Latest revision as of 16:49, 12 December 2024

General information about Slurm

Any kind of calculation on the compute nodes of bwForCluster BinAC 2 requires the user to define calculations as a sequence of commands or single command together with required run time, number of CPU cores and main memory and submit all, i.e., the batch job, to a resource and workload managing software. BinAC 2 has installed the workload managing software Slurm. Therefore any job submission by the user is to be executed by commands of the Slurm software. Slurm queues and runs user jobs based on fair sharing policies.

External Slurm documentation

You can find the official Slurm configuration and some other material here:

Slurm documentation: https://slurm.schedmd.com/documentation.html
Slurm cheat sheet: https://slurm.schedmd.com/pdfs/summary.pdf
Slurm tutorials: https://slurm.schedmd.com/tutorials.html

SLURM terminology

SLURM knows and mirrors the division of the cluster into nodes with several cores. When queuing jobs, there are several ways of requesting resources and it is important to know which term means what in SLURM. Here are some basic SLURM terms:

Job: A job is a self-contained computation that may encompass multiple tasks and is given specific resources like individual CPUs/GPUs, a specific amount of RAM or entire nodes. These resources are said to have been allocated for the job.

Task: A task is a single run of a single process. By default, one task is run per node and one CPU is assigned per task.

Partition: A partition (usually called queue outside SLURM) is a waiting line in which jobs are put by users.

Socket: Receptacle on the motherboard for one physically packaged processor (each of which can contain one or more cores).

Core: A complete private set of registers, execution units, and retirement queues needed to execute programs.

Thread: One or more hardware contexts withing a single core. Each thread has attributes of one core, managed & scheduled as a single logical processor by the OS.

CPU: A CPU in Slurm means a single core. This is different from the more common terminology, where a CPU (a microprocessor chip) consists of multiple cores. Slurm uses the term sockets when talking about CPU chips. Depending upon system configuration, a CPU can be either a core or a thread. On BinAC 2 Hyperthreading is activated on every machine. This means that the operating system and Slurm sees each physical core as two logical cores.

Slurm Commands

Slurm commands	Brief explanation
sbatch	Submits a job and queues it in an input queue
saclloc	Request resources for an interactive job
squeue	Displays information about active, eligible, blocked, and/or recently completed jobs
scontrol	Displays detailed job state information
sstat	Displays status information about a running job
scancel	Cancels a job

Job Submission : sbatch

Batch jobs are submitted by using the command sbatch. The main purpose of the sbatch command is to specify the resources that are needed to run the job. sbatch will then queue the batch job. However, starting of batch job depends on the availability of the requested resources and the fair sharing value.

sbatch Command Parameters

The syntax and use of sbatch can be displayed via:

$ man sbatch

sbatch options can be used from the command line or in your job script. The following table shows the syntax and provides examples for each option.

