Introduction

At ITET the Condor Batch Queueing System is used since long time for running compute-intensive jobs. It uses the free resources on the tardis-PCs of the student rooms and on numerous PCs and compute servers at ITET institutes. Interactive work is privileged over batch computing, so running jobs could be killed by new interactive load or by shutdown/restart of a PC.

The SLURM system installed on the powerfull ITET arton compute servers is an alternative to the Condor batch computing system and reserved for staff of the contributing institutes (IBT,IFA,TIK,IKT,APS). It consists of a master host, where the scheduler resides and the arton compute nodes, where the batch jobs are executed. The compute nodes are powerfull servers, which resides in server rooms and are exclusively reserved for batch processing. Interactive logins are disabled.

SLURM

SLURM (Simple Linux Utility for Resource Management) is a free and open-source job scheduler for Linux and Unix-like kernels, used by many of the world's supercomputers and computer clusters. Slurm's design is very modular with about 100 optional plugins. In 2010, the developers of Slurm founded SchedMD (https://www.schedmd.com), which maintains the canonical source, provides develgfreudig@trollo:~/Batch$ sinfo PARTITION AVAIL TIMELIMIT NODES STATE NODELIST cpu.normal.32* up 1-00:00:00 2 idle arton02,zampano cpu.normal.64 up 1-00:00:00 1 idle arton09 cpu.normal.256 up 1-00:00:00 1 idle arton09 array.normal up 1-00:00:00 2 idle arton02,zampano gpu.normal up 1-00:00:00 1 mix artongpu01 gfreudig@trollo:~/Batch$ opment, level 3 commercial support and training services and also provide a very good online documentation to Slurm ( https://slurm.schedmd.com ).

SLURM Arton Grid

Hardware

At the moment the computing power of the SLURM Arton Grid is based on the following 11 cpu compute servers and 1 gpu compute server (compute nodes) :

The local disks (/scratch) of arton09, arton10 and arton11 are fast SSD-disks (6 GBit/s) with a size of 720 GByte.

The SLURM job scheduler runs on the linux server itetmaster01.

Software

The artons cpu nodes offer the same software environment as all D-ITET managed Linux clients, gpu nodes have a restricted software ( no desktops installed ).

Using SLURM

At a basic level, SLURM is very easy to use. The following sections will describe the commands you need to run and manage your batch jobs on the Grid Engine. The commands that will be most useful to you are as follows

• sbatch - submit a job to the batch scheduler
• squeue - examine running and waiting jobs
• sinfo - status compute nodes
• scancel - delete a running job

Setting environment

The above commands are only working if the environment variables for SLURM are set. Please put the following to lines in your ~/.bashrc :

export PATH=/usr/pack/slurm-19.05.0-sr/amd64-debian-linux9/bin:$PATH
export SLURM_CONF=/home/sladmitet/slurm/slurm.conf

sbatch -> Submitting a job

sbatch doesn't allow to submit a binary program directly, please put the program to run in a surrounding bash script. The sbatch command has the following syntax:

> sbatch [options] job_script [job_script arguments]

The job_script is a standard UNIX shell script. The fixed options for the SLURM Scheduler are placed in the job_script in lines starting with #SBATCH. The UNIX shell interpreter read this lines as comment lines and ignores them. Only temporary options should be placed outside the job_script. To test your job-script you can simply run it interactively.

Assume there is a c program primes.c which is compiled to an executable binary named primes with "gcc -o primes primes.c". The program runs 5 seconds and calculates prime numbers. The found prime numbers and a final summary report are written to standard output. A sample job_script primes.sh to perform a batch run of the binary primes on the Arton grid looks like this:

#
#SBATCH  --mail-type=ALL                     # mail configuration: NONE, BEGIN, END, FAIL, REQUEUE, ALL
#SBATCH  --output=log/%j.out                 # where to store the output ( %j is the JOBID )
/bin/echo Running on host: `hostname`
/bin/echo In directory: `pwd`
/bin/echo Starting on: `date`
/bin/echo SLURM_JOB_ID: $SLURM_JOB_ID
#
# binary to execute
./primes
echo finished at: `date`
exit 0;

You cat test the script by running it interactively in a terminal:

gfreudig@trollo:~/Batch$ ./primes.sh

If the script runs successfully you now can submit it as a batch job to the SLURM arton grid:

gfreudig@trollo:~/Batch$ sbatch primes.sh 
sbatch: Start executing function slurm_job_submit......
sbatch: Job partition set to : cpu.normal.32 (normal memory)
Submitted batch job 931
gfreudig@trollo:~/Batch$ 

When the job has finished, you find the output file of the job in the log subdirectory with a name of <JOBID>.out .
The directory for the job output must exist, it is not created automatically !

Like in condor its also possible to start an array job. The job above would run 10 times if you put the option #SBATCH --array=0-9 in the job-script. The repeated execution makes only sense if something is changed in the executed program with the array task count number.The array count number can be referenced through the variable $SLURM_ARRAY_TASK_ID. You can pass the value of $SLURM_ARRAY_TASK_ID or some derived parameters to the executable. A simple solution to pass an $SLURM_ARRAY_TASK_ID dependent input filename parameter for the executable would look like this:

.
#SBATCH   --array=0-9
#
# binary to execute
<path-to-executable> data$SLURM_ARRAY_TASK_ID.dat

Every run of the program in the array job with a different task-id will also produce a separate output file.

