This page gives a basic introduction to the O2 cluster for new cluster users. Reading this page will help you to submit interactive and batch jobs to the Slurm scheduler on O2, as well as teach you how to monitor and troubleshoot your jobs as needed.

Introduction to the Introduction

Learning about a new computer system can be intimidating for some. Some biologists are unfamiliar with UNIX, the command line, and clusters. But don't worry! Literally thousands of biologists have learned how to use our resources. You only need to learn a few things to get started, and if you run into trouble, you can always contact Research Computing for help.

The O2 cluster is a collection of hundreds of computers with thousands of processing cores. We're using the SLURM (Simple Linux Utility for Resource Management) scheduler on O2. SLURM is basically a system for ensuring that the hundreds of users "fairly" share the processors and memory in the cluster.

The basic process of running jobs:

1. You login via SSH (secure shell) to the host: o2.hms.harvard.edu
   
   

   1. If you are connecting to O2 from outside of the HMS network, you will be required to use two-factor authentication.

   2. Please reference this wiki page for detailed instructions on how to login to the cluster.

2. If necessary, you copy your data to the cluster from your desktop or another location. See the File Transfer page for data transfer instructions. You may also want to copy large data inputs to the scratch filesystem for faster processing.

3. You submit your job - for example, a program to map DNA reads to a genome - specifying how long your job will take to run and what partition to run in. You can modify your job submission in many ways, like requesting a large amount of memory, as described below.
   
   

4. Your job sits in a partition (job status PENDING), and when it's your turn to run, SLURM finds a computer that's not too busy.

5. Your job runs on that computer (also known as a "compute node"), and goes to job status RUNNING. While it's running, you don't interact with the program. If you're running a program that requires user input or pointing and clicking, see Interactive Sessions below.

6. The job finishes running (job status COMPLETED, or FAILED if it had an error). You get an email when the job is finished if you specified --mail-type (and optionally --mail-user) in your job submission command.

7. If necessary, you might want to copy data back from the scratch filesystem to a backed-up location, or to your desktop.

Definitions

Here's some of the terminology that you'll find used in relation to SLURM in this and other documents:

• host, node, and server are all just fancy words for a computer

• core Cluster nodes can run multiple jobs at a time. Currently, most of the nodes on O2 have 32 cores, meaning they can run 32 simple jobs at a time. Some jobs can utilize more than one core.

• master host: The system that performs overall coordination of the SLURM cluster. In our case we have a primary server named slurm-prod01 and one backup.

• submission host: When logging in to o2.hms.harvard.edu, you'll actually end up on a "login node" called login01, login02, login03, login04 or login05. You submit your SLURM jobs from there. Do not run any computationally heavy processes on login nodes!
  
  

• execution host: (or "compute node") A system where SLURM jobs will actually run. In O2, compute nodes are named compute-x-yy-zz. x, yy and zz tell the location of the machine in the computer room. 

• partition: Partitions can also be referred to as "queues." When submitting a job, you'll need to specify a partition to submit your job to. If you're not sure which partition to use, see the How to choose a partition in O2 page. We aimed to name the partitions logically, so the partition name will tell you the type of jobs that can be run through them, what resources those jobs can access, who can submit jobs to a given partition, and so forth.
  
  

• filesystem: From your perspective, just a fancy word for a big disk drive. The different filesystems O2 sees have different characteristics, in terms of the speed of reading/writing data, whether they're backed up, etc. See Filesystems.

Where do I store my data?

HMS Research Computing has detailed the various research data storage offerings here. Based upon where you are in the research data lifecycle, you may need to leverage different storage options. When you're in the process of analyzing your data, you can utilize "Active" storage. When you no longer need to use some data for a period of time (but will need to pull it back later or reference or analysis), you can move this data to "Standby" storage. Please reference the aforementioned HMS RC Storage page for a comparison of the various storage options, and to help identify which Storage option is right for your project. You can also reach out to Research Data Management for assistance with managing your research data using the available Storage options.

In terms of which types of storage can be accessed on O2, we currently have Active (Scratch, Compute, and Collaborations) and Standby storage accessible. O2 cluster users can compute against data stored in Scratch, and Compute directories. Data can be transferred to/from Collaborations folders for computation on O2, but cannot be directly used for analysis on O2. Cluster users can move data to/from Standby storage, but cannot compute against data stored there, as the name suggests. Both Collaborations and Standby storage are only accessible from the nodes on O2 dedicated to file transfer.

