The Torque cluster job scheduler is an open-source scheduler based on the original PBS codebase. TORQUE can be integrated with both the open-source Maui cluster scheduler or the commercial Moab workload manager.
See Cluster job schedulers for a description of the different use-cases of a cluster job-scheduler.
Running an interactive job¶
You can start a new interactive job on your Flight Compute cluster by using the
qsub -I command; the scheduler will search for an available compute node, and provide you with an interactive login shell on the node if one is available.
In the above command, the
qsub command is used together with the option
-I which informs the cluster scheduler you wish to start an interactive job.
qsub -I command can also be executed from an interactive desktop session; the job-scheduler will automatically find an available compute node to launch the job on. Applications launched from within the interactive session are executed on the assigned cluster compute node.
In order to run graphical applications within an interactive session, you must launch your interactive session with the
-X option, which enables X forwarding. Launch an interactive session with X forwarding with the following command:
qsub -I -X
When you’ve finished running your application in your interactive session, simply type
logout, or press Ctrl+D to exit the interactive job.
If the job-scheduler could not satisfy the resource you’ve requested for your interactive job (e.g. all your available compute nodes are busy running other jobs), the job will queue until resources are available:
[[email protected](scooby) ~]$ qsub -I qsub: waiting for job 5.login1.scooby.prv.alces.network to start
Submitting a batch job¶
Batch (or non-interactive) jobs allow users to leverage one of the main benefits of having a cluster scheduler; jobs can be queued up with instructions on how to run them and then executed across the cluster while the user does something else. Users submit jobs as scripts, which include instructions on how to run the job - the output of the job (stdout and stderr in Linux terminology) is written to a file on disk for review later on. You can write a batch job that does anything that can be typed on the command-line.
We’ll start with a basic example - the following script is written in
bash (the default Linux command-line interpreter). You can create the script yourself using the Nano command-line editor - use the command
nano simplejobscript.sh to create a new file, then type in the contents below. The script does nothing more than print some messages to the screen (the
lines), and sleeps for 120 seconds. We’ve saved the script to a file called
simplejobsscript.sh - the
.sh extension helps to remind us that this is a
shell script, but adding a filename extension isn’t strictly necessary for Linux.
#!/bin/bash -l echo "Starting running on host $HOSTNAME" sleep 120 echo "Finished running - goodbye from $HOSTNAME"
We use the
-l option to
bash on the first line of the script to request a login session. This ensures that environment modules can be loaded as required as part of your script.
We can execute that script directly on the login node by using the command
bash simplejobscript.sh - after a couple of minutes, we get the following output:
Started running on host login1 Finished running - goodbye from login1
To submit your job script to the cluster job scheduler, use the command
qsub simplejobscript.sh. The job scheduler should immediately report the job-ID for your job; your job-ID is unique for your current Alces Flight Compute cluster - it will never be repeated once used.
Viewing and controlling queued jobs¶
Once your job has been submitted, use the
qstat command to view the status of the job queue. If you have available compute nodes, your job should be shown in the
R (running) state; if your compute nodes are busy, or you’ve launched an auto-scaling cluster and currently have no running nodes, your job may be shown in the
Q (queued) state until compute nodes are available to run it. Jobs shown in
C state have completed, and are automatically removed from the job queue after a few minutes.
You can keep running the
qstat command until your job finishes running. The output of your batch job will be stored in a file for you to look at. The default location to store the output file is your home directory. You can use the Linux
more command to view your output file:
[[email protected](scooby) ~]$ more simplejobscript.sh.o26 Running on host node-x4a Finished running - goodbye from node-x4a
Your job runs on whatever node the scheduler can find which is available for use - you can try submitting a bunch of jobs at the same time, and using the
qstat -n command, see which node each job is running on.
