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Introduction

Running a program on tara is a bit different than running one on a standard workstation. When we log into the cluster, we are interacting with the front end node. But we would like our programs to run on the compute nodes, which is where the real computing power of the cluster is. We will walk through the processes of running serial and parallel code on the cluster, and then later discuss some of the finer details. This page uses the code examples from the compilation tutorial. Please download and compile those examples, so you can follow along.

Resource intensive jobs (long running, high memory demand, etc) should be run on the compute nodes of the cluster. You cannot execute jobs directly on the compute nodes yourself; you must request the cluster's batch system do it on your behalf. To use the batch system, you will submit a special script which contains instructions to execute your job on the compute nodes. When submitting your job, you specify one of the available queues. Your job will wait in the queue until it is "next in line", and free processors on the compute nodes become available. Which job is "next in line" is determined by the scheduling policy of the cluster. Once a job is started, it continues until it either completes or reaches its time limit, in which case it is terminated by the system.

Queues on tara

There are several different queues to which users can submit jobs. Make sure to note that "queues" are also called "partitions" in SLURM terminology. You'll see the term "partition" used when interacting with the system.

Queue	Priority	Node Limits	Default Node Sharing	Default/Max Time Limit	Notes
develop	N/A	two nodes total, no limits per user or per job	Shared	5 minutes / 30 minutes	This queue is for debugging and testing of code, before submitting them to the other queues. Do not run programs on the other queues until you have verified that they run without error and without problems in this queue. Two nodes with InfiniBand connection are available for this queue, so you should be able to test all aspects of code including message passing across and between nodes. This queue uses dedicated hardware and does not compete for resources with the other queues.
long_term	5	four nodes total, one node per user at a time	Shared	4 hours / 5 days	This queue is intended for jobs that require very few nodes, but might run for a long time. The priority given by the scheduler is very low and thus will only be available at times of low system load.
serial	4	all compute nodes available, one core per job, no limit on jobs per user	Shared	4 hours / 23 hours	This queue is for serial jobs only. The scheduler will take jobs from it periodically to fill in the "gaps" in time and space between parallel jobs. Its lower priority than the default queue will ensure that serial jobs aren't preventing parallel jobs from running. Jobs in this queue will not have exclusive access to nodes; i.e. other jobs may run concurrently on different cores of the same node.
parallel	3	all compute nodes available, no limits per user or per job	Exclusive	4 hours / 23 hours	This is the standard queue for all users and for parallel code. All 82 compute nodes are available for scheduling, subject to the limits established by the usage policy. The actual number of nodes that you can get depends on the system load, so you should monitor this (see squeue information below) before submitting jobs. Each job gets exclusive use of nodes through this queue, i.e., one job's nodes are not shared with jobs from other users; this makes this queue inappropriate for typical serial or software package jobs. The default walltime limit is intended to prevent run-away jobs from clogging the queue. Note that the scheduling software uses your requested walltime to schedule jobs into "gaps" in space and time between other parallel jobs, effectively prioritizing shorter jobs.
queues for paying research groups	2	limited to number of nodes purchased by group, no limits per user or per job	TBD	no time limit	Each research group that purchased compute nodes will receive their own queue. Suppose your group has purchased N nodes, then you will be able to run jobs in this queue using up to 8*N cores. The cores will be distributed among the nodes based on your submission script. Jobs in this queue are given high priority by the scheduler, which implements the priority access established in the usage policy. When system load permits, you can run additional jobs through the default queue. Notice that submitting all your jobs to the queue of your research group constitutes also a form of good user behavior because it leaves the public nodes available for all other users. NOTE (4/27/2010): These queues do not exist yet, and may not represent the final solution for giving priority of cluster resources to paying research groups.
perform	1	all compute nodes available, no limits per user or per job	Shared	4 hours / no time limit	This is the highest priority queue and is reserved for admin use, testing purposes, performance studies, and special arrangments. (It has a very different meaning than the high_priority queue on hpc; users do not have access to this queue by default.)