We can also get more granular in talking about where to store your files, specifically what directories a cluster user can write to on O2. Every cluster user has a /home directory with a 100GiB quota. When you login to the cluster, you will be placed in your home directory. This directory is named like /home/user, where "user" is replaced with your HMS ID in lowercase. Additionally, each cluster user can use /n/scratch for storage of temporary or intermediary files. The per-user scratch quota is 25TiB or 2.5 million files/directories. Any file on scratch that is not modified for 45 days will be deleted, and there are no backups of scratch data. A cluster user must create their scratch directory using a provided script. Finally, there are lab or group directories. These are located under /n/groups, /n/data1, or /n/data2. The quota for group directories is shared for all group members, and there is not a standard quota that all groups have. Any of these directory options (/home, /n/scratch, group directory) can be used to store data that will be computed against on O2. When you submit a job to the SLURM scheduler on O2, you will need to mention in which directory your data (that you want to compute on) is stored and where you want the output data to be stored.

For more details on using these Storage options on O2, please refer to the Filesystems, Filesystems Quotas, Scratch Storage and the File Transfer wiki pages.

Partitions (aka Queues)

The current partition configuration:

Check our How to choose a partition in O2 chart to see which partition you should use.

* For all partitions except for mpi, there is a maximum of 20 cores per job. 

Basic SLURM commands

Note:  Any time <userid> is mentioned in this document, it should be replaced with your HMS ID (formerly eCommons ID) and omit the <>. Likewise, <jobid> should be replaced with an actual job ID, such as 12345. The name of a batch job submission script should be inserted wherever <jobscript> is mentioned.

Submitting Jobs

There are two commands to submit jobs on O2: sbatch or srun.

sbatch is used for submitting batch jobs, which are non-interactive. The sbatch command requires writing a job script to use in job submission. When invoked, sbatch creates a job allocation (resources such as nodes and processors) before running the commands specified in the job script. 

srun is used for starting interactive sessions (commonly used on O2) or job steps (less commonly used on O2). When used for an interactive session, srun will create a new job allocation. It can also be used from within a pre-existing allocation, such as within an sbatch script.

The sbatch command

You submit jobs to SLURM using the sbatch command, followed by the script you'd like to run. When you submit the job, sbatch will give you a numeric JobID that you can later use to monitor your job. The SLURM scheduler will then find a computer that has an open slot matching any specifications you gave (see below on requesting resources), and tell that computer to run your job.

sbatch also accepts option arguments to configure your job, and O2 users should specify all of the following with each job, at a minimum:

• the partition (using -p)

• a runtime limit, i.e., the maximum hours and minutes (-t 2:30:00) the job will run. The job will be killed if it runs longer than this limit, so it's better to overestimate.

Most users will be submitting jobs to the short, medium, long, priority, or interactive partitions, some in mpi. Here is a guide to choose a proper partition:

sbatch options quick reference

The sbatch command can take many, many different flags. This is just a quick description of the most popular flags. They are described in more detail further down the page. 

Only -p and -t are required. If you are running a multi-threaded or parallel job, which splits a job in pieces over multiple cores/threads/processors to run faster, -c or -n is required.

-e errfile

sbatch script and job submission examples

We can specify all the options of sbatch in a script and submit. An example batch script is seen below:

myjob.sh

The batch script can be submitted like this:

You don't need to use a separate script for submitting jobs with sbatch. You can use the --wrap option to run a single command. However, we discourage the use of this parameter for several reasons: (1) it makes your science less reproducible, (2) it makes jobs harder to troubleshoot (SLURM job accounting doesn't retain commands used with --wrap), and (3) certain complex commands (such as piping, or using | ) may not be interpreted properly. Instead, we recommend using sbatch with a script, or running these commands interactively.

Additionally, if you need some flexibility in your script (e.g. you need to submit the same job but with multiple input files), submission scripts can take command line arguments as well as inheriting environment variables (your entire current environment is exported with the job when it submits). Here is a shell script that takes a single command line argument:

arguments.sh

If you submit this script like so:

When the job finishes, you should have an output file called file.txt that contains the text hello world. If you are savvy at bash, you can use this functionality to script loops that can vary the parameters you send to your submission script to submit lots of similar jobs at the same time without having to manually modify the script itself.

