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==== Slurm ====

The Simple Linux Utility for Resource Management (SLURM) is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters. The architecture is described here [[https://computing.llnl.gov/linux/slurm/quickstart.html]].

  * Installation
    * begins with installing Munge
      * fairly straightforward, build RPMs from tarball
      * installed on head node and all compute nodes
      * copied the munge.key from head node to all compute nodes
    * slum installed from source code with
      * --prefix=/opt/slurm-14  --sysconfdir=/opt/slurm-14/etc
      * launched the configurator web page and set up a simple setup
        * created the openssl key and cert (see slurm web pages)
        * logs to files not mysql for now
        * change some settings in slurm.conf, particularly
          * FirstJObId(1), MaxJobId(999999)
          * MaxJobCount=120000
          * MaxTaskPerNode=65533
          * 
Then I created a simple file to test Slurm

<code>

#!/bin/bash

#SBATCH --time=1:30:10
#SBATCH --job-name="NUMBER"
#SBATCH --output="tmp/outNUMBER"
#SBATCH --begin=10:35:00

echo "$SLURMD_NODENAME JOB_PID=$SLURM_JOB_ID"
date

</code>

<code>

# I then submit it like so
for i in `seq 1 8`; do cat run | sed "s/NUMBER/$i/g" | sbatch; done

# and observe
sinfo
PARTITION AVAIL  TIMELIMIT  NODES  STATE NODELIST
test*        up   infinite      2   comp v[2-3]
test*        up   infinite      5  alloc v[1,4-5,7-8]
test*        up   infinite      1   idle v6

# and when I raised the total number of jobs to 25,000 the distribution is
^v1  ^v2  ^v3  ^v4  ^v5  ^v6  ^v7  ^v8 ^
|3138|3130|3149|3133|3108|3119|3110|3113

# time to process queues of different sizes (I stage them with the --begin parameter)
# jobs do have to open the output files though, just some crude testing of slurm
# scheduling capabilities

^NrJobs^1,000^10,000^25,000^
|mm:ss | 0:33|  6:32| 19:37|

# 20 mins for 25,000 jobs via sbatch

</code>

Slurm is installed on a PE2950 with dual quad cores and 16 GB of memory. It is part of my high priority queue and allocated to Openlava (v2.2).

My slurm compute nodes (v1-v8) are created in a virtual KVM environment on another PE2950 (2.6 Ghz, 16 GB ram) dual quad core with hyperthreading and virtualization turned on in the BIOS. Comments on how to build that KVM environment are here [[cluster:132|LXC Linux Containers]]. The virtual nodes are single core, 1 GB or ram.

From slurm.conf

<code>

# COMPUTE NODES
NodeName=v[1-8] NodeAddr=192.168.150.[1-8] CPUs=1 RealMemory=1 \
  Sockets=1 CoresPerSocket=1 ThreadsPerCore=1 State=UNKNOWN
NodeName=swallowtail NodeAddr=192.168.1.136 CPUs=8 RealMemory=16 \
  Sockets=2 CoresPerSocket=4 ThreadsPerCore=1 State=UNKNOWN
  
PartitionName=test Nodes=v[1-8] Default=YES MaxTime=INFINITE State=UP

</code>


==== IO error ====

At around 32K jobs I ran into IO problems.

<code>

sbatch: error: Batch job submission failed: I/O error writing script/environment to file

</code>

Oh, this is OS error from ext3 file system, max files and dirs exceeded.

Switching "StateSaveLocation=/sanscratch_sharptail/tmp/slurmctld" to XFS file system.


==== High Throughput ====

[[https://computing.llnl.gov/linux/slurm/high_throughput.html]]

Vanilla out of the box with these changes

  * MaxJobCount=120000
  * SlurmctldPort=6820-6825
  * SchedulerParameters=max_job_bf=100,interval=30  (NOTE:  unset these, pages states default values are fine)

^NrJobs^N^hh:mm^N^hh:mm^
|50,000|8|00:56|  |  |
|75,000|8|01:53|16|01:57|
|100,000|8|03:20|  |  |

The N=16 is 8 one core VMs and swallowtail itself (8 cores). So we're scheduling bound. But 50,000 jobs per hour is grand! Certainly would exceed what we throw at it. And that's on old hardware.