Interacting with the Batch System

There are several basic commands you'll need to know to submit jobs, cancel them, and check their status. These are:

• sbatch - submit a job to the batch queue system
• squeue - check the current jobs in the batch queue system
• sinfo - view the current status of the queues
• scancel - cancel a job

Check here for more detailed information about job monitoring.

scancel

The first command we will mention is scancel. If you've submitted a job that you no longer want, you should be a responsible user and kill it. This will prevent resources from being wasted, and allow other users' jobs to run. Jobs can be killed while they are pending (waiting to run), or while they are actually running. To remove a job from the queue or to cancel a running job cleanly, use the scancel command with the identifier of the job to be deleted. For instance: The job identifier can be obtained from the job listing from squeue (see below) or you might have noted it from the response of the call to sbatch, when you originally submitted the job (also below). Try "man scancel" for more information.

sbatch

Now that we know how to cancel a job, we will see how to submit one. You can use the sbatch command to submit a script to the batch queue system. In this example run.slurm is the script we are sending to the batch queue system. We will see shortly how to formulate such a script. Notice that sbatch returns a job identifier. We can use this to kill the job later if necessary, or to check its status. For more information, check the man page by running "man sbatch".

squeue

You can use the squeue command to check the status of jobs in the batch queue system. Here's an example of the basic usage: The most interesting column is the one titled ST for "status". It shows what a job is doing at this point in time. The state "PD" indicates that the job has been queued. When free processor cores become available and this process is "next in line", it will change to the "R" state and begin executing. You may also see a job with status "CG" which means it's completing, and about to exit the batch system. Other statuses are possible too, see the man page for squeue.Once a job has exited the batch queue system, it will no longer show up in the squeue display.

We can also see several other pieces of useful information. The TIME column shows the current walltime used by the job. For example, job 2578 has been running for 1 day, 2 hours, 40 minutes, and 36 seconds. The nodelist column shows which compute nodes have been assigned to the job. For job 2626, nodes n9, n10, n11, and n12 are being used. However for job 2564, we can see that it's pending because it's waiting on resources.

sinfo

The sinfo command also shows the current status of the batch system, but from the point of view of the queues. Here is an example In this scenario, the two develop nodes are idle, node n8 is busy running a job, and the remaining compute nodes are idle.

Running Serial Jobs

This section assumes you've already compiled the serial hello world example. Now we'll see how to run it several different ways.

Test runs on the front-end node

The most obvious way to run the program is on the front end node, which we normally log into.

We can see the reported hostname which confirms that the program ran on the front end node.

Test runs on the develop queue

Let's submit our job to the testing queue, since we just finished creating it and we're not completely sure that it works. The following script will accomplish this. Save it to your account alongside the "hello-serial" executable. Now we're ready to submit our job to the scheduler. To accomplish this, use the sbatch command as follows If the submission was successful, the sbatch command returns a job identifier to us. We can use this to check the status of the job (squeue), or delete it (scancel) if necessary. This job should run very quickly if there are processors available, but we can try to check its status in the batch queue system. The following command shows that our job is not in the system - it has already completed. We should have obtained two output files. The file slurm.err contains stderr output from our program. If slurm.err isn't empty, check the contents carefully as something may have gone wrong. The file slurm.out contains our stdout output; it should contain the hello world message from our program. Notice that the hostname no longer matches the front end node, but one of the test nodes. We've successfully used one of the compute nodes to run our job. The develop queue limits jobs to five minutes by default, measured in "walltime", which is just the elapsed run time. After your job has reached this time limit, it is stopped by the scheduler. This is done to ensure that everyone has a fair chance to use the cluster.

Production runs on the serial queue

Once our job has been tested and we're confident that it's working correctly, we can run it in the serial queue. Now the walltime limit for our job will be raised from five minutes to four hours (by default). There are also many more compute nodes available in this queue, so we probably won't have to wait long to find a free processor. Start by creating the following script. The only change from the testing queue script is the name of the queue to use. To submit our job to the scheduler, we issue the command We can check the job's status, but in this case it has already completed This time our stdout output file indicates that our job has run on one of the primary compute nodes, rather than a develop node

---

Running Parallel Jobs

This section assumes that you've successfully compiled the parallel hello world example. Now we'll see how to run this program on the cluster.

Test runs on the develop queue

Example 1: Single process

First we will run the hello_parallel program as a single process. This will appear very similar to the serial job case. The difference is that now we are using the MPI-enabled executable hello_parallel, rather than the plain hello_serial executable. Create the following script in the same directory as the hello_parallel program. Notice the addition of the "srun" command before the executable, which is used to launch MPI-enabled programs. We've also added "--nodes=1" and "--ntasks-per-node=1" to specify what kind of resources we'll need for our parallel program. Now submit the script Checking the output after the job has completed, we can see that exactly one process has run and reported back.

