Resource Requests
Aside from the time, RAM, and CPU requirements listed on the SlurmBasics page, we have a couple other requestable resources:
Valid gres options are: gpu[[:type]:count] fabric[[:type]:count]
Generally, if you don't know if you need a particular resource, you should use the default. These can be generated with the command
srun --gres=help
Fabric
We currently offer 3 "fabrics" as request-able resources in Slurm. The "count" specified is the line-rate (in Gigabits-per-second) of the connection on the node.
Infiniband
First of all, let me state that just because it sounds "cool" doesn't mean you need it or even want it. InfiniBand does absolutely no good if running on a single machine. InfiniBand is a high-speed host-to-host communication fabric. It is (most-often) used in conjunction with MPI jobs (discussed below). Several times we have had jobs which could run just fine, except that the submitter requested InfiniBand, and all the nodes with InfiniBand were currently busy. In fact, some of our fastest nodes do not have InfiniBand, so by requesting it when you don't need it, you are actually slowing down your job. To request Infiniband, add --gres=fabric:ib:1 to your sbatch command-line.
ROCE
ROCE, like InfiniBand is a high-speed host-to-host communication layer. Again, used most often with MPI. Most of our nodes are ROCE enabled, but this will let you guarantee the nodes allocated to your job will be able to communicate with ROCE. To request ROCE, add --gres=fabric:roce:1 to your sbatch command-line.
Ethernet
Ethernet is another communication fabric. All of our nodes are connected by ethernet, this is simply here to allow you to specify the interconnect speed. Speeds are selected in units of Gbps, with all nodes supporting 1Gbps or above. The currently available speeds for ethernet are: 1, 10, 40, and 100. To select nodes with 40Gbps and above, you could specify --gres=fabric:eth:40 on your sbatch command-line. Since ethernet is used to connect to the file server, this can be used to select nodes that have fast access for applications doing heavy IO. The Dwarves and Heroes have 40 Gbps ethernet and we measure single stream performance as high as 20 Gbps, but if your application requires heavy IO then you'd want to avoid the Moles which are connected to the file server with only 1 Gbps ethernet.
CUDA
CUDA is the resource required for GPU computing. 'kstat -g' will show you the GPU nodes and the jobs running on them. To request a GPU node, add --gres=gpu:1 for example to request 1 GPU for your job. You can also request a given type of GPU (kstat -g -l to show types) by using --gres=gpu:nvidia_geforce_gtx_1080_ti:1 for a 1080Ti GPU on the Wizards or Dwarves, --gres=gpu:nvidia_quadro_gp100:1 for the P100 GPUs on Wizard20-21 that are best for 64-bit codes like Vasp, or --gres=gpu:nvidia_geforce_gtx_980_ti:1 for the older 980Ti GPUs on Dwarf38-39. Most of these GPU nodes are owned by various groups. If you want access to GPU nodes and your group does not own any, we can add you to the --partition=ksu-gen-gpu.q group that has priority on Dwarf38-39. For more information on compiling CUDA code click on this CUDA link.
Parallel Jobs
There are two ways jobs can run in parallel, intranode and internode. Note: Beocat will not automatically make a job run in parallel. Have I said that enough? It's a common misperception.
Intranode jobs
Intranode jobs run on many cores in the same node. These jobs can take advantage of many common libraries, such as OpenMP, or any programming language that has the concept of threads. Often, your program will need to know how many cores you want it to use, and many will use all available cores if not told explicitly otherwise. This can be a problem when you are sharing resources, as Beocat does. To request multiple cores, use the sbatch directives '--nodes=1 --cpus-per-task=n' or '--nodes=1 --ntasks-per-node=n', where n is the number of cores you wish to use. If your command can take an environment variable, you can use $SLURM_CPUS_ON_NODE to tell how many cores you've been allocated.
Internode (MPI) jobs
Internode jobs can utilize many cores on one or more nodes. Communicating between nodes is trickier than talking between cores on the same node. The specification for doing so is called "Message Passing Interface", or MPI. We have OpenMPI installed on Beocat for this purpose. Most programs written to take advantage of large multi-node systems will use MPI, but MPI also allows an application to run on multiple cores within a node. You can tell if you have an MPI-enabled program because its directions will tell you to run 'mpirun program'. Requesting MPI resources is only mildly more difficult than requesting single-node jobs. Instead of using '--cpus-per-task=n', you would use '--nodes=n --tasks-per-node=m' or '--nodes=n --ntasks=o' for your sbatch request, where n is the number of nodes you want, m is the number of cores per node you need, and o is the total number of cores you need.
Some quick examples:
--nodes=6 --ntasks-per-node=4 will give you 4 cores on each of 6 nodes for a total of 24 cores.
