Scheduling jobs

Overview

Teaching: 45 min
Exercises: 30 min

Questions

• What is a scheduler and why are they used?

• How do I launch a program to run on any one node in the cluster?

• How do I capture the output of a program that is run on a node in the cluster?

Objectives

• Run a simple Hello World style program on the cluster.

• Submit a simple Hello World style script to the cluster.

• Use the batch system command line tools to monitor the execution of your job.

• Inspect the output and error files of your jobs.

Job scheduler

An HPC system might have thousands of nodes and thousands of users. How do we decide who gets what and when? How do we ensure that a task is run with the resources it needs? This job is handled by a special piece of software called the scheduler. On an HPC system, the scheduler manages which jobs run where and when.

The following illustration compares these tasks of a job scheduler to a waiter in a restaurant. If you can relate to an instance where you had to wait for a while in a queue to get in to a popular restaurant, then you may now understand why sometimes your jobs do not start instantly as in your laptop.

Job scheduling roleplay (optional)

Your instructor will divide you into groups taking on different roles in the cluster (users, compute nodes and the scheduler). Follow their instructions as they lead you through this exercise. You will be emulating how a job scheduling system works on the cluster.

notes for the instructor here

The scheduler used in this lesson is PBS Pro. Although PBS Pro is not used everywhere, running jobs is quite similar regardless of what software is being used. The exact syntax might change, but the concepts remain the same.

Running a batch job

The most basic use of the scheduler is to run a command non-interactively. Any command (or series of commands) that you want to run on the cluster is called a job, and the process of using a scheduler to run the job is called batch job submission.

In this case, the job we want to run is just a shell script. Let’s create a demo shell script to run as a test.

Creating our test job

Using your favorite text editor, create the following script and run it. Does it run on the cluster or just our login node?

#!/bin/bash

echo 'This script is running on:'
hostname
sleep 60

If you completed the previous challenge successfully, you probably realise that there is a distinction between running the job through the scheduler and just “running it”. To submit this job to the scheduler, we use the qsub command.

yourUsername@eslogin001:~> qsub -q R7090776 -A tc011 -l select=1,walltime=0:10:0 example-job.sh

7099586.sdb

And that’s all we need to do to submit a job. Our work is done – now the scheduler takes over and tries to run the job for us. While the job is waiting to run, it goes into a list of jobs called the queue. To check on our job’s status, we check the queue using the command qstat -u yourUsername.

yourUsername@eslogin001:~> qstat -u yourUsername

sdb: 
                                                            Req'd  Req'd   Elap
Job ID          Username Queue    Jobname    SessID NDS TSK Memory Time  S Time
--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----
7099586.sdb     yourUser R7090776 example-jo          1  24    --  00:10 R 00:00

We can see all the details of our job, most importantly that it is in the “R” or “RUNNING” state. Sometimes our jobs might need to wait in a queue (“Q” or “QUEUED”) or have an error. The best way to check our job’s status is with qstat. Of course, running qstat repeatedly to check on things can be a little tiresome. To see a real-time view of our jobs, we can use the watch command. watch reruns a given command at 2-second intervals. This is too frequent, and will likely upset your system administrator. You can change the interval to a more reasonable value, for example 20 seconds, with the -n 20 parameter. Let’s try using it to monitor another job.

yourUsername@eslogin001:~> qsub -q R7090776 -A tc011 -l select=1,walltime=0:10:0 example-job.sh
yourUsername@eslogin001:~> watch -n 20 qstat -u yourUsername

You should see an auto-updating display of your job’s status. When it finishes, it will disappear from the queue. Press Ctrl-C when you want to stop the watch command.

Customising a job

The job we just ran had a lot of command-line options and used all of the scheduler’s default options. In a real-world scenario, that’s probably not what we want. The default options represent a reasonable minimum. Chances are, we will need more cores and more time. To get access to these resources we must customize our job script.