Example 2: One node, two processes

Next we will run the job on two processes of the same node. This is one important test, to ensure that our code will function in parallel. We want to be especially careful that the communications work correctly, and that processes don't hang. We modify the single process script and set "--ntasks-per-node=2". Submit the script to the batch queue system Now observe that two processes have run and reported in. Both were located on the same node as we expected.

Example 3: Two nodes, one process per node

Now let's try to use two different nodes, but only one process on each node. This will exercise our program's use of the high performance network, which didn't come into the picture when a single node was used. Submit the script to the batch queue system Notice that again we have two processes, but this time they have distinct processor names.

Example 4: Two nodes, eight processes per node

To illustrate the use of more processes, let's try a job that uses two nodes, eight processes on each node. This is still possible on the develop queue. Therefore it is possible to run small performance studies which are completely restricted to the develop queue. Use the following batch script Submit the script to the batch system Now observe the output. Notice that the processes have reported back in a non-deterministic order, and there are eight per node if you count them.

Production runs on the parallel queue

Now we've tested our program in several important configurations in the develop queue. We know that it performs well, and processes do not hang. We may now want to solve larger problems which are more time consuming, or perhaps we may wish to use more processes. We can promote our code to "production", by simply changing "--partition=develop" to "--partition=parallel".

Some details about the batch system

A SLURM batch script is a special kind of shell script. As we've seen, it contains information about the job like its name, expected walltime, etc. It also contains the procedure to actually run the job. Read on for some important details about SLURM scripting, as well as a few other features that we didn't mention yet.

For more information, try the following sources

• Try the command "man sbatch"
• See the DoIT SLURM Usage Guide
• See the official SLURM documentation

Parts of a SLURM script

Here is a quick reference for the options discussed on this page.

: (colon)	Indicates a commented-out line that should be ignored by the scheduler.
#SBATCH	Indicates a special line that should be interpreted by the scheduler.
srun ./hello_parallel	This is a special command used to execute MPI programs. The command uses directions from SLURM to assign your job to the scheduled nodes.
--job-name=hello_serial	This sets the name of the job; the name that shows up in the "Name" column in squeue's output. The name has no significance to the scheduler, but helps make the display more convenient to read.
--output=slurm.out --error=slurm.err	This tells SLURM where it should send your job's output stream and error stream, respectively. If you would like to prevent either of these streams from being written, set the file name to /dev/null
--partition=parallel	Set the queue (aka partition) in which your job will run. See above on this page for the list of queues and their descriptions.
--nodes=4	Request four nodes. The queue you've chosen will determine if you might share the node with other users, if you don't request all processor cores on the node.
--ntasks-per-node=8	Request eight tasks to be run on each node. The number of tasks may not exceed the number of processor cores on the node.
--ntasks=11	Request 11 tasks for your job.
--time=1-12:30:00	This option sets the maximum amount of time SLURM will allow your job to run before it is automatically killed. In the example shown, we have requested 1 day, 12 hours, 30 minutes, and 0 seconds. Several other formats are accepted such as "HH:MM:SS" (assuming less than a day).
--mail-type=type	SLURM can email you when your job reaches certain states. Set type to one of: BEGIN to notify you when your job starts, END for when it ends, FAIL for if it fails to run, or ALL for all of the above. See the example below.
--mail-user=email@umbc.edu	Specify a recipient(s) for notification emails (see example below)
--mem-per-cpu=MB	Specify a memory limit for each process of your job. The default is 2994
--mem=MB	Specify a memory limit for each node of your job. The default is that there is a per-core limit
--exclusive	Specify that you need exclusive access to nodes for your job. This is the opposite of "--shared". The default behavior depends on the queue you use.
--shared	Specify that your job may share nodes with other jobs. This is the opposite of "--exclusive"
--begin=2010-01-20T01:30:00	Tell the scheduler not to attempt to run the job until the given time has passed.
--dependency=afterany:15030:15031	Tell the scheduler not to run the job until jobs with IDs 15030 and 15031 have completed.
#SBATCH --account=pi_name	Tell the scheduler to charge this job to pi_name

Job scheduling issues

Don't leave large-scale jobs enqueued during weekdays. Suppose you have a job that requires all the nodes on the cluster. If you submit this job to the scheduler, it will remain enqueued until all the nodes become available. Usually the scheduler will allow smaller jobs to run first, but sometimes it will enqueue them behind yours. The latter causes a problem during peak usage hours, because it clogs up the queue and diminishes the overall throughput of the cluster. The best times to submit these large-scale types of jobs are nights and weekends.