--ntasks=40 will give you 40 cores spread across any number of nodes.
--nodes=10 --ntasks=100 will give you a total of 100 cores across 10 nodes.
Requesting memory for multi-core jobs
Memory requests are easiest when they are specified per core. For instance, if you specified the following: '--tasks=20 --mem-per-core=20G', your job would have access to 400GB of memory total.
Other Handy Slurm Features
Email status changes
One of the most commonly used options when submitting jobs not related to resource requests is to have have Slurm email you when a job changes its status. This takes may need two directives to sbatch: --mail-user and --mail-type.
--mail-type
--mail-type is used to tell Slurm to notify you about certain conditions. Options are comma separated and include the following
| Option | Explanation |
|---|---|
| NONE | This disables event-based mail |
| BEGIN | Sends a notification when the job begins |
| END | Sends a notification when the job ends |
| FAIL | Sends a notification when the job fails. |
| REQUEUE | Sends a notification if the job is put back into the queue from a running state |
| STAGE_OUT | Burst buffer stage out and teardown completed |
| ALL | Equivalent to BEGIN,END,FAIL,REQUEUE,STAGE_OUT |
| TIME_LIMIT | Notifies if the job ran out of time |
| TIME_LIMIT_90 | Notifies when the job has used 90% of its allocated time |
| TIME_LIMIT_80 | Notifies when the job has used 80% of its allocated time |
| TIME_LIMIT_50 | Notifies when the job has used 50% of its allocated time |
| ARRAY_TASKS | Modifies the BEGIN, END, and FAIL options to apply to each array task (instead of notifying for the entire job |
--mail-user
--mail-user is optional. It is only needed if you intend to send these job status updates to a different e-mail address than what you provided in the Account Request Page. It is specified with the following arguments to sbatch: --mail-user=someone@somecompany.com
Job Naming
If you have several jobs in the queue, running the same script with different parameters, it's handy to have a different name for each job as it shows up in the queue. This is accomplished with the '-J JobName' sbatch directive.
Separating Output Streams
Normally, Slurm will create one output file, containing both STDERR and STDOUT. If you want both of these to be separated into two files, you can use the sbatch directives '--output' and '--error'.
| option | default | example |
|---|---|---|
| --output | slurm-%j.out | slurm-206.out |
| --error | slurm-%j.out | slurm-206.out |
%j above indicates that it should be replaced with the job id.
Running from the Current Directory
By default, jobs run from your home directory. Many programs incorrectly assume that you are running the script from the current directory. You can use the '-cwd' directive to change to the "current working directory" you used when submitting the job.
Running in a specific class of machine
If you want to run on a specific class of machines, e.g., the Dwarves, you can add the flag "--constraint=dwarves" to select any of those machines.
Processor Constraints
Because Beocat is a heterogenous cluster (we have machines from many years in the cluster), not all of our processors support every new and fancy feature. You might have some applications that require some newer processor features, so we provide a mechanism to request those.
--contraint tells the cluster to apply constraints to the types of nodes that the job can run on. For instance, we know of several applications that must be run on chips that have "AVX" processor extensions. To do that, you would specify --constraint=avx on you sbatch or srun command lines. Using --constraint=avx will prohibit your job from running on the Mages while --contraint=avx2 will eliminate the Elves as well as the Mages.
Slurm Environment Variables
Within an actual job, sometimes you need to know specific things about the running environment to setup your scripts correctly. Here is a listing of environment variables that Slurm makes available to you. Of course the value of these variables will be different based on many different factors.