You can also take environment variables as well. Say you run the following command:

Then make the following change to arguments.sh before submitting it as above:

You will still get the same result as above, even though you have not exported DESTFILE inside your submission script. The value of DESTFILE was exported along with the rest of your environment at submission time, because it was already set. However, for obvious reasons, it is recommended that you make such exports inside your submission script for documentation purposes so that you have a convenient record of what you set those variables to if you return to your submission some time later.

The srun command

Like sbatch, srun can be used to submit jobs under the SLURM scheduler. sbatch and srun even share many of the same options! However, srun is implemented a bit differently. You can use srun to create job steps (simply put srun in front of the commands you want to run) within an sbatch script, or to start an interactive session. If srun is used within an sbatch script, it will use the preexisting resource allocation. If srun is submitted independent of sbatch, you will need to specify resources to be allocated to your job. 

Interactive Sessions

You can submit "interactive" jobs under SLURM, which allows you to actually log in to a compute node. You can then test commands interactively, watch your code compile, run programs that require user input while the command is running, or use a graphical interface. (Matlab and R can run either in batch mode or graphical mode, for example.) These jobs are intended to be run in the interactive partition, but currently can be used in any partition. It may take longer to be assigned an interactive job in a partition other than interactive, due to the priority of the partition. See How to choose a partition in O2 for more detail.

You can then run your commands, and logout when you're done. The interactive queue has a limit of 12 hours and a default allocation of 1 CPU core and 4GB of memory.

You can request extra memory or multiple cores (up to 20) in an interactive session srun.

Note: It is not recommended to submit srun commands from within an interactive job. These commands will be executed on the same resource allocation as the interactive job. If you want to submit other jobs from within an interactive job, use sbatch commands instead. 

Within sbatch scripts

With the SLURM scheduler, it is encouraged to put srun in front of every command you want to run in an sbatch script. However, this is not required!

Using srun will denote job steps, so when you monitor your job, each srun command will be shown as a different step. Additionally, if your job fails, job steps will help you troubleshoot better by narrowing down which command had the issue.

Here is an example script that will give the hostname of the node it was executed on, and print a message. 

If we save this script as srun_in_sbatch.sh, it can be submitted by sbatch srun_in_sbatch.sh. After the job completes, you can see the job statistics (which will be broken down by numbered job steps) by running sacct -j <jobid> or by using O2_jobs_report -j <jobid> .

Job Arrays

Job arrays can be leveraged to quickly submit a number of similar jobs. For example, you can use job arrays to start multiple instances of the same program on different input files, or with different input parameters. A job array is technically one job, but with multiple tasks.

When using job arrays, you write an sbatch script including resource requests using #SBATCH directives and then the generic command you want to run. The requested resources like cores, memory, walltime, etc. will all be on a per-task basis, not for the overall job array.

The only difference between a job array script and a standard sbatch script is that you need to state how many copies of the analysis you want to run with the --array parameter.

There are two ways to submit job arrays. The first method is to add in the --array parameter on the command line:

Or you can insert this line in the script instead:

Job arrays have two job IDs: the overall job ID for the whole array, and IDs for each task in the array. You can use %a and %A in your output and error filenames to substitute the array index and the job array ID, respectively. Using these filename patterns will mean you can figure out which job array ID and task in the array the file corresponds to based upon its name. It is preferable to use %A and %a instead of %j; %j will give a unique job ID for each task in the array.

Job arrays do not have to be submitted for consecutive indexes. You can submit only for certain indexes, or with a certain step size. It is helpful to be able to run specific indexes, for example, when previously these tasks failed in a prior run.

If files are cleverly numbered, you can reference them with ${SLURM_ARRAY_TASK_ID} which fetches the array ID, e.g. you need to process 30 fastq files, and they are named (something like) fastq1.fastq, fastq2.fastq, etc.:

A different input file will go to each job in the array, mapped 1-to-1. 

The full set of environment variables when a job array is submitted:

To control how many jobs can be executed at a time, specify this inside the --array flag with %. To modify the above sbatch command to only allow 5 running jobs in the array at a time:

This also works with the #SBATCH directive.