Comments in UNIX (denoted by #) are typically treated as comments and ignored. But there are exceptions. For instance the special #! comment at the beginning of scripts specifies what program should be used to run it (typically /bin/bash). Schedulers like PBS Pro also have a special comment used to denote special scheduler-specific options. Though these comments differ from scheduler to scheduler, PBS Pro’s special comment is #PBS. Anything following the #PBS comment is interpreted as an instruction to the scheduler. These options can also be passed as command-line arguments, but it is often more convenient to have them hard-wired into the job script.

The clever # trick …

The use of #PBS to denote commands to the scheduler is a very neat (and commonly used in Unix) trick which has a number of useful effects:

• You can specify commands that are interpreted at submission time (e.g. how much wallclock time you require) and commands that are interpreted much later at run time (e.g. the program you want to execute) in the same script.
• If you are using different HPC machines with different schedulers, you can have commands for both schedulers in the same script and the ones that aren’t relevant will simply be ignored as commments
• You can run the script interactively on the login nodes for debugging purposes (e.g. to check that all the input files are copied across properly) and the scheduler commands will be ignored as comment.

Let’s illustrate this by example. By default, a job’s name is the name of the script, but the -N option can be used to change the name of a job.

Submit the following job (qsub -q R7090776 example-job.sh):

#!/bin/bash
#PBS -N new_name
#PBS -A tc011
#PBS -l walltime=00:10:00
#PBS -l select=1

echo 'This script is running on:'
hostname
sleep 60

yourUsername@eslogin001:~> qstat -u yourUsername

sdb: 
                                                            Req'd  Req'd   Elap
Job ID          Username Queue    Jobname    SessID NDS TSK Memory Time  S Time
--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----
7099587.sdb     yourUser R7090776 new_name            1  24    --  00:10 R 00:00

Fantastic, we’ve successfully changed the name of our job!

Setting up email notifications

Jobs on an HPC system might run for days or even weeks. We probably have better things to do than constantly check on the status of our job with qstat. Looking at the man page for qsub, can you set up our test job to send you an email when it finishes?

Resource requests

But what about more important changes, such as the number of cores and memory for our jobs? One thing that is absolutely critical when working on an HPC system is specifying the resources required to run a job. This allows the scheduler to find the right time and place to schedule our job. If you do not specify requirements (such as the amount of time you need), you will likely be stuck with your site’s default resources, which is probably not what we want.

The following are several key resource requests:

• -l select=<nnodes> - how many nodes does your job need? Note that there are 24 cores per node on ARCHER.

• -l walltime=<hours:minutes:seconds> - How much real-world time (walltime) will your job take to run?

Note that just requesting these resources does not make your job run faster! We’ll talk more about how to make sure that you’re using resources effectively in a later episode of this lesson.

Submitting resource requests

Submit a job that will use 2 full nodes and 10 minutes of walltime.

Job environment variables

When PBS Pro runs a job, it sets a number of environment variables for the job. One of these will let us check what directory our job script was submitted from. The PBS_O_WORKDIR variable is set to the directory from which our job was submitted. Using the PBS_O_WORKDIR variable, modify your job so that it prints (to stdout) the location from which the job was submitted.

Resource requests are typically binding. If you exceed them, your job will be killed. Let’s use walltime as an example. We will request 30 seconds of walltime, and attempt to run a job for one minute.

#!/bin/bash
#PBS -A tc011
#PBS -l walltime=00:00:30
#PBS -l select=1

echo 'This script is running on:'
hostname
sleep 60

Submit the job and wait for it to finish. Once it is has finished, check the error file.

yourUsername@eslogin001:~> qsub -q R7090776 example-job.sh
yourUsername@eslogin001:~> watch -n 20 qstat -u yourUsername
yourUsername@eslogin001:~> cat example-job.sh.e7099588

=>> PBS: job killed: walltime 50 exceeded limit 30

Our job was killed for exceeding the amount of resources it requested. Although this appears harsh, this is actually a feature. Strict adherence to resource requests allows the scheduler to find the best possible place for your jobs. Even more importantly, it ensures that another user cannot use more resources than they’ve been given. If another user messes up and accidentally attempts to use all of the cores or memory on a node, PBS Pro will either restrain their job to the requested resources or kill the job outright. Other jobs on the node will be unaffected. This means that one user cannot mess up the experience of others, the only jobs affected by a mistake in scheduling will be their own.