sbatch Options
Command line	Job Script	Purpose	Example	Default value
`-t time` or `--time=time`	#SBATCH --time=time	Wall clock time limit.	`-t 2:30:00` Limits run time to 2h 30 min. `-t 2-12` Limits run time to 2 days and 12 hours.	Depends on Slurm partition.
-N count or --nodes=count	#SBATCH --nodes=count	Number of nodes to be used.	`-N 1` Run job on one node. `-N 2` Run job on two nodes (have to use MPI!)
-n count or --ntasks=count	#SBATCH --ntasks=count	Number of tasks to be launched.	`-n 2` launch two tasks in the job.	One task per node
--ntasks-per-node=count	#SBATCH --ntasks-per-node=count	Maximum count of tasks per node. (Replaces the option `ppn` of MOAB.)	`--ntasks-per-node=2` Run 2 tasks per node	1 task per node
-c count or --cpus-per-task=count	#SBATCH --cpus-per-task=count	Number of CPUs required per (MPI-)task.	`-c 2` Request two CPUs per (MPI-)task.	1 CPU per (MPI-)task
`--mem=<size>[units]`	#SBATCH --mem=value_in_MB	Memory in MegaByte per node.	Example `--mem=10g` Request all memory on node	Depends on Slurm configuration. It's better to specify `--mem` in every case.
--mem-per-cpu=value_in_MB	#SBATCH --mem-per-cpu=value_in_MB	Minimum Memory required per allocated CPU. (Replaces the option pmem of MOAB. You should omit the setting of this option.)
--mail-type=type	#SBATCH --mail-type=type	Notify user by email when certain event types occur. Valid type values are NONE, BEGIN, END, FAIL, REQUEUE, ALL.
--mail-user=mail-address	#SBATCH --mail-user=mail-address	The specified mail-address receives email notification of state changes as defined by --mail-type.
--output=name	#SBATCH --output=name	File in which job output is stored.
--error=name	#SBATCH --error=name	File in which job error messages are stored.
-J name or --job-name=name	#SBATCH --job-name=name	Job name.
--export=[ALL,] env-variables	#SBATCH --export=[ALL,] env-variables	Identifies which environment variables from the submission environment are propagated to the launched application. Default is ALL. If adding an environment variable to the submission environment is intended, the argument ALL must be added.
-A group-name or --account=group-name	#SBATCH --account=group-name	Change resources used by this job to specified group. You may need this option if your account is assigned to more than one group. By command "scontrol show job" the project group the job is accounted on can be seen behind "Account=".
-p queue-name or --partition=queue-name	#SBATCH --partition=queue-name	Request a specific queue for the resource allocation.
--reservation=reservation-name	#SBATCH --reservation=reservation-name	Use a specific reservation for the resource allocation.
-C LSDF or --constraint=LSDF	#SBATCH --constraint=LSDF	Job constraint LSDF Filesystems.

sbatch --partition queues

Queue classes define maximum resources such as walltime, nodes and processes per node and queue of the compute system. Details can be found here:

BinAC 2 partitions

sbatch Examples

If you are coming from Moab/Torque on BinAC 1 or you are new to HPC/Slurm the sbatch options may confuse you. The following examples give an orientation how to run typical workloads on BinAC 2.

You can find every file mentioned on this Wiki page on BinAC 2 at: /pfs/10/project/examples

Serial Programs

When you use serial programs that use only one process, you can omit most of the sbatch parameters, as the default values are sufficient.

To submit a serial job that runs the script serial_job.sh and requires 5000 MB of main memory and 10 minutes of wall clock time, Slurm will allocate one physical core to your job.

a) execute:

$ sbatch -p compute -t 10:00 --mem=5000m  serial_job.sh

or b) add after the initial line of your script serial_job.sh the lines:

#SBATCH --time=10:00
#SBATCH --mem=5000m
#SBATCH --job-name=simple-serial-job

and execute the modified script with the command line option --partition=compute

$ sbatch -p=compute serial_job.sh

Note, that sbatch command line options overrule script options.

Multithreaded Programs

Multithreaded programs run their processes on multiple threads and share resources such as memory.
You may use a program that includes a built-in option for multithreading (e.g., options like --threads).
For multithreaded programs based on Open Multi-Processing (OpenMP) a number of threads is defined by the environment variable OMP_NUM_THREADS. By default, this variable is set to 1 (OMP_NUM_THREADS=1).

Important: Hyperthreading is activated on bwForCluster BinAC 2. Hyperthreading can be beneficial for some applications and codes, but it can also degrade performance in other cases. We therefore recommend to run a small test job with and without hyperthreading to determine the best choice.

a) Program with built-in multithreading option

The example uses the common Bioinformatics software called samtools as example for using built-in multithreading.

The module bio/samtools/1.21 provides an example jobscript that requests 4 CPUs and runs samtools sort with 4 threads.

