Using Great Lakes

1Before you can use the Great Lakes cluster, the Principal Investigator (PI) must establish a Slurm account by contacting HPC Support with lists of users, admins, and a shortcode. Trial accounts are also available for new PIs.

Email Support

2 A Great Lakes cluster login account is also needed for each user, which will give you access to run jobs.

Fill Out Form

See the Great Lakes cluster cheat sheet for a list of common Linux (Bash) and Slurm commands, including Torque and Slurm comparisons.

Cheat Sheet (PDF)


Cluster Defaults and Partition Limits

Great Lakes Cluster Defaults

Cluster Defaults Default Value
Default walltime 60 minutes
Default memory Per CPU 768 MB
Default number of CPUs

no memory specified: 1 core
Memory specified: memory/768 = # of cores (rounded down)

/scratch file deletion policy

60 days without being accessed.  (see SCRATCH STORAGE POLICIES below)

/scratch quotas per root account

10 TB storage and 1 million inode limit

/home quota per user

80 GB

Max queued jobs per user per account


Shell timeout if idle:

2 hours

Great Lakes Partition Limits

Partition Limit standard gpu largemem standard-oc viz debug*
Max walltime 2 weeks

1 day 4 hours
Max running Mem per root account 7,000 GB 1.5 TB 660 GB 189 GB 40 GB
Max running CPUs per root account 500 cores 36 cores 132 cores 36 cores 8 cores
Max running GPUs per root account n/a 5 Tesla V100 n/a n/a 1 Tesla P40 n/a

*all debug limits are per user, and only one job can run at a time.  largemem and standard-oc limits are per account


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Getting Started (Command Line)

1. Get Duo

You must use Duo authentication to log on to Great Lakes.  Get more details on the Safe Computing Two-Factor page and enroll here.

2. Get a Great Lakes user login

You must establish a user login on Great Lakes by filling out this form.

3. Get an SSH Client & Connect to Great Lakes

You must be on campus or on the VPN to connect to Great Lakes.  If you are trying to log in from off campus, or using an unauthenticated wireless network such as MGuest, you have a couple of options:

Mac or Linux:

Open Terminal and type:


You will be required to enter your Kerberos level-1 password to log in. Please note that as you type your password, nothing you type will appear on the screen; this is completely normal. Press “Enter/Return” key once you are done typing your password.

Windows (using PuTTY):

Download and install PuTTY here.

Launch PuTTY and enter as the host name, then click open.

If you receive a “PuTTY Security Alert” pop-up, this is completely normal, click the “Yes” option. This will tell PuTTY to trust the host the next time you want to connect to it. From there, a terminal window will open; you will be required to enter your UMICH uniqname and then your Kerberos level-1 password in order to log in. Please note that as you type your password, nothing you type will appear on the screen; this is completely normal. Press “Enter/Return” key once you are done typing your password.

All Operating Systems:

At the “Enter a passcode or select one of the following options:” prompt, type the number of your preferred choice for Duo authentication.

4. Get files

You can use SFTP (best for simple transfers of small files) or Globus (best for large files or a commonly used endpoint) to transfer data to your /home directory.

SFTP: Mac or Windows using FileZilla
  1. Open FileZilla and click the “Site Manager” button
  2. Create a New Site, which you can name “Great Lakes” or something similar
  3. Select the “SFTP (SSH File Transfer Protocol)” option
  4. In the Host field, type
  5. Select “Interactive” for Logon Type
  6. In the User field, type your uniqname
  7. Click “Connect”
  8. Enter your Kerberos password
  9. Select your Duo method (1-3) and complete authentication
  10. Drag and drop files between the two systems
  11. Click “Disconnect” when finished

On Windows, you can also use WinSCP with similar settings, available alongside PuTTY here.

SFTP: Mac or Linux using Terminal

To copy a single file, type:

scp localfile

To copy an entire directory, type:

scp -r localdir

These commands can also be reversed in order to copy files from Great Lakes to your machine:

scp -r localdir

You will need to authenticate via Duo to complete the file transfer.

Globus: Windows, Mac, or Linux

Globus is a reliable high performance parallel file transfer service provided by many HPC sites around the world. It enables easy transfer of files from one system to another, as long as they are Globus endpoints.