<code>
WARNING: We will use a much slower algorithm with proctrack/pgid, use 
Proctracktype=proctrack/linuxproc or some other proctrack when using jobacct_gather/linux
</code>

After fixing that. Hmmm.

^NrJobs^N^hh:mm^
| 1,000|8|00:02| 
|10,000|8|00:22| 
|15,000|8|00:31| 
|20,000|8|00:41| 


Debug Level is 3 above. Falling back to proctrack/pgid while setting debug to level 1. Also setting SchedulerType=sched/builtin (removing the backfill). This is throughput allright, just 8 KVM nodes handling the jobs.

^NrJobs^N^hh:mm:ss^
| 1,000|8|00:00:34| 
|10,000|8|00:05:57| 
|25,000|8|00:15:07| 
|50,000|8|00:29:55|
|75,000|8|00:44:15|
|100,000|8|00:58:16|

 Next I will add a proplog/epilog script to my submit job script which will create 
/localscratch/$SLURM_JOB_ID, echo the date into file foo, then cat foo to standard out and finish with removing the scratch dir. These prolog/epilog actions needs to be done by slurmd but so far it errors for me.  Does slow things down a bit. Same conditions as above.

<code>
#!/bin/bash
/share/apps/lsf/slurm_prolog.pl

#SBATCH --job-name="NUMBER"
#SBATCH --output="tmp/outNUMBER"
#SBATCH --begin=10:00:00

# unique job scratch dir
export MYLOCALSCRATCH=/localscratch/$SLURM_JOB_ID
cd $MYLOCALSCRATCH
pwd

echo "$SLURMD_NODENAME JOB_PID=$SLURM_JOB_ID" >> foo
date  >> foo
cat foo 

/share/apps/lsf/slurm_epilog.pl
</code>


^NrJobs^N^hh:mm:ss^
| 1,000|8|00:05:00| 
| 5,000|8|00:23:43| 
|10,000|8|00:47:12| 
|25,000|8|00:58:01|


==== MPI ====

With ''sbatch'' there is no need for a wrapper script, slurm figures it all out.

<code>

#!/bin/bash
#/share/apps/lsf/slurm_prolog.pl

#SBATCH --job-name="MPI"
#SBATCH --ntasks=8
#SBATCH --begin=now

# unique job scratch dir
#export MYLOCALSCRATCH=/localscratch/$SLURM_JOB_ID
#cd $MYLOCALSCRATCH

echo "$SLURMD_NODENAME JOB_PID=$SLURM_JOB_ID"

rm -rf err out logfile mdout restrt mdinfo

export PATH=/share/apps/openmpi/1.2+intel-9/bin:$PATH
export LD_LIBRARY_PATH=/share/apps/openmpi/1.2+intel-9/lib:$LD_LIBRARY_PATH
which mpirun

mpirun /share/apps/amber/9+openmpi-1.2+intel-9/exe/pmemd -O \
-i inp/mini.in -p 1g6r.cd.parm -c 1g6r.cd.randions.crd.1 \
-ref 1g6r.cd.randions.crd.1

#/share/apps/lsf/slurm_epilog.pl

</code>

When submitted we see

<code>

             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
            902246      test      MPI    hmeij  R       0:05      8 v[1-8]

</code>

Dumping the environment we observe some key parameters

<code>

SLURM_NODELIST=v[1-8]
SLURM_JOB_NAME=MPI
SLURMD_NODENAME=v1
SLURM_NNODES=8
SLURM_NTASKS=8
SLURM_TASKS_PER_NODE=1(x8)
SLURM_NPROCS=8
SLURM_CPUS_ON_NODE=1
SLURM_JOB_NODELIST=v[1-8]
SLURM_JOB_CPUS_PER_NODE=1(x8)
SLURM_JOB_NUM_NODES=8

</code>

And in the slurmjob.log file

<code>

JobId=902245 UserId=hmeij(8216) GroupId=its(623) \
Name=MPI JobState=COMPLETED Partition=test TimeLimit=UNLIMITED \
StartTime=2014-08-21T15:55:06 EndTime=2014-08-21T15:57:04 \
NodeList=v[1-8] NodeCnt=8 ProcCnt=8 WorkDir=/home/hmeij/1g6r/cd

</code>


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