Make sure the scheduler is in control of your programs. Avoid spawning processes or running background jobs from your code, as the scheduler can lose track of them. Programs running outside of the scheduler hinder its ability to allocate new jobs to free processors.

Avoid jobs that run for many days uninterrupted. This is a note for users with access to the unlimited time queues. It's best for the overall productivity of the cluster to design jobs that are "medium" in size - they should be large enough to accomplish a significant amount of work, but small enough so that resources are not tied up for too long.

Consider saving your progress. This is related to the issue above. Running your entire computation at once might be impractical or infeasible. Besides the fact that very long jobs can make scheduling new jobs difficult, it can also be very inconvenient for you if they fail after running for several days (due to a hardware issue for example). It may be possible to design your code to save its progress occasionally, so that it won't need to restart from the beginning if there are any issues.

Estimate memory usage before running your job. As discussed later in this page, jobs on tara have a default memory limit which you can raise or lower as needed. If your job uses more than this limit, it will be killed by the scheduler. Requesting the maximum available memory for every job is not a very good user behavior though, because it can lower the overall productivity of the system. Imagine you're running a serial job which requires half the available memory. Specifying this in your submission script will allow the scheduler to use the remaining 7 cores and 12 GB on the node for other users' jobs.

The best strategy is to estimate how much memory you will need, and specify a reasonable upper bound in your submission script. Two suggestions to do this estimation are (1) calculate the sizes of the main objects in your code, and (2) run a small-scale version of the problem and observe the actual memory used. For more information on how to observe memory usage, see checking memory usage.

Estimate walltime usage before running your job. Giving an accurate estimate of your job's necessary walltime is very helpful for the scheduler, and the overall productivity of the cluster. See below for more information, and an example.

Use less nodes and more processes per node. Performance studies have demonstrated that using multiple cores on the same node gives generally comparable performance to using a single core on the same number of nodes. And using the minimum number of nodes required for your job benefits the overall productivity of the cluster. See the technical report HPCF-2010-2.

Consider whether you need exclusive access to your nodes, or if they can be shared with others. If your job isn't using all 8 cores on a node, it might be possible for another job to make use of the free cores. Allowing your nodes to be shared helps to improve the overall productivity of the cluster. The downside however is that other jobs might interfere with the performance of your job. If your job uses a small amount of memory and it's not a performance study (for example), sharing is probably a good option. To see how to control exclusive vs. shared, see the examples below.

Specifying a time limit

Most of the queues on tara have two walltime limits - a default limit and a maximum limit. Let's consider the parallel queue for example. If you do not specify a "--time=" statement in your batch script, the scheduler will use the default four hour time limit. If your job runs over this limit, it will be terminated. You may request a larger time limit, up to 23 hours maximum.

However, it benefits you and other cluster users to give a good estimate, and not just request the maximum. Suppose the system currently has 14 free nodes, and there are two jobs in the parallel queue waiting to run. Suppose also that no additional nodes will become free in the next few hours. The first queued job ("job #1") requires 16 nodes, and the second job ("job #2") requires only 2 nodes. Since job #1 was queued first, job #2 would normally need to wait behind it. However, if the scheduler sees that job #2 would complete in the time that job #1 would be waiting, it can allow job #2 to skip ahead.

Here is an example where we've specified a time limit of 10 hours, 15 minutes, and 0 seconds. Notice that we've started with the batch script from Running Parallel Jobs, Example 1 and added a single "--time=" statement.