[[email protected](scooby) ~]$ qstat -n login1.scooby.prv.alces.network: Req'd Req'd Elap Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time ----------------------- ----------- -------- ---------------- ------ ----- ------ --------- --------- - --------- 12.login1.scooby.prv.alce alces batch simplejobscript. 7320 1 1 -- 01:00:00 R 00:01:46 node-x4a 13.login1.scooby.prv.alce alces batch simplejobscript. 9602 1 1 -- 01:00:00 R 00:01:48 node-xb3 14.login1.scooby.prv.alce alces batch simplejobscript. 4286 1 1 -- 01:00:00 R 00:01:49 node-xd2
The scheduler is likely to spread jobs around over different nodes (if you have multiple nodes). The login node is not included in your cluster for scheduling purposes - jobs submitted to the scheduler will only run on your cluster compute nodes. You can use the
qdel <job-ID> command to delete a job you’ve submitted, whether it’s running or still in the queued state.
[[email protected](scooby) ~]$ qsub simplejobscript.sh 45.login1.scooby.prv.alces.network [[email protected](scooby) ~]$ qsub simplejobscript.sh 46.login1.scooby.prv.alces.network [[email protected](scooby) ~]$ qsub simplejobscript.sh 47.login1.scooby.prv.alces.network [[email protected](scooby) ~]$ qsub simplejobscript.sh 48.login1.scooby.prv.alces.network [[email protected](scooby) ~]$ qdel 47 [[email protected](scooby) ~]$ qstat Job ID Name User Time Use S Queue ------------------------- ---------------- --------------- -------- - ----- 45.login1 ...ejobscript.sh alces 0 R batch 46.login1 ...ejobscript.sh alces 0 R batch 47.login1 ...ejobscript.sh alces 00:00:00 C batch 48.login1 ...ejobscript.sh alces 0 R batch
Viewing compute host status¶
Users can use the
pbsnodes -a or
pbsnodes -l 'up' options to view cluster node information. Any options other than
-a require PBS manager or PBS operator privileges.
Users can view compute host status in the following formats:
[root@login1(scooby) ~]# pbsnodes -l 'up' node-xb3.scooby.prv.alc free node-x4a.scooby.prv.alc free node-xd2.scooby.prv.alc free node-x94.scooby.prv.alc free [root@login1(scooby) ~]# pbsnodes -a node-xb3.scooby.prv.alces.network state = free power_state = Running np = 2 ntype = cluster status = rectime=1473089112,macaddr=0a:d7:ca:29:2a:a7,cpuclock=Fixed,varattr=,jobs=,state=free,netload=123268589,gres=,loadave=0.00,ncpus=2,physmem=3689160kb,availmem=3390616kb,totmem=3689160kb,idletime=3992,nusers=0,nsessions=0,uname=Linux node-xb3 3.10.0-327.18.2.el7.x86_64 #1 SMP Thu May 12 11:03:55 UTC 2016 x86_64,opsys=linux mom_service_port = 15002 mom_manager_port = 15003 node-x4a.scooby.prv.alces.network state = free power_state = Running np = 2 ntype = cluster status = rectime=1473089112,macaddr=0a:fd:8b:97:43:f1,cpuclock=Fixed,varattr=,jobs=,state=free,netload=121838538,gres=,loadave=0.00,ncpus=2,physmem=3689160kb,availmem=3402548kb,totmem=3689160kb,idletime=2652,nusers=0,nsessions=0,uname=Linux node-x4a 3.10.0-327.18.2.el7.x86_64 #1 SMP Thu May 12 11:03:55 UTC 2016 x86_64,opsys=linux mom_service_port = 15002 mom_manager_port = 15003 node-xd2.scooby.prv.alces.network state = free power_state = Running np = 2 ntype = cluster status = rectime=1473089113,macaddr=0a:77:b2:48:26:93,cpuclock=Fixed,varattr=,jobs=,state=free,netload=119609907,gres=,loadave=0.00,ncpus=2,physmem=3689160kb,availmem=3402008kb,totmem=3689160kb,idletime=1443,nusers=0,nsessions=0,uname=Linux node-xd2 3.10.0-327.18.2.el7.x86_64 #1 SMP Thu May 12 11:03:55 UTC 2016 x86_64,opsys=linux mom_service_port = 15002 mom_manager_port = 15003 node-x94.scooby.prv.alces.network state = free power_state = Running np = 2 ntype = cluster status = rectime=1473089103,macaddr=0a:82:bd:7d:5d:dd,cpuclock=Fixed,varattr=,jobs=,state=free,netload=118696570,gres=,loadave=0.00,ncpus=2,physmem=3689160kb,availmem=3403592kb,totmem=3689160kb,idletime=1026,nusers=0,nsessions=0,uname=Linux node-x94 3.10.0-327.18.2.el7.x86_64 #1 SMP Thu May 12 11:03:55 UTC 2016 x86_64,opsys=linux mom_service_port = 15002 mom_manager_port = 15003
pbsnodes output will display some of the following information about the compute hosts in your cluster:
- The hostname of your compute nodes
- The number of nodes in the list
- Current usage of the node - if no jobs are running, the state will be listed as
- The detected number of CPUs (including hyper-threaded cores)
- The amount of memory in KB per node
- The amount of disk space available per node
In order to promote efficient usage of your cluster, the job-scheduler automatically sets a number of default resources to your jobs when you submit them. These defaults must be overridden by users to help the scheduler understand how you want it to run your job - if we don’t include any instructions to the scheduler, then our job will take the defaults shown below. If there is no default limit in place, the limit will be unlimited or not defined - it is important to inform the cluster scheduler how much of each resource you require.
