_64,Xeon,4216,Turing gpu:rtx2080ti:8 idle
show_available_nodes
The show_available_nodes command alias takes zero or more arguments that correspond to Slurm constructs, resources, or features that you are looking to request a job with and tells you what nodes could theoretically[0,1] run a job with these arguments immediately. It assumes your job is a single-node job.
These arguments are:
--partition: Only include nodes in the specified partition(s).--account: Only include nodes from partitions that can use the specified account(s).--qos: Only include nodes from partitions that can use the specified QoS(es).--cpus: Only include nodes with at least this many CPUs free.--mem: Only include nodes with at least this much memory free. The default unit is MB if unspecified, but any of {K,M,G,T} can be suffixed to the number provided (will then be interpreted as KB, MB, GB, or TB, respectively).- GRES-related arguments:
--gres,--and-gres: Only include nodes whose list of GRES contains all of the specified GRES type/quantity pairings.--or-gres: Only include nodes whose list of GRES contains any of the specified GRES type/quantity pairings. Functionally identical to --and-gres if only one GRES type/quantity pairing is specified.
- GPU-related arguments:
--gpus,--and-gpus: Only include nodes whose list of GPUs (a subset of GRES) contains all of the specified GPU type/quantity pairings.--or-gpus: Only include nodes whose list of GPUs (a subset of GRES) contains any of the specified GPU type/quantity pairings. Functionally identical to --and-gpus if only one GPU type/quantity pairing is specified.
- Feature-related arguments:
--feature,--and-feature: Only include nodes whose list of features contains all of the specified feature(s).--or-feature: Only include nodes whose list of features contains any of the specified feature(s). Functionally identical to --and-feature if only one feature is specified.
These arguments are also viewable by running show_available_nodes -h.
If your passed argument set does not contain any resource-based arguments (CPUs/RAM/GRES or GPUs), a node is defined as available if it has at least 1 CPU and 1MB of RAM available.
If there are no nodes available that meet your passed argument set, you will receive the message There are no nodes that have currently free resources that meet this argument set.
Footnotes
[0] - As of now, this command alias does not factor in resources occupied by jobs that could be preempted (based on the partition(s) passed to it, if present). This is soon to come.
[1] - This command alias also does not factor in jobs with higher priority values requesting more resources, in the same partition(s), blocking execution of a job submitted with the arguments checked by the command alias. This is due to the complexity of calculating a job's priority value before it is actually submitted.
Examples
Show all available nodes:
$ show_available_nodes brigid17 cpus=16,mem=414593M brigid18 cpus=8,mem=24875M ...
Show nodes available in the tron partition:
$ show_available_nodes --partition tron tron00 cpus=14,mem=50433M,gres=gpu:rtxa6000:1 tron01 cpus=10,mem=17665M,gres=gpu:rtxa6000:2 ...
Show nodes with one or more RTX A5000 or RTX A6000 GPUs available to the vulcan account:
$ show_available_nodes --account vulcan --or-gpus rtxa5000:1,rtxa6000:1 vulcan32 cpus=16,mem=193778M,gres=gpu:rtxa6000:4 vulcan33 cpus=15,mem=181499M,gres=gpu:rtxa5000:3 ...
Show nodes with 4 or more CPUs, 48G or more memory, and one or more RTX A6000 GPUs available in the scavenger partition:
$ show_available_nodes --partition=scavenger --cpus=4 --mem=48g --or-gpus=rtxa6000:1 cbcb27 cpus=59,mem=218303M,gres=gpu:rtxa6000:6 clip06 cpus=20,mem=93448M,gres=gpu:rtxa6000:1 ...
Show nodes with Turing or Ampere architecture GPUs available in the scavenger partition:
$ show_available_nodes --partition=scavenger --or-feature=Ampere,Turing cbcb25 cpus=24,mem=255278M,gres=gpu:rtx2080ti:1,gpu:gtx1080ti:1 cbcb26 cpus=127,mem=447707M,gres=gpu:rtxa5000:7 ...
Show nodes with Zen architecture CPUs and Ampere architecture GPUs available in the scavenger partition:
$ show_available_nodes --partition=scavenger --and-feature=Zen,Ampere cbcb26 cpus=127,mem=447707M,gres=gpu:rtxa5000:7 cbcb27 cpus=59,mem=218303M,gres=gpu:rtxa6000:6 ...
(bogus example) Attempt to show nodes available in the bogus partition:
$ show_available_nodes --partition=bogus There are no nodes that have currently free resources that meet this argument set.
Requesting GPUs
If you need to do processing on a GPU, you will need to request that your job have access to GPUs just as you need to request processors or CPU cores. In SLURM, GPUs are considered "generic resources" also known as GRES. To request some number of GPUs be reserved/available for your job, you can use the flag --gres=gpu:# (with the actual number of GPUs you want). If there are multiple types of GPUs available in the cluster and you need a specific type, you can provide the type option to the gres flag e.g. --gres=gpu:rtxa5000:#. If you do not request a specific type of GPU, you are likely to be scheduled on an older, lower spec'd GPU.
Note that some QoSes may have limits on the number of GPUs you can request per job, so you may need to specify a different QoS to request more GPUs.
$ srun --pty --qos=medium --gres=gpu:2 nvidia-smi ... Wed Mar 6 16:59:39 2024 +---------------------------------------------------------------------------------------+ | NVIDIA-SMI 535.129.03 Driver Version: 535.129.03 CUDA Version: 12.2 | |-----------------------------------------+----------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+======================+======================| | 0 NVIDIA GeForce RTX 2080 Ti Off | 00000000:3D:00.0 Off | N/A | | 32% 23C P8 1W / 250W | 0MiB / 11264MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ | 1 NVIDIA GeForce RTX 2080 Ti Off | 00000000:40:00.0 Off | N/A | | 32% 25C P8 1W / 250W | 0MiB / 11264MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| | No running processes found | +---------------------------------------------------------------------------------------+
Please note that your job will only be able to see/access the GPUs you requested. If you only need 1 GPU, please only request 1 GPU. The others on the node (if any) will be left available for other users.
$ srun --pty --gres=gpu:rtxa5000:1 nvidia-smi Thu Aug 25 15:22:15 2022 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 470.129.06 Driver Version: 470.129.06 CUDA Version: 11.4 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 NVIDIA RTX A5000 Off | 00000000:01:00.0 Off | Off | | 30% 23C P8 20W / 230W | 0MiB / 24256MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
As with all other flags, the --gres flag may also be passed to sbatch and salloc rather than directly to srun.
MPI example
To run MPI jobs, you will need to include the --mpi=pmix flag in your submission arguments.
#!/usr/bin/bash #SBATCH --job-name=mpi_test # Job name #SBATCH --nodes=4 # Number of nodes #SBATCH --ntasks=8 # Number of MPI ranks #SBATCH --ntasks-per-node=2 # Number of MPI ranks per node #SBATCH --ntasks-per-socket=1 # Number of tasks per processor socket on the node #SBATCH --time=00:30:00 # Time limit hrs:min:sec srun --mpi=pmix /nfshomes/username/testing/mpi/a.out