#!/bin/bash

#SBATCH --time=19:00
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=5000m
#SBATCH --partition compute
[...]
samtools sort -@ 4 sample.bam -o sample.sorted.bam

You can use the example jobscript with this command

sbatch /opt/bwhpc/common/bio/samtools/1.21/bwhpc-examples/binac2-samtools-1.21-bwhpc-examples.slurm

b) OpenMP

We will run an exaple OpenMP Hello-World program. The jobscript looks like this:

#!/bin/bash
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
#SBATCH --time=1:00
#SBATCH --mem=5000m   
#SBATCH -J OpenMP-Hello-World

export OMP_NUM_THREADS=$(${SLURM_JOB_CPUS_PER_NODE}/2)

echo "Executable running on ${SLURM_JOB_CPUS_PER_NODE} cores with ${OMP_NUM_THREADS} threads"

# Run parallel Hello World
/pfs/10/project/examples/openmp_hello_world

Submit the job to the compute partition and get the output (in the stdout-file)

sbatch --partition=compute /pfs/10/project/examples/openmp_hello_world.sh

Executable  running on 4 cores with 4 threads
Hello from process: 0
Hello from process: 2
Hello from process: 1
Hello from process: 3

OpenMPI

If you want to run MPI-jobs on batch nodes, generate a wrapper script mpi_hello_world.sh for OpenMPI containing the following lines:

#!/bin/bash

#SBATCH --partition compute
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=2
#SBATCH --cpus-per-task=2
#SBATCH --mem-per-cpu=2000
#SBATCH --time=05:00

# Load the MPI implementation of your choice
module load mpi/openmpi/4.1-gnu-14.2

# Run your MPI program
mpirun --bind-to core --map-by core --report-bindings mpi_hello_world

Attention: Do NOT add mpirun options -n <number_of_processes> or any other option defining processes or nodes, since Slurm instructs mpirun about number of processes and node hostnames.

Use ALWAYS the MPI options --bind-to core and --map-by core|socket|node. Please type man mpirun for an explanation of the meaning of the different options of mpirun option --map-by.

The above jobscript runs four OpenMPI tasks, distributed between two nodes. Because of hyperthreading you have to set --cpus-per-task=2. This means each MPI-task will get one physical core. If you omit --cpus-per-task=2 MPI will fail.

Attention: Not all compute nodes are connected via Infiniband. Tell Slurm you want Infiniband via --constraint=ib when submitting or add #SBATCH --constraint=ib to your jobscript.

$ sbatch --constraint=ib /pfs/10/project/examples/mpi_hello_world.sh

This will run a simple Hello World program:

[...]
Hello world from processor node2-031, rank 3 out of 4 processors
Hello world from processor node2-031, rank 2 out of 4 processors
Hello world from processor node2-030, rank 1 out of 4 processors
Hello world from processor node2-030, rank 0 out of 4 processors

Multithreaded + MPI parallel Programs

Multithreaded + MPI parallel programs operate faster than serial programs on multi CPUs with multiple cores. All threads of one process share resources such as memory. On the contrary MPI tasks do not share memory but can be spawned over different nodes. Because hyperthreading is switched on BinaC 2, the option --cpus-per-task (-c) must be set to 2*n, if you want to use n threads.

OpenMPI with Multithreading

Multiple MPI tasks using OpenMPI must be launched by the MPI parallel program mpirun. For multithreaded programs based on Open Multi-Processing (OpenMP) number of threads are defined by the environment variable OMP_NUM_THREADS. By default this variable is set to 1 (OMP_NUM_THREADS=1).
For OpenMPI a job-script to submit a batch job called job_ompi_omp.sh that runs a MPI program with 4 tasks and a 28-fold threaded program ompi_omp_program requiring 3000 MByte of physical memory per thread (using 28 threads per MPI task you will get 28*3000 MByte = 84000 MByte per MPI task) and total wall clock time of 3 hours looks like:

#!/bin/bash
#SBATCH --ntasks=4
#SBATCH --cpus-per-task=56
#SBATCH --time=03:00:00
#SBATCH --mem=83gb    # 84000 MB = 84000/1024 GB = 82.1 GB
#SBATCH --export=ALL,MPI_MODULE=mpi/openmpi/3.1,EXECUTABLE=./ompi_omp_program
#SBATCH --output="parprog_hybrid_%j.out"  