- Maximum job runtime (in hours):
- Default number of nodes:
You can view any default limits in place on the default
batch queue with the following command:
[root@login1(torque) ~]# qmgr -c 'list queue batch' Queue batch queue_type = Execution total_jobs = 1 state_count = Transit:0 Queued:0 Held:0 Waiting:0 Running:1 Exiting:0 Complete:0 resources_default.nodes = 1 resources_default.walltime = 01:00:00 mtime = Mon Sep 19 09:18:33 2016 resources_assigned.nodect = 1 enabled = True started = True
Providing job-scheduler instructions¶
Users can help the scheduler to understand how you want it to run your job by providing instructions - job instructions can be provided in two ways; they are:
Job instructions can be provided in two ways; they are:
- On the command line, as parameters to your
qsubcommand. For example, you can set the name of your job using the
- In your job script, by including the scheduler directives at the top of your job script - you can achieve the same effect as providing options with the
qsubcommand. Lines in your script containing scheduler directives must start with
#PBSand be located at the top of your script, after the shell line. Create an example job script or modify your existing script to include a scheduler directive to use a specified job name:
[[email protected](scooby) ~]$ cat simplejobscript.sh #!/bin/bash -l #PBS -N mytestjob echo "Running on host $HOSTNAME" sleep 120 echo "Finished running - goodbye from $HOSTNAME" [[email protected](scooby) ~]$ qsub simplejobscript.sh 51.login1.scooby.prv.alces.network [[email protected](scooby) ~]$ qstat Job ID Name User Time Use S Queue ------------------------- ---------------- --------------- -------- - ----- 49.login1 mytestjob alces 00:00:00 C batch 50.login1 mytestjob alces 0 R batch 51.login1 mytestjob alces 0 R batch
Including job scheduler instructions in your job-scripts is often the most convenient method of working for batch jobs - follow the guidelines below for the best experience:
- Lines in your script that include job-scheduler directives must start with
#PBSat the beginning of the line
- You can have multiple lines starting with
#PBSin your job-script, but they must appear at the top of the script without any lines in-between
- You can put multiple instructions separated by a space on a single line starting with
- The scheduler will parse the script from top to bottom and set instructions in order; if you set the same parameter twice, the second value will be used
- Instructions are parsed at job submission time, before the job itself has actually run. This means you can’t, for example, tell the scheduler to put your job output in a directory that you create in the job-script itself - the directory will not exist when the job starts running, and your job will fail with an error
- You can use dynamic variables in your instructions (see below)
Dynamic scheduler variables¶
Your cluster job scheduler automatically creates a number of pseudo environment variables which are available to your job-scripts when they are running on cluster compute nodes, along with standard Linux variables. Useful values include the following:
$HOMEThe location of your home-directory
$USERThe Linux username of the submitting user
$HOSTNAMEThe Linux hostname of the compute node running the job
$PBS_JOBIDJob allocation number
$PBS_ARRAYIDJob array ID (index) number
Simple scheduler instruction examples¶
Here are some commonly used scheduler instructions, along with some examples of their usage:
Setting output file location¶
To set the output file location for your job, use the
-o [file_name] option. This will send all
stdout to the specified file. The
-e [file_name] option can also be used to specify an ouput file for all
stderr. If you wish to combine both
stderr to the same output file - you can use the option
-j oe [file_name].