  • The Globus endpoint for Great Lakes is “umich#greatlakes”.
How to use Globus

Globus Online is a web front end to the Globus transfer service. Globus Online accounts are free and you can create an account with your University identity.

  • Set up your Globus account and learn how to transfer files using the Globus documentation.  Select “University of Michigan” from the dropdown box to get started.
  • Once you are ready to transfer files, enter “umich#greatlakes” as one of your endpoints.
Globus Connect Personal

Globus Online also allows for simple installation of a Globus endpoint for Windows, Mac, and Linux desktops and laptops.

  • Follow the Globus instructions to download the Globus Connect Personal installer and set up an endpoint on your desktop or laptop.
Batch File Copies

A non-standard use of Globus Online is that you can use it to copy files from one location to another on the same cluster. To do this use the same endpoint (umich#greatlakes as an example) for both the sending and receiving machines. Setup the transfer and Globus will make sure the rest happens. The service will email you when the copy is finished.

Command Line Globus

There are Command line tools for Globus that are intended for advanced users. If you wish to use these, contact HPC support.

5. Submit a job

This is a simple guide to get your jobs up and running. For more advanced Slurm features, see the Slurm User Guide for Great Lakes. If you are familiar with using the resource manager Torque, you may find the migrating from Torque to Slurm guide useful.

Batch Jobs

Most work will be queued to be run on Great Lakes and is described through a batch script. The sbatch command is used to submit a batch script to Slurm. To submit a batch script simply run the following from a shared file system; those include your home directory, /scratch, and any directory under /nfs that you can normally use in a job on Flux. Output will be sent to this working directory (jobName-jobID.log). Do not submit jobs from /tmp or any of its subdirectories.

$ sbatch

The batch job script is composed of three main components:

  • The interpreter used to execute the script
  • #SBATCH directives that convey submission options
  • The application(s) to execute along with its input arguments and options


# The interpreter used to execute the script

#“#SBATCH” directives that convey submission options:

#SBATCH --job-name=example_job
#SBATCH --mail-type=BEGIN,END
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem-per-cpu=1000m 
#SBATCH --time=10:00
#SBATCH --account=test
#SBATCH --partition=standard

# The application(s) to execute along with its input arguments and options:

sleep 60

How many nodes and processors you request will depend on the capability of your software and what it can do. There are four common scenarios:

Example: One Node, One Processor

This is the simplest case and is shown in the example above. The majority of software cannot use more than this. Some examples of software for which this would be the right configuration are SAS, Stata, R, many Python programs, most Perl programs.

NOTE: If you will be using licensed software, for example, Stata, SAS, Abaqus, Ansys, etc., then you may need to request licenses. See below table of common submission options for the syntax; in the Software section, we show the command to see which software requires you to request a license.

#SBATCH --job-name JOBNAME
#SBATCH --nodes=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: One Node, Multiple Processors

This is similar to what a modern desktop or laptop is likely to have. Software that can use more than one processor may be described as multicore, multiprocessor, or mulithreaded. Some examples of software that can benefit from this are MATLAB and Stata/MP. You should read the documentation for your software to see if this is one of its capabilities.

#SBATCH --job-name JOBNAME
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: Multiple Nodes, One Process per CPU

This is the classic MPI approach, where multiple machines are requested, one process per processor on each node is started using MPI. This is the way most MPI-enabled software is written to work.

#SBATCH --job-name JOBNAME
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: Multiple Nodes, Multiple CPUs per Process

This is often referred to as the “hybrid mode” MPI approach, where multiple machines are requested and multiple processes are requested. MPI will start a parent process or processes on each node, and those in turn will be able to use more than one processor for threaded calculations.

#SBATCH --job-name JOBNAME
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s
Common Job Submission Options
Description Slurm directive (#SBATCH option) Great Lakes Usage
Job name --job-name=<name> --job-name=gljob1
Account --account=<account> --account=test
Queue --partition=<partition_name> --partition=standard

Available partitions: standard (default), gpu (GPU jobs only), largemem (large memory jobs only), vizdebug, standard-oc (on-campus software only)

Wall time limit --time=<dd-hh:mm:ss> --time=01-02:00:00
Node count --nodes=<count> --nodes=2
Process count per node --ntasks-per-node=<count> --ntasks-per-node=1
Minimum memory per processor --mem-per-cpu=<memory> --mem-per-cpu=1000m
Request software license(s) --licenses=<application>@slurmdb:<N> --licenses=stata@slurmdb:1
requests one license for Stata
Request event notification


Note: multiple mail-type requests may be specified in a comma separated list:



Please note that if your job is set to utilize more than one node, make sure your code is MPI enabled in order to run across these nodes. More advanced job submission options can be found in the Slurm User Guide for Great Lakes.