Putting all this together, here is a full example job array script to run an array with 4 tasks:

The script would be submitted with sbatch akin to any other batch job submission. Our files in this example would be named consecutively like sample_1_R1.fastq, sample_2_R1.fastq, and so on. There will be 4 tasks to run, one fastqc run for each of the fastq files. ${SLURM_ARRAY_TASK_ID} will be replaced with the actual task id. For the first task, the full analysis command would be fastqc sample_1_R1.fastq. The output would be written to files with unique names (of the form fastqc_%A_%a.out), as %a would be replaced with the array indexes 1-4.

To cancel specific jobs in the array:

Job Dependencies

 If you need to submit a job that is reliant on a previous job(s) (e.g. <jobid> must complete successfully first), use

Other dependency parameters:

Using ? with a dependency allows it to be satisfied no matter what. It is possible to chain multiple dependencies together:

This job will submit only after jobs 1 and 2 have completed successfully AND after 3 and 4 have terminated, and after 5 has failed (or not failed). If any requirement is not met (e.g. job 1 fails), the job will never submit. The squeue command will show these jobs with unmet requirements as PENDING status with DependencyNeverSatisfied for the reason why the job is waiting for execution. 

To allow Slurm to manage dependencies for you, provide the following flag:

If yes, then if a dependency is failed, the job will automatically cancel itself. Our current configuration already removes jobs whose dependencies will never be met, but it is probably best to always include this flag when submitting a job with dependencies (in case our configuration changes in the future).

Monitoring Jobs

There are several commands you may wish to use to see the status of your jobs, including:

• squeue - by default squeue will show information about all users' jobs. Use -u <userid> to get information just about yours. An easier-to-use alternative command to squeue is called O2squeue.
  
  

• scontrol - most scontrol options can't be invoked by regular users, but scontrol show job <jobid> is a useful command that gives detailed job information. This command only works for currently running jobs.

• sstat - shows status information for currently running jobs. Many fields can be requested using the --format parameter. Reference the job status fields in the sstat documentation for more information.
  
  

• sacct - reports accounting information for jobs and job steps. This works for both running or completed jobs, but it is most useful for completed jobs. Many fields can be requested using the --format parameter. Check the job accounting fields in the sacct documentation for more information. An easier-to-use alternative command is called O2_jobs_report.
  
  

Example monitoring commands

Controlling jobs

There are several ways to modify your submitted jobs in SLURM, such as:

• canceling a job using scancel

• pause a job using scontrol hold

• release a job using scontrol release
  
  

Example job monitoring commands

Job Completion

By default, you will not receive an execution report by e-mail when a job you have submitted to Slurm completes. If you would like to receive such notifications, please use --mail-type and optionally --mail-user in your job submission. Currently, the SLURM emails contain minimal information (jobid, job name, run time, status, and exit code); this was an intentional design feature from the SLURM developers. At this time, we suggest running sacct/O2_jobs_report queries for more detailed information than the job emails provide.

Troubleshooting jobs

We recommend monitoring your jobs using commands on O2 such as squeue or O2squeue, and sacct or O2_jobs_report.

More information for a job can be found by running sacct -j <jobid>, or by using O2_jobs_report -j <jobid>. The following command can be used to obtain accounting information for a completed job:

If your job reports COMPLETED status, then your job worked as expected. If your job reports FAILED status, there was a problem with the tool used or the job submission script.

If you get a TIMEOUT error, the CPUTime reported will be greater than the -t limit you specified in your job.

Similarly, if your job used too much memory, you will receive an error like: Job <jobid> exceeded memory limit <memorylimit>, being killed. For this job, sacct or O2_jobs_report will report a larger MaxRSS than ReqMem, and OUT_OF_MEMORY job status. You will need to rerun the job, requesting more memory.

For much more information on figuring out why jobs don't start, or don't finish, see the separate page on troubleshooting Slurm jobs.

If you are contacting Research Computing because a job did not behave as expected, it's often helpful to include the job report in your request. Just attach it to the email, or paste it into the form.

Job output handling

By default, your job output will not be emailed to you, even if you request email notifications. Slurm will automatically put job error and output in a file called slurm-<jobid>.out in the directory you submitted the job from.

If you would prefer, you can direct the output by specifying the -o (stdout) and -e (stderr) options to sbatch:

You can add the execution node name and jobids to the output and error files using %N and %j:

For other available "filename patterns" (what %N and %j are called by the SLURM developers), please reference the SLURM sbatch documentation.