CUDA_VISIBLE_DEVICES=NoDevFiles
ENVIRONMENT=BATCH
GPU_DEVICE_ORDINAL=NoDevFiles
HOSTNAME=dwarf37
SLURM_CHECKPOINT_IMAGE_DIR=/var/slurm/checkpoint
SLURM_CLUSTER_NAME=beocat
SLURM_CPUS_ON_NODE=1
SLURM_DISTRIBUTION=cyclic
SLURMD_NODENAME=dwarf37
SLURM_GTIDS=0
SLURM_JOB_CPUS_PER_NODE=1
SLURM_JOB_GID=163587
SLURM_JOB_ID=202
SLURM_JOBID=202
SLURM_JOB_NAME=slurm_simple.sh
SLURM_JOB_NODELIST=dwarf37
SLURM_JOB_NUM_NODES=1
SLURM_JOB_PARTITION=batch.q,killable.q
SLURM_JOB_QOS=normal
SLURM_JOB_UID=163587
SLURM_JOB_USER=mozes
SLURM_LAUNCH_NODE_IPADDR=10.5.16.37
SLURM_LOCALID=0
SLURM_MEM_PER_NODE=1024
SLURM_NNODES=1
SLURM_NODEID=0
SLURM_NODELIST=dwarf37
SLURM_NPROCS=1
SLURM_NTASKS=1
SLURM_PRIO_PROCESS=0
SLURM_PROCID=0
SLURM_SRUN_COMM_HOST=10.5.16.37
SLURM_SRUN_COMM_PORT=37975
SLURM_STEP_ID=0
SLURM_STEPID=0
SLURM_STEP_LAUNCHER_PORT=37975
SLURM_STEP_NODELIST=dwarf37
SLURM_STEP_NUM_NODES=1
SLURM_STEP_NUM_TASKS=1
SLURM_STEP_TASKS_PER_NODE=1
SLURM_SUBMIT_DIR=/homes/mozes
SLURM_SUBMIT_HOST=dwarf37
SLURM_TASK_PID=23408
SLURM_TASKS_PER_NODE=1
SLURM_TOPOLOGY_ADDR=due1121-prod-core-40g-a1,due1121-prod-core-40g-c1.due1121-prod-sw-100g-a9.dwarf37
SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
SLURM_UMASK=0022
SRUN_DEBUG=3
TERM=screen-256color
TMPDIR=/tmp
USER=mozes
Sometimes it is nice to know what hosts you have access to during a job. You would checkout the SLURM_JOB_NODELIST to know that. There are lots of useful Environment Variables there, I will leave it to you to identify the ones you want.
Some of the most commonly-used variables we see used are $SLURM_CPUS_ON_NODE, $HOSTNAME, and $SLURM_JOB_ID.
Running from a sbatch Submit Script
No doubt after you've run a few jobs you get tired of typing something like 'sbatch -l mem=2G,h_rt=10:00 -pe single 8 -n MyJobTitle MyScript.sh'. How are you supposed to remember all of these every time? The answer is to create a 'submit script', which outlines all of these for you. Below is a sample submit script, which you can modify and use for your own purposes.