# Use when a defined module environment related to OpenMPI is wished
module load ${MPI_MODULE}
export OMP_NUM_THREADS=$((${SLURM_CPUS_PER_TASK}/2))
export MPIRUN_OPTIONS="--bind-to core --map-by socket:PE=${OMP_NUM_THREADS} -report-bindings"
export NUM_CORES=${SLURM_NTASKS}*${OMP_NUM_THREADS}
echo "${EXECUTABLE} running on ${NUM_CORES} cores with ${SLURM_NTASKS} MPI-tasks and ${OMP_NUM_THREADS} threads"
startexe="mpirun -n ${SLURM_NTASKS} ${MPIRUN_OPTIONS} ${EXECUTABLE}"
echo $startexe
exec $startexe

Execute the script job_ompi_omp.sh by command sbatch:

$ sbatch -p compute ./job_ompi_omp.sh

With the mpirun option --bind-to core MPI tasks and OpenMP threads are bound to physical cores.
With the option --map-by node:PE=<value> (neighbored) MPI tasks will be attached to different nodes and each MPI task is bound to the first core of a node. <value> must be set to ${OMP_NUM_THREADS}.
The option -report-bindings shows the bindings between MPI tasks and physical cores.
The mpirun-options --bind-to core, --map-by socket|...|node:PE=<value> should always be used when running a multithreaded MPI program.

GPU jobs

The nodes in the gpu_4 and gpu_8 queues have 4 or 8 NVIDIA Tesla V100 GPUs. Just submitting a job to these queues is not enough to also allocate one or more GPUs, you have to do so using the "--gres=gpu" parameter. You have to specifiy how many GPUs your job needs, e.g. "--gres=gpu:2" will request two GPUs.

The GPU nodes are shared between multiple jobs if the jobs don't request all the GPUs in a node and there are enough ressources to run more than one job. The individual GPUs are always bound to a single job and will not be shared between different jobs.

a) add after the initial line of your script job.sh the line including the information about the GPU usage:
#SBATCH --gres=gpu:2

#!/bin/bash
#SBATCH --ntasks=40
#SBATCH --time=02:00:00
#SBATCH --mem=4000
#SBATCH --gres=gpu:2

or b) execute:

$ sbatch -p <queue> -n 40 -t 02:00:00 --mem 4000 --gres=gpu:2 job.sh

If you start an interactive session on of the GPU nodes, you can use the "nvidia-smi" command to list the GPUs allocated to your job:

$ nvidia-smi
Sun Mar 29 15:20:05 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01    Driver Version: 440.33.01    CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla V100-SXM2...  Off  | 00000000:3A:00.0 Off |                    0 |
| N/A   29C    P0    39W / 300W |      9MiB / 32510MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Tesla V100-SXM2...  Off  | 00000000:3B:00.0 Off |                    0 |
| N/A   30C    P0    41W / 300W |      8MiB / 32510MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0     14228      G   /usr/bin/X                                     8MiB |
|    1     14228      G   /usr/bin/X                                     8MiB |
+-----------------------------------------------------------------------------+

In case of using Open MPI, the underlying communication infrastructure (UCX and Open MPI's BTL) is CUDA-aware. However, there may be warnings, e.g. when running

$ module load compiler/gnu/10.3 mpi/openmpi devel/cuad
$ mpirun mpirun -np 2 ./mpi_cuda_app
--------------------------------------
WARNING: There are more than one active ports on host 'uc2n520', but the
default subnet GID prefix was detected on more than one of these
ports.  If these ports are connected to different physical IB
networks, this configuration will fail in Open MPI.  This version of
Open MPI requires that every physically separate IB subnet that is
used between connected MPI processes must have different subnet ID
values.

