Slurm Quick Start Tutorial

Resource sharing on a supercomputer dedicated to technical and/or scientific computing is often organized by a piece of software called a resource manager or job scheduler. Users submit jobs, which are scheduled and allocated resources (CPU time, memory, etc.)
by the resource manager.

Slurm is a resource manager and job scheduler designed to do just that, and much more. It was originally
created by people at theLivermore Computing Center, and has grown into a full-fledge open-source software backed up by a large community, commercially
supported by the original developers, and installed in many of the Top500 supercomputers.

Gathering information

Slurm offers many commands you can use to interact with the system. For instance, the 

sinfo

 command
gives an overview of the resources offered by the cluster, while the 

squeue

 command
shows to which jobs those resources are currently allocated.

By default, 

sinfo

 lists the partitions that are available. A partition is
a set of compute nodes (computers dedicated to ... computing,) grouped logically. Typical examples include partitions dedicated to batch processing, debugging, post processing, or visualization.

# sinfo
PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
batch     up     infinite     2 alloc  node[8-9]
batch     up     infinite     6 idle   node[10-15]
debug*    up        30:00     8 idle   node[0-7]

In the above example, we see two partitions, named batch and debug. The latter is the default partition as it is marked with an asterisk. All nodes of the debug partition are idle, while two of the batch partition are being used.

The command 

sinfo

 can output the information in a node-oriented fashion,
with the argument 

-N

# sinfo -N -l
NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
node[0-1]       2 debug*    idle      2   3448    38536     16 (null)   (null)
node[2,4-7]     5 debug*    idle      2   3384    38536     16 (null)   (null)
node3           1 debug*    idle      2   3394    38536     16 (null)   (null)
node[8-9]       2 batch     allocated 2    246    82306     16 (null)   (null)
node[10-15]     6 batch     idle      2    246    82306     16 (null)   (null)

Note that with the 

-l

 argument, more information about the nodes is
provided: number of CPUs, memory, temporary disk (also called scratch space), node weight (an internal parameter specifying preferences in nodes for allocations when there are multiple possibilities), features of the nodes (such as processor type for instance)
and the reason, if any, for which a node is down.

You can actually specify precisely what information you would like 

sinfo

 to
output by using its 

--format

 argument. For more details, have a look
at the 

sinfo

 manpage with 

man
sinfo

.

The 

squeue

 command shows the list of jobs which are currently running
(they are in the RUNNING state, noted as 'R') or waiting for resources (noted as 'PD').

# squeue
JOBID PARTITION NAME USER ST  TIME  NODES NODELIST(REASON)
12345     debug job1 dave  R   0:21     4 node[9-12]
12346     debug job2 dave PD   0:00     8 (Resources)
12348     debug job3 ed   PD   0:00     4 (Priority)

The above output show that is one job running, whose name is job1 and whose jobid is 12345. The jobid is a unique identifier that is used by many Slurm commands when actions must be taken about one particular job. For instance, to
cancel job job1, you would use

scancel 12345

. Time is the
time the job has been running until now. Node is the number of nodes which are allocated to the job, while the Nodelist column lists the nodes which have been allocated for running jobs. For pending jobs, that column gives the reason why the job is pending.
In the example, job 12346 is pending because resources (CPUs, or other) are not available in sufficient amounts, while job 12348 is waiting for job 12346, whose priority is higher, to run. Each job is indeed assigned a priority depending on
several parameters whose details are beyond the scope of this document. Note that the priority for pending jobs can be obtained with the 

sprio

 command.

There are many switches you can use to filter the output by user (

--user

),
by partition (

--partition

) by state (

--state

),
etc. As with the 

sinfo

 command, you can choose what you want 

sprio

 to
output with the 

--format

 parameter.

Creating a job

Now the question is: How do you create a job?

A job consists in two parts:resource requests and job steps. Resource requests consist in a number of CPUs, computing expected duration, amounts of RAM or disk space, etc. Job steps describe tasks that must
be done, software which must be run.

The typical way of creating a job is to write a submission script. A submission script is a shell script, e.g. a Bash script,
whose comments, if they are prefixed with SBATCH, are understood by Slurm as parameters describing resource requests and other submissions options. You can get the complete list of parameters from the 

sbatch

 manpage
(

man sbatch

)

The script itself is a job step. Other job steps are created with the 

srun

 command.

For instance, the following script, hypothetically named submit.sh,

#!/bin/bash
#
#SBATCH --job-name=test
#SBATCH --output=res.txt
#
#SBATCH --ntasks=1
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=100

srun hostname
srun sleep 60

would request one CPU for 10 minutes, along with 100 MB of RAM, in the default queue.. When started, the job would run a first job step 

srun
hostname

, which will launch the UNIX command 

hostname

 on the
node on which the requested CPU was allocated. Then, a second jobs step will start the 

sleep

 command.
Interestingly, you can get near-realtime information about your program (memory consumption, etc.) with the 

sstat

 command.
Note that the 

--job-name

 parameter allows giving a meaningful name
to the job and the 

--output

 parameter defines the file to which the
output of the job must be sent.