#!/bin/bash
## A Sample sbatch script created by Kyle Hutson
##
## Note: Usually a '#" at the beginning of the line is ignored. However, in
## the case of sbatch, lines beginning with #SBATCH are commands for sbatch
## itself, so I have taken the convention here of starting *every* line with a
## '#', just Delete the first one if you want to use that line, and then modify
## it to your own purposes. The only exception here is the first line, which
## *must* be #!/bin/bash (or another valid shell).
## There is one strict rule for guaranteeing Slurm reads all of your options:
## Do not put *any* lines above your resource requests that aren't either:
## 1) blank. (no other characters)
## 2) comments (lines must begin with '#')
## Specify the amount of RAM needed _per_core_. Default is 1G
##SBATCH --mem-per-cpu=1G
## Specify the maximum runtime in DD-HH:MM:SS form. Default is 1 hour (1:00:00)
##SBATCH --time=1:00:00
## Require the use of infiniband. If you don't know what this is, you probably
## don't need it.
##SBATCH --gres=fabric:ib:1
## GPU directive. If You don't know what this is, you probably don't need it
##SBATCH --gres:gpu:1
## number of cores/nodes:
## quick note here. Jobs requesting 16 or fewer cores tend to get scheduled
## fairly quickly. If you need a job that requires more than that, you might
## benefit from emailing us at beocat@cs.ksu.edu to see how we can assist in
## getting your job scheduled in a reasonable amount of time. Default is
##SBATCH --cpus-per-task=1
##SBATCH --cpus-per-task=12
##SBATCH --nodes=2 --tasks-per-node=1
##SBATCH --tasks=20
## Constraints for this job. Maybe you need to run on the elves
##SBATCH --constraint=elves
## or perhaps you just need avx processor extensions
##SBATCH --constraint=avx
## Output file name. Default is slurm-%j.out where %j is the job id.
##SBATCH --output=MyJobTitle.o%j
## Split the errors into a seperate file. Default is the same as output
##SBATCH --error=MyJobTitle.e%j
## Name my job, to make it easier to find in the queue
##SBATCH -J MyJobTitle
## Send email when certain criteria are met.
## Valid type values are NONE, BEGIN, END, FAIL, REQUEUE, ALL (equivalent to
## BEGIN, END, FAIL, REQUEUE, and STAGE_OUT), STAGE_OUT (burst buffer stage
## out and teardown completed), TIME_LIMIT, TIME_LIMIT_90 (reached 90 percent
## of time limit), TIME_LIMIT_80 (reached 80 percent of time limit),
## TIME_LIMIT_50 (reached 50 percent of time limit) and ARRAY_TASKS (send
## emails for each array task). Multiple type values may be specified in a
## comma separated list. Unless the ARRAY_TASKS option is specified, mail
## notifications on job BEGIN, END and FAIL apply to a job array as a whole
## rather than generating individual email messages for each task in the job
## array.
##SBATCH --mail-type=ALL
## Email address to send the email to based on the above line.
## Default is to send the mail to the e-mail address entered on the account
## request form.
##SBATCH --mail-user myemail@ksu.edu
## And finally, we run the job we came here to do.
## $HOME/ProgramDir/ProgramName ProgramArguments
## OR, for the case of MPI-capable jobs
## mpirun $HOME/path/MpiJobName
File Access
Beocat has a variety of options for storing and accessing your files. Every user has a home directory for general use which is limited in size, has decent file access performance, and will soon be backed up nightly. Those needing more storage may purchase /bulk subdirectories which have the same decent performance but are not backed up. The /scratch file system will soon be implemented on a Lustre file system that will provide very fast temporary file access. When fast IO is critical to the application performance, access to the local disk on each node or to a RAM disk are the best options.
Home directory
Every user has a /homes/username directory that they drop into when they log into Beocat. The home directory is for general use and provides decent performance for most file IO. Disk space in each home directory is limited to 1 TB, so larger files should be kept in a purchased /bulk directory, and there is a limit of 100,000 files in each subdirectory in your account. This file system is fully redundant, so 3 specific hard disks would need to fail before any data was lost. All files will soon be backed up nightly to a separate file server in Nichols Hall, so if you do accidentally delete something it can be recovered.
Bulk directory
Bulk data storage may be provided at a cost of $45/TB/year billed monthly. Due to the cost, directories will be provided when we are contacted and provided with payment information.
Scratch file system
The /scratch file system may be faster than /bulk or /homes since each file written will access fewer disks. In order to use scratch, you first need to make a directory for yourself. Scratch is meant as temporary space for prepositioning files and accessing them during runs. Once runs are completed, any files that need to be kept should be moved to your home or bulk directories since files on the scratch file system may get purged after 30 days.
mkdir /scratch/$USER
Local disk
If you are running on a single node, it may also be faster to access your files from the local disk on that node. Each job creates a subdirectory /tmp/job# where '#' is the job ID number on the local disk of each node the job uses. This can be accessed simply by writing to /tmp rather than needing to use /tmp/job#.
You may need to copy files to local disk at the start of your script, or set the output directory for your application to point to a file on the local disk, then you'll need to copy any files you want off the local disk before the job finishes since Slurm will remove all files in your job's directory on /tmp on completion of the job or when it aborts. When we get the scratch file system working with Lustre, it may end up being faster than accessing local disk so we will post the access rates for each. Use 'kstat -l -h' to see how much /tmp space is available on each node.
# Copy input files to the tmp directory if needed
cp $input_files /tmp
# Make an 'out' directory to pass to the app if needed
mkdir /tmp/out
# Example of running an app and passing the tmp directory in/out
app -input_directory /tmp -output_directory /tmp/out
# Copy the 'out' directory back to the current working directory after the run
cp -rp /tmp/out .
RAM disk
If you need ultrafast access to files, you can use a RAM disk which is a file system set up in the memory of the compute node you are running on. The RAM disk is limited to the requested memory on that node, so you should account for this usage when you request memory for your job. Below is an example of how to use the RAM disk.
# Copy input files over if necessary
cp $any_input_files /dev/shm/
# Run the application, possibly giving it the path to the RAM disk to use for output files
app -output_directory /dev/shm/
# Copy files from the RAM disk to the current working directory and clean it up
cp /dev/shm/* .
When you leave KSU
If you are done with your account and leaving KSU, please clean up your directory, move any files to your supervisor's account that need to be kept after you leave, and notify us so that we can disable your account. The easiest way to move your files to your supervisor's account is for them to set up a subdirectory for you with the appropriate write permissions. The example below shows moving just a user's 'data' subdirectory to their supervisor. The 'nohup' command is used so that the move will continue even if the window you are doing the move from gets disconnected.
# Supervisor:
mkdir /bulk/$USER/$STUDENT_USERNAME
chmod ugo+w /bulk/$USER/$STUDENT_USERNAME
# Student:
nohup mv /homes/$USER/data /bulk/$SUPERVISOR_USERNAME/$USER &
File Sharing
This section will cover methods of sharing files with other users within Beocat and on remote systems.
Securing your home directory
By default your home directory is accessible to other users on Beocat for reading but not writing. If you do not want others to have any access to files in your home directory, you can set the permissions to restrict access to just yourself.
chmod go-rwx /homes/your_user_name
This removes read, write, and execute permission to everyone but yourself. Be aware that it may make it more difficult for us to help you out when you run into problems.