The following table shows the most common available options for sbatch to be placed in the job-script in lines starting with #SBATCH

The --nodes option should only be used for MPI jobs !

squeue -> Show running/waiting jobs

The squeue command shows the actual list of running and pending jobs in the system. As you can see in the following sample output the default format is not satisfying:

gfreudig@trollo:~/Batch$ squeue
             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
               951 cpu.norma primes.s gfreudig  R       0:11      1 arton02
               950 cpu.norma primes_4 gfreudig  R       0:36      1 arton02
               949 cpu.norma primes.s fgtest01  R       1:22      1 arton02
               948 gpu.norma primes.s fgtest01  R       1:39      1 artongpu01
gfreudig@trollo:~/Batch$ 

A better output is achieved with the following command:

gfreudig@trollo:~/Batch$ squeue -O jobarrayid:10,state:10,partition:16,reasonlist:18,username:10,tres-alloc:45,timeused:8,command:50
JOBID     STATE     PARTITION       NODELIST(REASON)  USER      TRES_ALLOC                                   TIME    COMMAND                                             
951       RUNNING   cpu.normal.32   arton02           gfreudig  cpu=1,mem=32G,node=1,billing=1               1:20    /home/gfreudig/BTCH/Slurm/jobs/single/primes.sh 600 
950       RUNNING   cpu.normal.32   arton02           gfreudig  cpu=4,mem=8G,node=1,billing=4                1:45    /home/gfreudig/BTCH/Slurm/jobs/multi/primes_4.sh 600
949       RUNNING   cpu.normal.32   arton02           fgtest01  cpu=1,mem=8G,node=1,billing=1                2:31    /home/fgtest01/BTCH/Slurm/jobs/single/primes.sh 600 
948       RUNNING   gpu.normal      artongpu01        fgtest01  cpu=1,mem=8G,node=1,billing=1,gres/gpu=1     2:48    /home/fgtest01/BTCH/Slurm/jobs/single/primes.sh 600 
gfreudig@trollo:~/Batch$ 

If you define an alias in your .bashrc with

alias sq1='squeue -O jobarrayid:10,state:10,partition:16,reasonlist:18,username:10,tres-alloc:45,timeused:8,command:50'

you can always use the command "sq1" as a better "squeue" command.

scancel -> Deleting a job

With scancel you can remove your waiting and running jobs from the scheduler queue. squeue gives you an overview of your jobs with the associated JOBIDs . A job can be deleted with

> scancel <JOBID>

To operate on an array job you can use the following commands

> scancel <JOBID>          # all jobs (waiting or running) of the array job are deleted
> scancel <JOBID>_n        # the job with task-ID n is deleted
> scancel <JOBID>_[n1-n2]  # the jobs with task-ID in the range n1-n2 are deleted

sinfo -> Show partition configuration

The partition status can be obtained by using the sinfo command. An example listing is shown below.

gfreudig@trollo:~/Batch$ sinfo
PARTITION      AVAIL  TIMELIMIT  NODES  STATE NODELIST
cpu.normal.32*    up 1-00:00:00      2   idle arton[01-11]
cpu.normal.64     up 1-00:00:00      1   idle arton[09-11]
cpu.normal.256    up 1-00:00:00      1   idle arton11
array.normal      up 1-00:00:00      2   idle arton[01-08]
gpu.normal        up 1-00:00:00      1   idle artongpu01
gfreudig@trollo:~/Batch$ 

For normal jobs (single,multicore) you can not select the partition for the job to run in the sbatch command, the partition is selected by the scheduler according to your memory request. Array jobs are put in the array.normal partition, gpu jobs in the gpu.normal partition. Here a table of the job memory limits in the different partiitons:

Only a job with a --mem request of maximal 32 GByte can run in the cpu.normal.32 partition which contains all 11 artons.

Multicore jobs/ job to core binding

The modern linux kernels are able to bind a process and all its childs to a fixed number of cores. By default a job submitted to the SLURM arton grid is bound to to the numbers of requested cores/cpus. The default number of requested cpus is 1, if you have an application which is able to run multithreaded on several cores you must use the --cpus-per-task option in the sbatch command to get a binding to more than one core. To see if there are processes with core bindings on a machine use the "hwloc-ps -c" command:

gfreudig@trollo:~/Batch$ ssh arton02 hwloc-ps -c
43369   0x00010001              slurmstepd: [984.batch]
43374   0x00010001              /bin/sh
43385   0x00010001              codebin/primes
gfreudig@trollo:~/Batch$

Job input/output data storage

Temporary data storage of a job, which is only used while the job is running, should be placed in the /scratch directory of the compute nodes. The environment variables of the tools should be set accordingly. The Matlab MCR_ROOT_CACHE variable is set automatically by the SLURM scheduler.
The file system protection of the /scratch directory allows everybody to create files and directories in it. A cron job runs periodically on the execution hosts to prevent the /scratch directory from getting full and cleans it according to given policies. Therefore data you put in the /scratch directory of a compute node is not safe over time.

Small sized input and output data for the jobs is best placed in your home directory. It is available on every compute node through the /home automounter.

If you have problems with the quota limit in your home directory you could transfer data from your home or the /scratch directory of your submit host to the /scratch directories of the arton compute nodes and vice versa. To do this you are allowed to login interactively on arton01 with your personal account. All /scratch directories of the compute nodes are available on arton01 with the /scratch_net automount system. You can access the /scratch directory of arton<nn> under /scratch_net/arton<nn>. So you are able to transfer data between the /scratch_net directories and your home with normal linux file copy and to the scratch of your submission host with scp.

Please do not use the possible login on arton01 to run compute jobs interactively. Our procguard system will detect you. Other data storage concepts for the arton grid are possible and will be investigated, if the above solution proves not to be sufficient.
In the near future ISG will provide a network attached scratch storage system (Netscratch) which will be accessible from all managed linux clients and also from the compute nodes.

SLURM and Matlab