Email notifications for job status changes

You can request the scheduler to email you on certain events related to your job. Namely:

• When the job starts running
• When the job exits normally
• If the job is aborted

As an example of how to use this feature, let's ask the scheduler to email us on all three events when running the hello_serial program. Let's start with the batch script developed earlier, and add the options "--mail-type=ALL" and "--mail-user=username@domain.edu". That is, where "username@domain.edu" is your actual email address. After submitting this script, we can check our email and receive the following messages. Because hello_serial is such a trivial program, the start and end emails appear to have been sent simultaneously. For a more substantial program the waiting time could be significant, both for your job to start and for it to run to completion. In this case email notifications could be useful to you.

Controlling exclusive vs. shared access to nodes

Certain queues do not give you exclusive access to the nodes you're assigned by the scheduler, by default. For example, if you submit a job to the serial queue, it may run on a node with other users's jobs. Other queues like the parallel queue have the opposite default behavior, giving exclusive access to any nodes assigned to you. You can override the default behavior of the queue using the "--exclusive" and "--shared" options.

Here's an example where we reserve an entire node in the serial queue.

In this example, we permit sharing of our nodes in the parallel queue.

The memory limit

Jobs on tara are limited to a maximum of 23,954 MB per node out of the total 24 GB system memory. A small amount is reserved for the operating system, to protect against jobs overwhelming the nodes. By default, jobs are limited to 2994 MB per core, based on the number of cores you have requested. If your job goes over the memory limit, it will be killed by the batch system.

The memory limit may be specified per core or per node. To set the limit per core, simply add a line to your submission script as follows:

#SBATCH --mem-per-cpu=4500

where 4500 represents a number of MB. Similarly, to set the limit per node you can use this instead.

#SBATCH --mem=4500

In the serial case, the two options are equivalent. For most parallel situations it is probably more natural to use the per core limit, given that the scheduler has some freedom to assign processes to nodes for you.

If your job is killed because it has exceeded its memory limit, you will receive an error similar to the following in your stderr output. Notice that the effective limit is reported in the error.

slurmd[n1]: error: Job 13902 exceeded 3065856 KB memory limit, being killed
slurmd[n1]: error: *** JOB 13902 CANCELLED AT 2010-04-22T17:21:40 ***
srun: forcing job termination

Note that the memory limit can be useful in conducting performance studies. If your code runs out of physical memory and begins to use swap space, the performance will be severely degraded. For a performance study, this may be considered an invalid result and you may want to try a smaller problem, use more nodes, etc. One way to protect against this is to reserve entire nodes (as discussed elsewhere on this page), and setting the memory limit to less than 23 GB per node. This is about the maximum you can use before swapping starts to occur. Then the batch system will kill your job if it's close enough to swapping.

5/13/2010: Note that when using MVAPICH2, if your job has exclusive access to its assigned nodes (by virtue of the queue you've used - for example the parallel queue, or by the "--exclusive" flag), it will have access to the maximum available memory. This is not the case with OpenMPI. We hope to obtain version of SLURM will support this feature consistently. To avoid confusion in the meantime, we recommend using the "--mem" and "--mem-per_cpu" options as the preferred method of controlling the memory limit.

Requesting an arbitrary number of tasks

So far on this page we've requested some number of nodes, and some number of tasks per node. But what if our application requires a number of tasks like 11, which can not be split evenly among a set of nodes. That is, unless we use one process per node, which isn't a very efficient use of those nodes.

We can split our 11 processes among as few as two nodes, using the following script. Notice that we don't specify anything else like how many nodes to use. The scheduler will figure this out for us, and most likely use the minimum number of nodes (two) to accomodate our tasks.

Running this yields the following output in slurm.out Now suppose we want to limit the number of tasks per node to 2. This can be accomplished with the following batch script. Notice that we needed to move out of the develop queue to demonstrate this scenario. Now we've specified --ntasks-per-node=2 at the top of the script, in addition to --ntasks=11. where we've sorted the output to make it easier to read.

It's also possible to use the "--ntasks" and "--nodes" options together, to specify the number of tasks and nodes, but leave the number of tasks per node up to the scheduler. See "man sbatch" for more information about these options.

Setting a 'begin' time

You can tell the scheduler to wait a specified amount of time before attempting to run your job. This is useful for example, if your job requires many nodes. Being a conscientious user, you may want to wait until late at night for your job to run. By adding the following to your batch script, we can have the scheduler wait until 1:30am on 2010-01-20 for example. You can also specify a relative time See "man sbatch" for more information.