By default, the scheduler stores data relative to your home-directory - but to avoid confusion, we recommend specifying a full path to the filename to be used. Although Linux can support several jobs writing to the same output file, the result is likely to be garbled - it’s common practice to include something unique about the job (e.g. it’s job-ID) in the output filename to make sure your job’s output is clear and easy to read.
The directory used to store your job output file(s) must exist before you submit your job to the scheduler. Your job may fail to run if the scheduler cannot create the output file in the directory requested.
For example; the following job-script includes a
-o [file_name] instruction to set the output file location:
#!/bin/bash -l #PBS -N mytestjob -o testjob.$PBS_JOBID echo "Starting running on host $HOSTNAME" sleep 120 echo "Finished running - goodbye from $HOSTNAME"
In the above example, assuming the job was submitted as the
alces user and was given the job-ID number
53, the scheduler will save the output data from the job in the filename
The directory specified must exist and be accessible by the compute node in order for the job you submitted to run.
Setting working directory for your job¶
Torque uses the directory that the job was submitted from to define the working directory for a job - no matter the location of the job submission script. For example, on your cluster if you create a new directory in your home directory named
cd to the
You can then submit a job script that exists in any directory, and the job output and working directory will be the current working directory. The dynamic variable
$PBS_O_WORKDIR variable should be used to determine the working directory. The following example job script demonstrates this functionality:
Waiting for a previous job before running¶
You can instruct the scheduler to wait for an existing job to finish before starting to run the job you are submitting with the
-W depend=[spec] option. For example, to wait until the job ID
55 has finished, the following example command can be used:
[[email protected](scooby) ~]$ qsub simplejobscript.sh 55.login1.scooby.prv.alces.network [[email protected](scooby) ~]$ qsub -W depend=afterok:55 simplejobscript.sh 56.login1.scooby.prv.alces.network [[email protected](scooby) ~]$ qstat Job ID Name User Time Use S Queue ------------------------- ---------------- --------------- -------- - ----- 54.login1 mytestjob alces 00:00:00 C batch 55.login1 mytestjob alces 0 R batch 56.login1 mytestjob alces 0 H batch
Your job will be held in
H (hold) state until the dependency condition is met.
Running task array jobs¶
A common workload is having a large number of jobs to run which basically do the same thing, aside perhaps from having different input data. You could generate a job-script for each of them and submit it, but that’s not very convenient - especially if you have many hundreds or thousands of tasks to complete. Such jobs are known as task arrays - an embarrassingly parallel job will often fit into this category.
A convenient way to run such jobs on a cluster is to use a task array, using the
-t [array_spec] directive. Your job-script can then use the pseudo environment variables created by the scheduler to refer to data used by each task in the job. The following example job-script uses the
$PBS_ARRAYID variable to echo its current task ID to an output file:
#!/bin/bash -l #PBS -N array_job #PBS -j oe array_job.$PBS_JOBID.$PBS_ARRAYID #PBS -t 1-5 echo "Hello from $PBS_ARRAYID - part of $PBS_JOBID"
The example script will create output files for each of the task array jobs run through the scheduler:
All tasks in an array job are given a job ID with the format
54 would be job number
54, array task
Array jobs can easily be cancelled using the
qdel command - the following examples show various levels of control over an array job:
- Cancels all array tasks under the job ID
qdel -t 100-200 60
- Cancels array tasks
100-200under the job ID
qdel -t 5 60
- Cancels array task
5under the job ID
When cancelling array tasks under an array job, the job ID number must include the two empty brackets
 as shown after the job ID
Requesting more resources¶
By default, jobs are constrained to a default set of resources - users can use scheduler instructions to request more resources for their jobs. The following documentation shows how these requests can be made.
Running multi-threaded jobs¶
If users want to use multiple cores on a compute node to run a multi-threaded application, they need to inform the scheduler - this allows jobs to be efficiently spread over compute nodes to get the best possible performance. Using multiple CPU cores is achieved by specifying
-l mppwidth=[count] option in either your submission command or the scheduler directives in your job script. The
-l mppwidth=[count] option informs the scheduler of the number of cores you wish to reserve for use. If the parameter is omitted, a default of 1 core is assumed. You could specify the option
-l mppwidth=4 to request 4 CPU cores for your job.