Sharing files within your group
By default all your files and directories have a 'group' that is your user name followed by _users as 'ls -l' shows. In my case they have the group of daveturner_users. If your working group owns any nodes on Beocat, then you have a group name that can be used to securely share files with others within your group. Below is an example of creating a directory called 'share', changing the group to ksu-cis-hpc (my group is ksu-cis-hpc so I submit jobs to --partition=ksu-cis-hpc.q), then changing the permissions to restrict access to just that group.
mkdir share chgrp ksu-cis-hpc share chmod g+rx share chmod o-rwx share
This will give people in your group the ability to read files in the 'share' directory. If you also want them to be able to write or modify files in that directory then use 'chmod g+rwx' instead.
If you want to know what groups you belong to use the line below.
groups
If your group does not own any nodes, you can still request a group name and manage the participants yourself.
Openly sharing files on the web
If you create a 'public_html' directory on your home directory, then any files put there will be shared openly on the web. There is no way to restrict who has access to those files.
cd mkdir public_html
Then access the data from a web browser using the URL:
http://people.beocat.ksu.edu/~your_user_name
This will show a list of the files you have in your public_html subdirectory.
Globus
We have a page here dedicated to Globus
Array Jobs
One of Slurm's useful options is the ability to run "Array Jobs"
It can be used with the following option to sbatch.
--array=n[-m[:s]]
Submits a so called Array Job, i.e. an array of identical tasks being differentiated only by an index number and being treated by Slurm
almost like a series of jobs. The option argument to --arrat specifies the number of array job tasks and the index number which will be
associated with the tasks. The index numbers will be exported to the job tasks via the environment variable SLURM_ARRAY_TASK_ID. The option
arguments n, and m will be available through the environment variables SLURM_ARRAY_TASK_MIN and SLURM_ARRAY_TASK_MAX.
The task id range specified in the option argument may be a single number, a simple range of the form n-m or a range with a step size.
Hence, the task id range specified by 2-10:2 would result in the task id indexes 2, 4, 6, 8, and 10, for a total of 5 identical tasks, each
with the environment variable SLURM_ARRAY_TASK_ID containing one of the 5 index numbers.
Array jobs are commonly used to execute the same type of operation on varying input data sets correlated with the task index number. The
number of tasks in a array job is unlimited.
STDOUT and STDERR of array job tasks follow a slightly different naming convention (which can be controlled in the same way as mentioned above).
slurm-%A_%a.out
%A is the SLURM_ARRAY_JOB_ID, and %a is the SLURM_ARRAY_TASK_ID
Examples
Change the Size of the Run
Array Jobs have a variety of uses, one of the easiest to comprehend is the following:
I have an application, app1 I need to run the exact same way, on the same data set, with only the size of the run changing.
My original script looks like this:
#!/bin/bash
RUNSIZE=50
#RUNSIZE=100
#RUNSIZE=150
#RUNSIZE=200
app1 $RUNSIZE dataset.txt
For every run of that job I have to change the RUNSIZE variable, and submit each script. This gets tedious.
With Array Jobs the script can be written like so:
#!/bin/bash
#SBATCH --array=50-200:50
RUNSIZE=$SLURM_ARRAY_TASK_ID
app1 $RUNSIZE dataset.txt
I then submit that job, and Slurm understands that it needs to run it 4 times, once for each task. It also knows that it can and should run these tasks in parallel.
Choosing a Dataset
A slightly more complex use of Array Jobs is the following:
I have an application, app2, that needs to be run against every line of my dataset. Every line changes how app2 runs slightly, but I need to compare the runs against each other.
Originally I had to take each line of my dataset and generate a new submit script and submit the job. This was done with yet another script:
#!/bin/bash
DATASET=dataset.txt
scriptnum=0
while read LINE
do
echo "app2 $LINE" > ${scriptnum}.sh
sbatch ${scriptnum}.sh
scriptnum=$(( $scriptnum + 1 ))
done < $DATASET
Not only is this needlessly complex, it is also slow, as sbatch has to verify each job as it is submitted. This can be done easily with array jobs, as long as you know the number of lines in the dataset. This number can be obtained like so: wc -l dataset.txt in this case lets call it 5000.
#!/bin/bash
#SBATCH --array=1:5000
app2 `sed -n "${SLURM_ARRAY_TASK_ID}p" dataset.txt`
This uses a subshell via `, and has the sed command print out only the line number $SLURM_ARRAY_TASK_ID out of the file dataset.txt.
Not only is this a smaller script, it is also faster to submit because it is one job instead of 5000, so sbatch doesn't have to verify as many.
To give you an idea about time saved: submitting 1 job takes 1-2 seconds. by extension if you are submitting 5000, that is 5,000-10,000 seconds, or 1.5-3 hours.