Interactive Jobs

An interactive job is a job that returns a command line prompt (instead of running a script) when the job runs. Interactive jobs are useful when debugging or interacting with an application. The srun command is used to submit an interactive job to Slurm. When the job starts, a command line prompt will appear on one of the compute nodes assigned to the job. From here commands can be executed using the resources allocated on the local node.

[user@gl-login1 ~]$ srun --pty --account=test /bin/bash 
srun: job 309 queued and waiting for resources 
srun: job 309 has been allocated resources 
[user@gl3160 ~]$ hostname 
[user@gl3160 ~]$

Jobs submitted with srun –pty /bin/bash will be assigned the cluster default values of 1 CPU and 1024MB of memory.  The account must also be specified; the job will not run otherwise. If additional resources are required, they can be requested as options to the srun command. The following example job is assigned 2 nodes with 4 CPUS and 4GB of memory each:

[user@gl-login1 ~]$ srun --nodes=2 --account=test --ntasks-per-node=4 --mem-per-cpu=1GB --pty /bin/bash
srun: job 894 queued and waiting for resources
srun: job 894 has been allocated resources
[user@gl3160 ~]$ srun hostname

In the above example srun is used within the job from the first compute node to run a command once for every task in the job on the assigned resources. srun can be used to run on a subset of the resources assigned to the job. See the srun man page for more details.

GPU and Large Memory Jobs

Jobs can request GPUs with the job submission options --partition=gpu and a count option from the table below. All counts can be represented by gputype:number or just a number (default type will be used). Available GPU types can be found with the command sinfo -O gres -p <partition>. GPUs can be requested in both Batch and Interactive jobs.

Description Slurm directive (#SBATCH or srun option) Example
GPUs per node --gpus-per-node=<gputype:number> --gpus-per-node=2 or --gpus-per-node=v100:2
GPUs per job --gpus=<gputype:number> --gpus=2 or --gpus=v100:2
GPUs per socket --gpus-per-socket=<gputype:number> --gpus-per-socket=2 or --gpus-per-socket=v100:2
GPUs per task --gpus-per-task=<gputype:number> --gpus-per-task=2 or --gpus-per-task=v100:2
CPUs required per GPU --cpus-per-gpu=<number>  --cpus-per-gpu=4
Memory per GPU --mem-per-gpu=<number>  --mem-per-gpu=1000m

Jobs can request nodes with large amounts of RAM with --partition=largemem.

Submitting a Job in One Line

If you wish to submit a job without needing a separate script, you can use sbatch --wrap=<command string>.  This will wrap the specified command in a simple “sh” shell script, which is then submitted to the Slurm controller.

Using Local Disk During a Job

During your job, you may write to and read from two temporary locations on the node:

  • /tmp: Two 7200 RPM SATA drives in RAID 0, 3.5 TB per node
  • /tmpssd: Faster solid state drive, 426 GB per node

These folders are local, meaning they are only available to the processes running on that specific node and are not shared across the cluster.  If you need shared space, your /scratch folder may be a better temporary work space.

Keep in mind that these are temporary folders and may be used by others during or after your job. Please try not to completely fill the space so that others can use it, and move or delete your /tmp and /tmpssd files after your work is finished.

Job Status

Most of a job’s specifications can be seen by invoking scontrol show job <jobID>.  More details about the job can be written to a file by using  scontrol write batch_script <jobID> output.txt. If no output file is specified, the script will be written to slurm<jobID>.sh.

A job’s record remains in Slurm’s memory for 30 minutes after it completes.  scontrol show job will return “Invalid job id specified” for a job that completed more than 30 minutes ago.  At that point, one must invoke the sacct command to retrieve the job’s record from the Slurm database.

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Getting Started (Web-based)

1. Get Duo

You must use Duo authentication to log on to the Great Lakes OnDemand web service.  Get more details on the Safe Computing Two-Factor page and enroll here.