Please see this FAQ entry for more details:

  http://www.open-mpi.org/faq/?category=openfabrics#ofa-default-subnet-gid

NOTE: You can turn off this warning by setting the MCA parameter
      btl_openib_warn_default_gid_prefix to 0.
--------------------------------------------------------------------------

Please run Open MPI's mpirun using the following command:

$ mpirun --mca pml ucx --mca btl_openib_warn_default_gid_prefix 0 -np 2 ./mpi_cuda_app

or disabling the (older) communication layer Bit-Transfer-Layer (short BTL) alltogether:

$ mpirun --mca pml ucx --mca btl ^openib -np 2 ./mpi_cuda_app

(Please note, that CUDA per v11.4 is only available with up to GCC-10)

Start time of job or resources : squeue --start

The command can be used by any user to displays the estimated start time of a job based a number of analysis types based on historical usage, earliest available reservable resources, and priority based backlog. The command squeue is explained in detail on the webpage https://slurm.schedmd.com/squeue.html or via manpage (man squeue).

Access

By default, this command can be run by any user.

List of your submitted jobs : squeue

Displays information about YOUR active, pending and/or recently completed jobs. The command displays all own active and pending jobs. The command squeue is explained in detail on the webpage https://slurm.schedmd.com/squeue.html or via manpage (man squeue).

Access

By default, this command can be run by any user.

Flags

Flag	Description
-l, --long	Report more of the available information for the selected jobs or job steps, subject to any constraints specified.

Examples

squeue example on BinaC 2 (Only your own jobs are displayed!).

$ squeue 
             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
          18088744    single CPV.sbat   ab1234 PD       0:00      1 (Priority)
          18098414  multiple CPV.sbat   ab1234 PD       0:00      2 (Priority) 
          18090089  multiple CPV.sbat   ab1234  R       2:27      2 uc2n[127-128]
$ squeue -l
            JOBID PARTITION     NAME     USER    STATE       TIME TIME_LIMI  NODES NODELIST(REASON) 
         18088654    single CPV.sbat   ab1234 COMPLETI       4:29   2:00:00      1 uc2n374
         18088785    single CPV.sbat   ab1234  PENDING       0:00   2:00:00      1 (Priority)
         18098414  multiple CPV.sbat   ab1234  PENDING       0:00   2:00:00      2 (Priority)
         18088683    single CPV.sbat   ab1234  RUNNING       0:14   2:00:00      1 uc2n413

The output of squeue shows how many jobs of yours are running or pending and how many nodes are in use by your jobs.

Shows free resources : sinfo_t_idle

The Slurm command sinfo is used to view partition and node information for a system running Slurm. It incorporates down time, reservations, and node state information in determining the available backfill window. The sinfo command can only be used by the administrator.
SCC has prepared a special script (sinfo_t_idle) to find out how many processors are available for immediate use on the system. It is anticipated that users will use this information to submit jobs that meet these criteria and thus obtain quick job turnaround times.

Access

By default, this command can be used by any user or administrator.

Example

The following command displays what resources are available for immediate use for the whole partition.

$ sinfo_t_idle
Partition dev_multiple  :      8 nodes idle
Partition multiple      :    332 nodes idle
Partition dev_single    :      4 nodes idle
Partition single        :     76 nodes idle
Partition long          :     80 nodes idle
Partition fat           :      5 nodes idle
Partition dev_special   :    342 nodes idle
Partition special       :    342 nodes idle
Partition dev_multiple_e:      7 nodes idle
Partition multiple_e    :    335 nodes idle
Partition gpu_4         :     12 nodes idle
Partition gpu_8         :      6 nodes idle

For the above example jobs in all partitions can be run immediately.

Detailed job information : scontrol show job

scontrol show job displays detailed job state information and diagnostic output for all or a specified job of yours. Detailed information is available for active, pending and recently completed jobs. The command scontrol is explained in detail on the webpage https://slurm.schedmd.com/scontrol.html or via manpage (man scontrol).
Display the state of all your jobs in normal mode: scontrol show job
Display the state of a job with <jobid> in normal mode: scontrol show job <jobid>

Access

End users can use scontrol show job to view the status of their own jobs only.