Checkpoint/Restart using DMTCP
DMTCP is Distributed Multi-Threaded CheckPoint software that will checkpoint your application without modification, and can be set up to automatically restart your job from the last checkpoint if for example the node you are running on fails. This has been tested successfully on Beocat for some scalar and OpenMP codes, but has failed on all MPI tests so far. We would like to encourage users to try DMTCP out if their non-MPI jobs run longer than 24 hours. If you want to try this, please contact us first since we are still experimenting with DMTCP.
The sample job submission script below shows how dmtcp_launch is used to start the application, then dmtcp_restart is used to start from a checkpoint if the job has failed and been rescheduled. If you are putting this in an array script, then add the Slurm array task ID to the end of the ckeckpoint directory name like ckptdir=ckpt-$SLURM_ARRAY_TASK_ID.
#!/bin/bash -l
#SBATCH --job-name=gromacs
#SBATCH --mem=50G
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
module purge
module load GROMACS/2016.4-foss-2017beocatb-hybrid
module load DMTCP
module list
ckptdir=ckpt
mkdir -p $ckptdir
export DMTCP_CHECKPOINT_DIR=$ckptdir
if ! ls -1 $ckptdir | grep -c dmtcp_restart_script > /dev/null
then
echo "Using dmtcp_launch to start the app the first time"
dmtcp_launch --no-coordinator mpirun -np 1 -x OMP_NUM_THREADS=4 gmx_mpi mdrun -nsteps 50000 -ntomp 4 -v -deffnm 1ns -c 1ns.pdb -nice 0
else
echo "Using dmtcp_restart from $ckptdir to continue from a checkpoint"
dmtcp_restart $ckptdir/*.dmtcp
fi
You will need to run several tests to verify that DMTCP is working properly with your application. First, run a short test without DMTCP and another with DMTCP with the checkpoint interval set to 5 minutes by adding the line export DMTCP_CHECKPOINT_INTERVAL=300 to your script. Then use kstat -d 1 to check that the memory in both runs is close to the same. Also use this information to calculate the time that each checkpoint takes. In most cases I've seen times less than a minute for checkpointing that will normally be done once each hour. If your application is taking more time, let us know. Sometimes this can be sped up by simply turning off compression by adding the line export DMTCP_GZIP=0. Make sure to remove the line where you set the checkpoint interval to 300 seconds so that the default time of once per hour will be used.
After verifying that your code completes using DMTCP and does not take significantly more time or memory, you will need to start a run then scancel it after the first checkpoint, then resubmit the same script to make sure that it restarts and runs to completion. If you are working with an array job script, the last is to try a few array tasks at once to make sure there is no conflict between the jobs.
Running jobs interactively
Some jobs just don't behave like we think they should, or need to be run with somebody sitting at the keyboard and typing in response to the output the computers are generating. Beocat has a facility for this, called 'srun'. srun uses the exact same command-line arguments as sbatch, but you need to add the following arguments at the end: --pty bash. If no node is available with your resource requirements, srun will tell you something like the following:
srun --pty bash srun: Force Terminated job 217 srun: error: CPU count per node can not be satisfied srun: error: Unable to allocate resources: Requested node configuration is not available
Note that, like sbatch, your interactive job will timeout after your allotted time has passed.
Connecting to an existing job
You can connect to an existing job using srun in the same way that the MonitorNode command allowed us to in the old cluster. This is essentially like using ssh to get into the node where your job is running which can be very useful in allowing you to look at files in /tmp/job# or in running htop to view the activity level for your job.
srun --jobid=# --pty bash where '#' is the job ID number
Altering Job Requests
We generally do not support users to modify job parameters once the job has been submitted. It can be done, but there are numerous catches, and all of the variations can be a bit problematic; it is normally easier to simply delete the job (using scancel jobid) and resubmit it with the right parameters. If your job doesn't start after modifying such parameters (after a reasonable amount of time), delete the job and resubmit it.
As it is unsupported, this is an excercise left to the reader. A starting point is man scontrol
Killable jobs
There are a growing number of machines within Beocat that are owned by a particular person or group. Normally jobs from users that aren't in the group designated by the owner of these machines cannot use them. This is because we have guaranteed that the nodes will be accessible and available to the owner at any given time. We will allow others to use these nodes if they designate their job as "killable." If your job is designated as killable, your job will be able to use these nodes, but can (and will) be killed off at any point in time to make way for the designated owner's jobs. Jobs that are marked killable will be re-queued and may restart on another node.
The way you would designate your job as killable is to add --gres=killable:1 to the sbatch or srun arguments. This could be either on the command-line or in your script file.
Note: This is a submit-time only request, it cannot be added by a normal user after the job has been submitted. If you would like jobs modified to be killable after the jobs have been submitted (and it is too much work to scancel the jobs and re-submit), send an e-mail to the administrators detailing the job ids and what you would like done.