Once the submission script is written properly, you need to submit it to slurm through the 

sbatch

 command,
which, upon success, responds with the jobid attributed to the job. (The dollar sign below is the shell prompt)

$ sbatch submit.sh
sbatch: Submitted batch job 99999999

The job then enters the queue in the PENDING state. Once resources become available and the job has highest priority, anallocation is created for it and it goes to the RUNNING state. If the job completes correctly, it goes to the COMPLETED state,
otherwise, it is set to the FAILED state.

Upon compeltion, the output file contains the result of the commands run in the script file. In the above example, you can see it with

cat
res.txt

.

Note that you can create an interactive job with the 

salloc

 command
or by issuing a 

srun

 command directly.

Going parallel

But, still, the real question is: How do you create a parallel job?

There are several ways a parallel job, one whose tasks are run simultaneously, can be created:
by running a multi-process program (SPMD paradigm, e.g. with MPI)
by running a multithreaded program (shared memory paradigm, e.g. with OpenMP or pthreads)
by running several instances of a single-threaded program (so-called embarrassingly parallel paradigm)
by running one master program controling several slave programs (master/slave paradigm)

In the Slurm context, a task is to be understood as a process. So a multi-process program is made of several tasks. By contrast, a multithreaded program is composed of only one task, which uses several CPUs.

Tasks are requested/created with the 

--ntasks

 option, while CPUs,
for the multithreaded programs, are requested with the 

--cpus-per-task

 option.
Tasks cannot be split across several compute nodes, so requesting several CPUs with the 

--cpus-per-task

 option
will ensure all CPUs are allocated on the same compute node. By contrast, requesting the same amount of CPUs with the 

--ntasks

 option
may lead to several CPUs being allocated on several, distinct compute nodes.

More submission script examples

Here are some quick sample submission scripts. For more detailed information, make sure to have a look at the Slurm FAQ and to follow our training
sessions. There is also Script Generation Wizard you can use to help you in submission scripts creation.

Message passing example (MPI)

#!/bin/bash
#
#SBATCH --job-name=test_mpi
#SBATCH --output=res_mpi.txt
#
#SBATCH --ntasks=4
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=100

module load openmpi
mpirun hello.mpi

Request four cores on the cluster for 10 minutes, using 100 MB of RAM per core. Assuming my_mpi_program was compiled with MPI support, 

mpirun

 will
create four instances of it, on the nodes allocated by Slurm.

You can try the above example by downloading the example hello world program from Wikipedia (name it for
instance

wiki_mpi_example.c

), and compiling it with

module load openmpi
mpicc wiki_mpi_example.c -o hello.mpi

The 

res_mpi.txt

 file should contain something like

0: We have 4 processors
0: Hello 1! Processor 1 reporting for duty

0: Hello 2! Processor 2 reporting for duty

0: Hello 3! Processor 3 reporting for duty

Shared memory example (OpenMP)

#!/bin/bash
#
#SBATCH --job-name=test_omp
#SBATCH --output=res_omp.txt
#
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=100

export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
./hello.omp

The job it will be run in an allocation where four cores have been reserved on the same compute node.

You can try it by using the hello world program from Wikipedia (name it for instance wiki_omp_example.c) and compiling it with

gcc -fopenmp wiki_omp_example.c -o hello.omp

The 

res_omp.txt

 file should contain something like

Hello World from thread 0
Hello World from thread 3
Hello World from thread 1
Hello World from thread 2
There are 4 threads

Embarrassingly parallel workload example

#!/bin/bash
#
#SBATCH --job-name=test_emb
#SBATCH --output=res_emb.txt
#
#SBATCH --ntasks=4
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=100

srun printenv SLURM_PROCID

In that configuration, the 

printenv

 command will be run four times,
and each will have its environment variable 

SLURM_PROCID

set to a
distinct value.

This setup is useful if the program is based on random draws (e.g. Monte-Carlo simulations): the application permitting, you can have four programs drawing 1000 samples and combine their output (with another program) to get the equivalent of
drawing 4000 samples.

Another typical use of this setting is parameter sweep where the same computation is carried on by each program except that some high-level parameter has distinct values in each case. Examples include optimisation of an integer-valued parameter
through range scanning. In the latter case, each instance of the program simply has to lookup the 

$SLURM_PROCID

 environment
variable and decide, accordingly, what values of the parameter to test.

The same can be set up to process several data files for instance. Each instance of the program just has to decide which file to read based upon the value set in its 

$SLURM_PROCID

 environment
variable.

Upon completion, the above job will create a file 

test_emp

 with four
lines:

0
1
2
3

Master/slave program example

#!/bin/bash
#
#SBATCH --job-name=test_ms
#SBATCH --output=res_ms.txt
#
#SBATCH --ntasks=4
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=100

srun --multi-prog multi.conf

With file multi.conf being, for example, as follows

0      echo     'I am the Master'
1-3    bash -c 'printenv SLURM_PROCID'

The above instructs Slurm to create four tasks (or processes), one running 

echo
'I am the Master'

, and the other 3 running

bash -c 'printenv SLURM_PROCID'

.
This is typically used in a producer/consumer setup where one program (the master) create computing tasks for the other program (the slaves) to perform.

Upon completion of the above job, file 

res_ms.txt

 will contain

I am the Master
1
2

3

转载自：http://www.ceci-hpc.be/slurm_tutorial.html