Arguments

Option	Default	Description	Example
-d	(n/a)	Detailed mode	Example: Display the state with jobid 18089884 in detailed mode. scontrol -d show job 18089884

Scontrol show job Example

Here is an example from BinAC 2.

squeue    # show my own jobs (here the userid is replaced!)
             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
          18089884  multiple CPV.sbat   bq0742  R      33:44      2 uc2n[165-166]

$
$ # now, see what's up with my pending job with jobid 18089884
$ 
$ scontrol show job 18089884

JobId=18089884 JobName=CPV.sbatch
   UserId=bq0742(8946) GroupId=scc(12345) MCS_label=N/A
   Priority=3 Nice=0 Account=kit QOS=normal
   JobState=RUNNING Reason=None Dependency=(null)
   Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0
   RunTime=00:35:06 TimeLimit=02:00:00 TimeMin=N/A
   SubmitTime=2020-03-16T14:14:54 EligibleTime=2020-03-16T14:14:54
   AccrueTime=2020-03-16T14:14:54
   StartTime=2020-03-16T15:12:51 EndTime=2020-03-16T17:12:51 Deadline=N/A
   SuspendTime=None SecsPreSuspend=0 LastSchedEval=2020-03-16T15:12:51
   Partition=multiple AllocNode:Sid=uc2n995:5064
   ReqNodeList=(null) ExcNodeList=(null)
   NodeList=uc2n[165-166]
   BatchHost=uc2n165
   NumNodes=2 NumCPUs=160 NumTasks=80 CPUs/Task=1 ReqB:S:C:T=0:0:*:1
   TRES=cpu=160,mem=96320M,node=2,billing=160
   Socks/Node=* NtasksPerN:B:S:C=40:0:*:1 CoreSpec=*
   MinCPUsNode=40 MinMemoryCPU=1204M MinTmpDiskNode=0
   Features=(null) DelayBoot=00:00:00
   OverSubscribe=NO Contiguous=0 Licenses=(null) Network=(null)
   Command=/pfs/data5/home/kit/scc/bq0742/git/CPV/bin/CPV.sbatch
   WorkDir=/pfs/data5/home/kit/scc/bq0742/git/CPV/bin
   StdErr=/pfs/data5/home/kit/scc/bq0742/git/CPV/bin/slurm-18089884.out
   StdIn=/dev/null
   StdOut=/pfs/data5/home/kit/scc/bq0742/git/CPV/bin/slurm-18089884.out
   Power=
   MailUser=(null) MailType=NONE

You can use standard Linux pipe commands to filter the very detailed scontrol show job output.

In which state the job is?

$ scontrol show job 18089884 | grep -i State
   JobState=COMPLETED Reason=None Dependency=(null)

Cancel Slurm Jobs

The scancel command is used to cancel jobs. The command scancel is explained in detail on the webpage https://slurm.schedmd.com/scancel.html or via manpage (man scancel).

Canceling own jobs : scancel

scancel is used to signal or cancel jobs, job arrays or job steps. The command is:

$ scancel [-i] <job-id>
$ scancel -t <job_state_name>

Flag	Default	Description	Example
-i, --interactive	(n/a)	Interactive mode.	Cancel the job 987654 interactively. scancel -i 987654
-t, --state	(n/a)	Restrict the scancel operation to jobs in a certain state. "job_state_name" may have a value of either "PENDING", "RUNNING" or "SUSPENDED".	Cancel all jobs in state "PENDING". scancel -t "PENDING"

Resource Managers

Batch Job (Slurm) Variables

The following environment variables of Slurm are added to your environment once your job has started (only an excerpt of the most important ones).