Scheduling Priority
Some users are members of projects that have contributed to Beocat. When those users have contributed nodes, the group gets access to a "partition" giving you priority on those nodes.
In most situations, the scheduler will automatically add those priority partitions to the jobs as submitted. You should not need to include a partition list in your job submission.
There are currently just a few exceptions that we will not automatically add:
- ksu-chem-mri.q
- ksu-gen-gpu.q
- ksu-gen-highmem.q
If you have access to those any of the non-automatic partitions, and have need of the resources in that partition, you can then alter your #SBATCH lines to include your new partition:
#SBATCH --partition=ksu-gen-highmem.q
Otherwise, you shouldn't modify the partition line at all unless you really know what you're doing.
Graphical Applications
Some applications are graphical and need to have some graphical input/output. We currently accomplish this with X11 forwarding
Connecting with an X11 client
Windows
If you are running Windows, we recommend MobaXTerm as your file/ssh manager, this is because it is one relatively simple tool to do everything. MobaXTerm also automatically connects with X11 forwarding enabled.
Linux/OSX
Both Linux and OSX can connect in an X11 forwarding mode. Linux will have all of the tools you need installed already, OSX will need XQuartz installed.
Then you will need to change your 'ssh' command slightly:
ssh -Y eid@headnode.beocat.ksu.edu
The -Y argument tells ssh to setup X11 forwarding.
Starting an Graphical job
All graphical jobs, by design, must be interactive, so we'll use the srun command. On a headnode, we run the following:
# load an X11 enabled application module load Octave # start an X11 job, sbatch arguments are accepted for srun as well, 1 node, 1 hour, 1 gb of memory srun --nodes=1 --time=1:00:00 --mem=1G --pty --x11 octave --gui
Because these jobs are interactive, they may not be able to run at all times, depending on how busy the scheduler is at any point in time. --pty --x11 are required arguments setting up the job, and octave --gui is the command to run inside the job.
Job Accounting
Some people may find it useful to know what their job did during its run. The sacct tool will read Slurm's accounting database and give you summarized or detailed views on jobs that have run within Beocat.
sacct
This data can usually be used to diagnose two very common job failures.
Job debugging
It is simplest if you know the job number of the job you are trying to get information on.
# if you know the jobid, put it here:
sacct -j 1122334455 -l
# if you don't know the job id, you can look at your jobs started since some day:
sacct -S 2017-01-01
My job didn't do anything when it ran!
| JobID | JobIDRaw | JobName | Partition | MaxVMSize | MaxVMSizeNode | MaxVMSizeTask | AveVMSize | MaxRSS | MaxRSSNode | MaxRSSTask | AveRSS | MaxPages | MaxPagesNode | MaxPagesTask | AvePages | MinCPU | MinCPUNode | MinCPUTask | AveCPU | NTasks | AllocCPUS | Elapsed | State | ExitCode | AveCPUFreq | ReqCPUFreqMin | ReqCPUFreqMax | ReqCPUFreqGov | ReqMem | ConsumedEnergy | MaxDiskRead | MaxDiskReadNode | MaxDiskReadTask | AveDiskRead | MaxDiskWrite | MaxDiskWriteNode | MaxDiskWriteTask | AveDiskWrite | AllocGRES | ReqGRES | ReqTRES | AllocTRES |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 218 | 218 | slurm_simple.sh | batch.q | 12 | 00:00:00 | FAILED | 2:0 | Unknown | Unknown | Unknown | 1Gn | cpu=12,mem=1G,node=1 | cpu=12,mem=1G,node=1 | |||||||||||||||||||||||||||||
| 218.batch | 218.batch | batch | 137940K | dwarf37 | 0 | 137940K | 1576K | dwarf37 | 0 | 1576K | 0 | dwarf37 | 0 | 0 | 00:00:00 | dwarf37 | 0 | 00:00:00 | 1 | 12 | 00:00:00 | FAILED | 2:0 | 1.36G | 0 | 0 | 0 | 1Gn | 0 | 0 | dwarf37 | 65534 | 0 | 0.00M | dwarf37 | 0 | 0.00M | cpu=12,mem=1G,node=1 | ||||
| 218.0 | 218.0 | qqqqstat | 204212K | dwarf37 | 0 | 204212K | 1420K | dwarf37 | 0 | 1420K | 0 | dwarf37 | 0 | 0 | 00:00:00 | dwarf37 | 0 | 00:00:00 | 1 | 12 | 00:00:00 | FAILED | 2:0 | 196.52M | Unknown | Unknown | Unknown | 1Gn | 0 | 0 | dwarf37 | 65534 | 0 | 0.00M | dwarf37 | 0 | 0.00M | cpu=12,mem=1G,node=1 |
If you look at the columns showing Elapsed and State, you can see that they show 00:00:00 and FAILED respectively. This means that the job started and then promptly ended. This points to something being wrong with your submission script. Perhaps there is a typo somewhere in it.