Running graphical programs on O2 with X11

A number of programs with graphical user interfaces (e.g., R, Matlab) use the X11 system which lets the program run on an O2 computer, but show the graphics on your desktop. To do this, you need to have an X11 server running on your desktop, and your SSH connection needs to have X11 forwarding enabled. See this page for X11 setup instructions.

You will need to connect to O2 with the -XY flags in your SSH command. Substitute <username> for your HMS ID:

To enable graphics forwarding within an srun job, add the --x11 flag to your job submission. For example:

For graphics forwarding within sbatch job submissions no additional parameter is required, provided that you submitted a job after having logged in with the corresponding ssh flag.

Running jobs on multiple cores

Modern computers have multiple cores, allowing them to run multiple jobs at once. Many programs allow you to use multiple cores for a single run. (Just to confuse you, different programs talk about running on multiple "processors" or "cores" or "threads" or "CPUs".) This can allow you a substantial speedup, but there are some important issues to be aware of.

Unlike multi-core programs, truly parallel programs use a system like MPI to let you run on more than one computer at a time. For information on that, see the Parallel Jobs on O2 page.

How many cores?

First, you need to tell SLURM the number of cores you want to run on. You do this with the -c flag. The number of cores you request from the program should always be the same as the number of cores you request from SLURM. Note that different programs have different options to specify multiple cores. For example, tophat -p 8 tells the Tophat aligner to use eight cores. So you might have a job submission script that has a line to request 8 cores:

Later on in the script, you have a tophat alignment command, which also specifies using 8 cores:

If you accidentally ask for only one core in the job submission (sbatch -c 1), but try to let your program use multiple cores (tophat -p 8), you will not be able to do so. On O2, CPU usage is restricted to the cores you request, which is performed by the cgroups plugin. You will not receive an explicit error message, but nevertheless, your job will be confined to the allocated cores. For this reason, you may observe a performance decay when running jobs on O2 as compared to other systems that allow you to use multiple cores that are not explicitly allocated to you. 

Time limits

Each job has a runtime limit, which is the maximum number of seconds that the job can be in RUNNING state. This is also known as "wall clock time". You specify this limit with the -t  parameter with sbatch or srun. If your job does not complete prior to the runtime limit, then your job will be killed. Each partition has a maximum time limit. See below for details:

Requesting resources

You may want to request a node with specific resources for your job. For example, your job may require 4 GB of free memory in order to run. Or you might want one of the nodes set aside for file transfers or other purposes.

Memory requirements

Every job requires a certain amount of memory (RAM, "active" memory, not disk storage) to run. If the jobs on a given node use too much memory, this memory exhaustion can adversely impact other running jobs, prevent the dispatch of new jobs, cause nodes to crash, and unfairly benefit a few intensive users at the expense of all other users of that node.

Jobs that use excessive memory without requesting resources may be terminated by Research Computing to allow other jobs to run.

If your job requires more than 1 GiB of memory, please override the defaults. For example, to submit a job to the short queue and reserve 8 GiB of memory for the duration of the job:

Note that without any units appended, memory reservation values are specified in MiB by default. You can change the size unit of the memory request by appending K, G, T for kibibyte, gibibyte, tebibytes etc. 

Filesystem Resources

Filesystem resources make sure that your job is only dispatched to a node that has the appropriate network file system mounted and accessible to it. All users are encouraged to use filesystem resources. During planned maintenance events or unplanned filesystem outages, using a filesystem resource requirement will keep your job from being unnecessarily dispatched and subsequently failing.

The file system resources are:

• groups (/n/groups)

• log (/n/log - for web hosting users)

• files (/n/files - accessible through transfer partition, but you must request access to run jobs in this partition)

• scratch (/n/scratch - for storage of temporary or intermediary files)

Example Usage:

sbatch --constraint="files"

If your job requires multiple filesystems:

sbatch --constraint="files&groups"

Feedback Wanted

We expect to make adjustments to the SLURM configuration as we learn more about how O2 is used. Your feedback will be the best way for us to learn what adjustments we should be considering to make the cluster most useful to the widest audience. We're also interested in feedback on what information is most useful for new users, so that we can expand and improve this documentation. Please email Research Computing if you have any feedback, questions, comments or concerns!