Running Parallel (MPI) jobs¶
If users want to run parallel jobs via a message passing interface (MPI), they need to inform the scheduler - this allows jobs to be efficiently spread over compute nodes to get the best possible performance. Using multiple CPU cores across multiple nodes is achieved by specifying the
-l nodes=X:ppn=Y option either in your job submission command or your job-script directives, to request Y cores on each of X nodes.
For example, to use 8 CPU cores on the cluster for a single application - you could use the following scheduler directive:
-l nodes=4:ppn=2Request 4 nodes using 2 cores across each requested node
The following example shows launching the Intel Message-passing (IMB) MPI benchmark across 64 cores on your cluster. This application is launched via the OpenMPI
mpirun command - the number of threads and list of hosts to use are specified as parameters to
mpirun. This jobscript loads the
apps/imb module before launching the application, which automatically loads the module for OpenMPI.
#!/bin/bash -l #PBS -l nodes=8:ppn=8 #PBS -N imb #PBS -j oe $HOME/outputs/imb_mpi.out.$PBS_JOBID module load apps/imb echo "List of nodes to use:" echo "---------------------" cat $PBS_NODEFILE mpirun --prefix $MPI_HOME \ -np 8 \ -npernode 2 \ --hostfile $PBS_NODEFILE \ $(which IMB-MPI1)
The above example job script demonstrates several additionally required options in the
mpirun command - most importantly
-np <number> and
-npernode <number>. These options define the total number of MPI processes, as well as the number of MPI processes per node to spawn.
Once the above job-script is submitted to the job-scheduler, the required number of nodes will be allocated for execution of the workload; e.g.
[[email protected](scooby) outputs]$ qsub ../imb_mpi.sh 35.login1.scooby.prv.alces.network [[email protected](scooby) outputs]$ cat imb.o35 List of nodes to use: --------------------- node-x90.scooby.prv.alces.network node-x90.scooby.prv.alces.network node-xd7.scooby.prv.alces.network node-xd7.scooby.prv.alces.network node-x81.scooby.prv.alces.network node-x81.scooby.prv.alces.network node-xc3.scooby.prv.alces.network node-xc3.scooby.prv.alces.network benchmarks to run PingPong #------------------------------------------------------------ # Intel (R) MPI Benchmarks 4.0, MPI-1 part #------------------------------------------------------------ # Date : Tue Sep 6 10:26:04 2016
If you request more CPU cores than your cluster can accommodate, your job will wait in the queue. If you are using the Flight Compute auto-scaling feature, your job will start to run once enough new nodes have been launched.
Requesting more memory¶
In order to promote best-use of the cluster scheduler - particularly in a shared environment, it is recommended to inform the scheduler the maximum required memory per submitted job. This helps the scheduler appropriately place jobs on the available nodes in the cluster.
You can specify the maximum amount of memory required per submitted job with the
-l mem=[XXXmb] option. This informs the scheduler of the memory required for the submitted job. Optionally - you can also request an amount of memory per CPU core rather than a total amount of memory required per job.
When running a job across multiple compute hosts, the
-l mem=[XXXmb] option informs the scheduler of the required memory per node
Requesting a longer runtime¶
In order to promote best-use of the cluster scheduler, particularly in a shared environment, it is recommended to inform the scheduler of the amount of time the submitted job is expected to take. You can inform the cluster scheduler of the expected runtime using the
-l walltime=[hh:mm:ss] option. For example - to submit a job that runs for a maximum of 2 hours, the following example job script could be used:
#!/bin/bash -l #PBS -l walltime=02:00:00
This guide is a quick overview of some of the many available options of the TORQUE cluster scheduler. For more information on the available options, you may wish to reference some of the following available documentation for the demonstrated TORQUE commands;
- Use the
man qstatcommand to see a full list of scheduler queue instructions
- Use the
man qsubcommand to see a full list of scheduler submission instructions
- Online documentation for the TORQUE scheduler is available here