My job ran but didn't finish!
| JobID | JobIDRaw | JobName | Partition | MaxVMSize | MaxVMSizeNode | MaxVMSizeTask | AveVMSize | MaxRSS | MaxRSSNode | MaxRSSTask | AveRSS | MaxPages | MaxPagesNode | MaxPagesTask | AvePages | MinCPU | MinCPUNode | MinCPUTask | AveCPU | NTasks | AllocCPUS | Elapsed | State | ExitCode | AveCPUFreq | ReqCPUFreqMin | ReqCPUFreqMax | ReqCPUFreqGov | ReqMem | ConsumedEnergy | MaxDiskRead | MaxDiskReadNode | MaxDiskReadTask | AveDiskRead | MaxDiskWrite | MaxDiskWriteNode | MaxDiskWriteTask | AveDiskWrite | AllocGRES | ReqGRES | ReqTRES | AllocTRES |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | 220 | slurm_simple.sh | batch.q | 1 | 00:01:27 | TIMEOUT | 0:0 | Unknown | Unknown | Unknown | 1Gn | cpu=1,mem=1G,node=1 | cpu=1,mem=1G,node=1 | |||||||||||||||||||||||||||||
| 220.batch | 220.batch | batch | 370716K | dwarf37 | 0 | 370716K | 7060K | dwarf37 | 0 | 7060K | 0 | dwarf37 | 0 | 0 | 00:00:00 | dwarf37 | 0 | 00:00:00 | 1 | 1 | 00:01:28 | CANCELLED | 0:15 | 1.23G | 0 | 0 | 0 | 1Gn | 0 | 0.16M | dwarf37 | 0 | 0.16M | 0.00M | dwarf37 | 0 | 0.00M | cpu=1,mem=1G,node=1 | ||||
| 220.0 | 220.0 | sleep | 204212K | dwarf37 | 0 | 107916K | 1000K | dwarf37 | 0 | 620K | 0 | dwarf37 | 0 | 0 | 00:00:00 | dwarf37 | 0 | 00:00:00 | 1 | 1 | 00:01:27 | CANCELLED | 0:15 | 1.54G | Unknown | Unknown | Unknown | 1Gn | 0 | 0.05M | dwarf37 | 0 | 0.05M | 0.00M | dwarf37 | 0 | 0.00M | cpu=1,mem=1G,node=1 |
If you look at the column showing State, we can see some pointers to the issue. The job ran out of time (TIMEOUT) and then was killed (CANCELLED).
| JobID | JobIDRaw | JobName | Partition | MaxVMSize | MaxVMSizeNode | MaxVMSizeTask | AveVMSize | MaxRSS | MaxRSSNode | MaxRSSTask | AveRSS | MaxPages | MaxPagesNode | MaxPagesTask | AvePages | MinCPU | MinCPUNode | MinCPUTask | AveCPU | NTasks | AllocCPUS | Elapsed | State | ExitCode | AveCPUFreq | ReqCPUFreqMin | ReqCPUFreqMax | ReqCPUFreqGov | ReqMem | ConsumedEnergy | MaxDiskRead | MaxDiskReadNode | MaxDiskReadTask | AveDiskRead | MaxDiskWrite | MaxDiskWriteNode | MaxDiskWriteTask | AveDiskWrite | AllocGRES | ReqGRES | ReqTRES | AllocTRES |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | 221 | slurm_simple.sh | batch.q | 1 | 00:00:00 | CANCELLED by 0 | 0:0 | Unknown | Unknown | Unknown | 1Mn | cpu=1,mem=1M,node=1 | cpu=1,mem=1M,node=1 | |||||||||||||||||||||||||||||
| 221.batch | 221.batch | batch | 137940K | dwarf37 | 0 | 137940K | 1144K | dwarf37 | 0 | 1144K | 0 | dwarf37 | 0 | 0 | 00:00:00 | dwarf37 | 0 | 00:00:00 | 1 | 1 | 00:00:01 | CANCELLED | 0:15 | 2.62G | 0 | 0 | 0 | 1Mn | 0 | 0 | dwarf37 | 65534 | 0 | 0 | dwarf37 | 65534 | 0 | cpu=1,mem=1M,node=1 |
If you look at the column showing State, we see it was "CANCELLED by 0", then we look at the AllocTRES column to see our allocated resources, and see that 1MB of memory was granted. Combine that with the column "MaxRSS" and we see that the memory granted was less than the memory we tried to use, thus the job was "CANCELLED".