Dependencies

You may want a job to wait until another one starts or finishes. This can be useful if one job's input depends on the other's output. It can also be useful to ensure that you're not running too many jobs at once. For example, suppose we want our job to wait until jobs (job with ID's) 15030 and 15031 complete. This can be accomplished by adding the following to our batch script. See "man sbatch" for more information.

Requeue-ability of your jobs

By default it's assumed that your job can be restarted if a node fails, or if the cluster is about to be brought offline for maintenance. For many jobs this is a safe assumption, but sometimes it may not be.

For example suppose your job appends to an existing data file as it runs. Suppose it runs partially, but then is restarted and then runs to completion. The output will then be incorrect, and it may not be easy for you to recognize. An easy way to avoid this situation is to make sure output files are newly created on each run.

Another way to avoid problems is to specify the following option in your submission script. This will prevent the scheduler from automatically restarting your job if any system failures occur.

For very long-running jobs, you might also want to consider designing them to save their progress occasionally. See job scheduling issues for more information.

Using scratch storage

Temporary scratch storage is available when you run a job on the compute nodes. The storage is local to each node. You can find the name of your scratch directory in the environment variable "$JOB_SCRATCH_DIR" which is provided by SLURM. Here is an example of how it may be accessed by your batch script. Submitting this script should yield something like the following You can of course also access $JOB_SCRATCH_DIR from C, MATLAB, or any other language or package. Remember that the files only exist for the duration of your job, so make sure to copy anything you want to keep to a separate location, before your job exits.

Check here for more information about scratch and other storage areas.

Charging computing time to a PI

If you a member of multiple research groups on tara this will apply to you. When you run a job on tara, the resources you've used (e.g. computing time) are "charged" to your PI. This simply means that there is a record of your group's use of the cluster. Our goal is to make sure everyone has access to their fair share of resources, especially the PIs who have paid for nodes.

You have a "primary" account which your jobs are charged to by default. To see this, try checking one of your jobs as follows (suppose our job has ID 25097)

Notice the "Account=pi_nagaraj" field - in this example, this is our default account. Suppose we are also working for another PI "pi_gobbert". When running jobs for that group, it's only fair that we charge the computing resources to that group instead. To accomplish that, we may add the "--account" option to our batch scripts.

Note that if you specify an invalid name for the account (a group that does not exist, or which you do not belong to), the scheduler will silently revert back to your default account. You can quickly check the status field in the scontrol output to make sure the option worked.

Interactive jobs

Normally interactive programs should be run on the front end node (i.e. not using the scheduler). If you need to run them on the compute nodes for some reason, contact our HPCF Point of Contact as requested in the usage policies.

Jobs stuck in the "PD" state

Your job may become stuck in the "PD" state either if there are not enough nodes available to run your job, or the cluster's scheduler has decided to run other jobs before yours.

A job cannot be run until there are enough free processor cores/nodes to meet its requirement. To illustrate, if somebody submits a job that uses all of the cluster nodes for twelve hours, nobody else can run any jobs until that large job finishes. If you are trying to run a sixteen node job, and there are a set of jobs running which leave less than 16 nodes available, then your job must wait.

When there are a sufficient number of processes/nodes available, the scheduler must decide which job to run next. The decision is based on several factors:

1. The number of nodes your job uses. A job that takes up the entire cluster will not run very soon. Use the options mentioned earlier to set the number of nodes your job uses.
2. The maximum length of time that your job claims it will take to run. As mentioned earlier, give a walltime estimate to give the scheduler an idea of how long this will be. Smaller jobs may be allowed to run ahead of larger ones. If you do not give an estimate, the scheduler will assume a default, which is based on the queue you've submitted to.
3. The job priority. This depends on when you submitted your job (generally first-in-first-out (FIFO) is used) and which queue you use. If you use the perform queue, your job will probably run before jobs in the serial queue.

It is also possible that someone else's job has gotten stuck, or that there is another problem on the cluster. If you suspect that may be the case, run squeue. If there are many jobs whose state ("ST" column) is "R" or "PD" then there are probably no problems on the cluster - there are just a lot of jobs taking up nodes. If a job has been in the "R" state for most of the day, or if you see jobs that are in states other than "PD" or "R" for more than a few seconds, then something is wrong. If this is the case, or if you notice any other strange behavior contact us.