But how much does it cost?

Although your job will be killed if it exceeds the selected runtime, a job that completes within the time limit is only charged for the time it actually used. However, you should always try and specify a wallclock limit that is close to (but greater than!) the expected runtime as this will enable your job to be scheduled more quickly. If you say your job will run for an hour, the scheduler has to wait until a full hour becomes free on the machine. If it only ever runs for 5 minutes, you could have set a limit of 10 minutes and it might have been run earlier in the gaps between other users’ jobs.

Cancelling a job

Sometimes we’ll make a mistake and need to cancel a job. This can be done with the qdel command. Let’s submit a job and then cancel it using its job number (remember to change the walltime so that it runs long enough for you to cancel it before it is killed!).

yourUsername@eslogin001:~> qsub -q R7090776 example-job.sh
yourUsername@eslogin001:~> qstat -u yourUsername

7099590.sdb

sdb: 
                                                            Req'd  Req'd   Elap
Job ID          Username Queue    Jobname    SessID NDS TSK Memory Time  S Time
--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----
7099590.sdb     yourUser R7090776 example-jo          1  24    --  00:10 R 00:00

Now cancel the job with its job number. Absence of any job info indicates that the job has been successfully cancelled.

yourUsername@eslogin001:~> qdel 7099590

... Note that it might take a minute for the job to disappear from the queue ...
yourUsername@eslogin001:~> qstat -u yourUsername

...(no output from qstat when there are no jobs to display)...

Other types of jobs

Up to this point, we’ve focused on running jobs in batch mode. PBS Pro also provides the ability to start an interactive session.

There are very frequently tasks that need to be done interactively. Creating an entire job script might be overkill, but the amount of resources required is too much for a login node to handle. A good example of this might be building a genome index for alignment with a tool like HISAT2. Fortunately, we can run these types of tasks as a one-off with qsub.

qsub (with the right options) can submit a job and then wait for it to start so we can use the compute node resources interactively. Let’s demonstrate this by submitting an interactive job that uses a single node:

yourUsername@eslogin001:~> qsub -q R7090776 -A tc011 -IVl select=1,walltime=0:10:0

You should be presented with a bash prompt. Note that the prompt will likely change to reflect your new location, in this case the worker node we are logged on. You can also verify this with hostname.

When you are done with the interactive job, type exit to quit your session.

Filesystem on ARCHER

At this point it is important to point out a quirk of the ARCHER system. It has two separate filesystems: /home/ and /work/.

• /home/ is meant for small files such as source code, and is the filesystem that you are on when you log in
• /work is a much larger and faster filesystem, meant for production runs and storing large datasets

The /home/ filesytem is not mounted on the compute nodes meaning that programs run in the batch queues cannot read from or write to files in your home directory. This has not been a problem so far as none of our programs have done file input or output. However, the parallel program we will run here reads and writes large images.

• When you log in, you will be in your home directory /home/tc011/tc011/yourUsername/
• Before you run real programs on ARCHER, you must change directory to /work/tc011/tc011/yourUsername/

Running parallel jobs using MPI

As we have already seen, the power of HPC systems comes from parallelism, i.e. having lots of processors/disks etc. connected together rather than having more powerful components than your laptop or workstation. Often, when running research programs on HPC you will need to run a program that has been built to use the MPI (Message Passing Interface) parallel library. The MPI library allows programs to exploit multiple processing cores in parallel to allow researchers to model or simulate faster on larger problem sizes. The details of how MPI work are not important for this course or even to use programs that have been built using MPI; however, MPI programs typically have to be launched in job submission scripts in a different way to serial programs and users of parallel programs on HPC systems need to know how to do this. Specifically, launching parallel MPI programs typically requires four things:

• A special parallel launch program such as mpirun, mpiexec, srun or aprun.
• A specification of how many processes to use in parallel. For example, our parallel program may use 256 processes in parallel.
• A specification of how many parallel processes to use per compute node. For example, if our compute nodes each have 32 cores we often want to specify 32 parallel processes per node.
• The command and arguments for our parallel program.