Environment	Brief explanation
SLURM_JOB_CPUS_PER_NODE	Number of processes per node dedicated to the job
SLURM_JOB_NODELIST	List of nodes dedicated to the job
SLURM_JOB_NUM_NODES	Number of nodes dedicated to the job
SLURM_MEM_PER_NODE	Memory per node dedicated to the job
SLURM_NPROCS	Total number of processes dedicated to the job
SLURM_CLUSTER_NAME	Name of the cluster executing the job
SLURM_CPUS_PER_TASK	Number of CPUs requested per task
SLURM_JOB_ACCOUNT	Account name
SLURM_JOB_ID	Job ID
SLURM_JOB_NAME	Job Name
SLURM_JOB_PARTITION	Partition/queue running the job
SLURM_JOB_UID	User ID of the job's owner
SLURM_SUBMIT_DIR	Job submit folder. The directory from which sbatch was invoked.
SLURM_JOB_USER	User name of the job's owner
SLURM_RESTART_COUNT	Number of times job has restarted
SLURM_PROCID	Task ID (MPI rank)
SLURM_NTASKS	The total number of tasks available for the job
SLURM_STEP_ID	Job step ID
SLURM_STEP_NUM_TASKS	Task count (number of MPI ranks)
SLURM_JOB_CONSTRAINT	Job constraints

Job Exit Codes

A job's exit code (also known as exit status, return code and completion code) is captured by SLURM and saved as part of the job record.
Any non-zero exit code will be assumed to be a job failure and will result in a Job State of FAILED with a reason of "NonZeroExitCode".
The exit code is an 8 bit unsigned number ranging between 0 and 255. While it is possible for a job to return a negative exit code, SLURM will display it as an unsigned value in the 0 - 255 range.

Displaying Exit Codes and Signals

SLURM displays a job's exit code in the output of the scontrol show job and the sview utility.
When a signal was responsible for a job or step's termination, the signal number will be displayed after the exit code, delineated by a colon(:).

Submitting Termination Signal

Here is an example, how to 'save' a Slurm termination signal in a typical jobscript.

[...]
exit_code=$?
mpirun  -np <#cores>  <EXE_BIN_DIR>/<executable> ... (options)  2>&1
[ "$exit_code" -eq 0 ] && echo "all clean..." || \
   echo "Executable <EXE_BIN_DIR>/<executable> finished with exit code ${$exit_code}"
[...]

Do not use 'time' mpirun! The exit code will be the one submitted by the first (time) program.
You do not need an exit $exit_code in the scripts.

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BinAC2/Slurm: Difference between revisions

Latest revision as of 16:49, 12 December 2024

Contents

General information about Slurm

External Slurm documentation

SLURM terminology

Slurm Commands

Job Submission : sbatch

sbatch Command Parameters

sbatch --partition queues

sbatch Examples

Serial Programs

Multithreaded Programs

OpenMPI

Multithreaded + MPI parallel Programs

OpenMPI with Multithreading

GPU jobs

Start time of job or resources : squeue --start

Access

List of your submitted jobs : squeue

Access

Flags

Examples

Shows free resources : sinfo_t_idle

Access

Example

Detailed job information : scontrol show job

Access

Arguments

Scontrol show job Example

Cancel Slurm Jobs

Canceling own jobs : scancel

Resource Managers

Batch Job (Slurm) Variables

Job Exit Codes

Displaying Exit Codes and Signals

Submitting Termination Signal

Navigation menu

BinAC2/Slurm: Difference between revisions

Latest revision as of 16:49, 12 December 2024

General information about Slurm

External Slurm documentation

SLURM terminology

Slurm Commands

Job Submission : sbatch

sbatch Command Parameters

sbatch --partition queues

sbatch Examples

Serial Programs

Multithreaded Programs

OpenMPI

Multithreaded + MPI parallel Programs

OpenMPI with Multithreading

GPU jobs

Start time of job or resources : squeue --start

Access

List of your submitted jobs : squeue

Access

Flags

Examples

Shows free resources : sinfo_t_idle

Access

Example

Detailed job information : scontrol show job

Access

Arguments

Scontrol show job Example

Cancel Slurm Jobs

Canceling own jobs : scancel

Resource Managers

Batch Job (Slurm) Variables

Job Exit Codes

Displaying Exit Codes and Signals

Submitting Termination Signal

Navigation menu

Search