To illustrate this process, we will use a simple MPI parallel program that sharpens an image. (We will meet this example program in more detail in a later episode.) Here is a job submission script that runs the sharpen program across two compute nodes on the cluster. Create a file called run-sharpen.pbs with the contents of this script in it.

#!/bin/bash
#PBS -N mpisharpen
#PBS -l select=2
#PBS -l walltime=00:10:00
#PBS -A tc011

# Change to the directory that the job was submitted from
cd $PBS_O_WORKDIR

module load epcc-training/sharpen
cp $SHARPEN_DATA/fuzzy.pgm .

aprun -n 48 -N 24 sharpen

The parallel launch line for the sharpen program can be seen towards the bottom of the script:

aprun -n 48 -N 24 sharpen

and this corresponds to the four required items we described above:

1. Parallel launch program: in this case the parallel launch program is called aprun
2. Total number of parallel processes: in this case this is 48 (all the cores on two full compute nodes) and is specified by the -n 48 option to aprun
3. Number of parallel processes per node: in this case this is 24 (number of cores on each node) and is specified by the -N 24 option to aprun (if not specified, the default is actually -N 24)
4. Our program and arguments: in this case this is sharpen, the program we want to run. It does not take any arguments.

As for our other jobs, we launch using the qsub command.

yourUsername@eslogin001:~> qsub -q R7090776 run-sharpen.pbs

7099586.sdb

If your job runs correctly, you should see an output file called sharpened.pgm

yourUsername@eslogin001:~> ls -l *.pgm

-rw-r--r-- 1 yourUsername tc011 1762743 Jun 26 17:29 fuzzy.pgm
-rw------- 1 yourUsername tc011 1678630 Jun 26 17:33 sharpened.pgm

If you only see fuzzy.pgm and not sharpened.pgm then look at both the output and error files from PBS Pro to work out what went wrong.

Running parallel jobs

Modify the sharpen script that you used above to use 3 full nodes rather than 2. Submit the script and check the output to confirm that it used the correct number of cores in parallel for the calculation.

Solution

We need to change the number of nodes requested to 3 in the PBS options and change the number of parallel processes to use in the option to the aprun parallel launch command to 72 (3 * 24 cores per node). Here is the fully modified script

#!/bin/bash
#PBS -N mpisharpen
#PBS -l select=3
#PBS -l walltime=00:10:00
#PBS -A tc011

# Change to the directory that the job was submitted from
cd $PBS_O_WORKDIR

module load epcc-training/sharpen
cp $SHARPEN_DATA/fuzzy.pgm .

aprun -n 72 -N 24 sharpen

Configuring parallel jobs

You will see in the job output that information is displayed about where each MPI process is running, in particular which node it is on.

Modify the sharpen script that you used above to still use 3 nodes, but to only use a total of 48 processes with 16 processes per node. Check the output file to ensure that you understand the job distribution.

Solution

#!/bin/bash
#PBS -N mpisharpen
#PBS -l select=3
#PBS -l walltime=00:10:00
#PBS -A tc011

# Change to the directory that the job was submitted from
cd $PBS_O_WORKDIR

module load epcc-training/sharpen
cp $SHARPEN_DATA/fuzzy.pgm .

aprun -n 48 -N 16 sharpen

What do you think the job distribution will be if you run 48 processes with 21 processes per node? Does this agree with what you see in practice?

Key Points

• The scheduler handles how compute resources are shared between users.

• Everything you do should be run through the scheduler.

• A job is just a shell script.

• If in doubt, request slightly more resources than you will need.