2. Get a Great Lakes user login

You must establish a user login on Great Lakes by filling out this form.

3. Connect to Great Lakes OnDemand

You must be on campus or on the VPN to connect to Great Lakes OnDemand.  If you are trying to log in from off campus or using an unauthenticated wireless network such as MGuest, you should install VPN software on your computer.

Once you are on the University network, follow these instructions to connect:

  1. Open your web browser (Firefox, Edge, or Chrome in incognito recommended) and navigate to: (for on-campus restricted software)
  2. Log into cosign using your uniqname and password:
  3. Complete Duo authentication: 
  4. You should now be logged in.

4. Get files

At the top of the page, click “Files” and then “Home Directory”.  A new tab will be created that contains the File Explorer: 

Here you can navigate your home folder.  The buttons do the following:

  • “Go To…”: Navigate to a specified folder
  • “Open in Terminal”: Opens the active folder in a terminal session (new tab)
  • “New File”: Creates a new file in the active folder
  • “New Dir”: Creates a new folder in the active folder
  • “Upload”: Select files from your local machine to upload to the active folder
  • “Show Dotfiles”: Reveals hidden files (usually do not need to be changed)
  • “Show Owner/Mode”: Shows ownership and permission information
  • “View”: Shows file contents inside the current tab
  • “Edit”: Opens a file editor in a new tab
  • “Rename/Move”: Gives a file a new path and/or name
  • “Download”: Downloads the file or folder to your local machine
  • “Copy”: Copies selected files to the clipboard
  • “Paste”: Pastes files from the clipboard
  • “(Un)Select All”: Select or unselect all files/folders
  • “Delete”: Deletes selected files/folders

5. Submit a job

At the top of the home page, click “Jobs” and then “Job Composer”.  A new tab will be created that contains the Job Composer: 

Upon your first visit to this page, you’ll go through a helpful tutorial.  The buttons do the following:

  • “New Job”: Creates a new job…
    • “From Default Template”: Uses system defaults for a bare bones “Hello World” job on the Great Lakes cluster.  Please note that you will still need to specify your account.
    • “From Specified Path”: Creates a job from a specified job script.  See the Slurm User Guide for Great Lakes for information on writing this script.  Some attributes (name, account) can be set here if not set in the script.
    • “From Selected Job”: Creates a new job that is a copy of the selected job.
  • “Edit Files”: Opens a the project folder in a new File Explorer tab, allowing you to edit the files within (see “Get Files” above for File Explorer instructions).
  • “Job Options”: Allows for editing the Name, Cluster, Job Script, and Account fields.
  • “Open Terminal”: Opens a terminal session in a new tab, starting in the project folder.
  • “Submit”: Submits the selected job to the cluster.
  • “Stop”: Stops the selected job if it has been submitted.
  • “Delete”: Delete the selected job.

To view active job information, click “Jobs” and then “Active Jobs” from the home page.

This is a simple guide to get your jobs up and running. For more advanced Slurm features and job scripting information, see the Slurm User Guide for Great Lakes. If you are familiar with using the resource manager Torque, you may find the migrating from Torque to Slurm guide useful.

Interactive Apps

At the top of the home page, click “Interactive Apps” and then select your desired application.

Great Lakes Remote Desktop

Launches an interactive desktop in a new tab (uses noVNC).  Specify your account (usually your PI’s uniqname), hours, memory, cores, and partition (standard, gpu, largemem):

Upon selecting “Launch”, your job will be queued on one of your nodes and shown on the “My Interactive Sessions” screen. As soon as the job’s status is “Running”, you can click on “Launch noVNC in New Tab”:

A remote desktop session will then be opened in a new tab for the requested amount of time.  If you finish early, return to the “My Interactive Sessions” tab and delete the job.


Launches an interactive desktop with MATLAB configured and running in a new tab (uses noVNC).  Specify your desired version, account, hours, and memory (4GB minimum):


Upon selecting “Launch”, your job will be queued on one of your nodes and shown on the “My Interactive Sessions” screen. As soon as the job’s status is “Running”, you can click on “Launch noVNC in New Tab”:

A remote desktop session running MATLAB will then be opened in a new tab for the requested amount of time. You may also use the terminal and other basic applications. If you finish early, return to the “My Interactive Sessions” tab and delete the job.

Jupyter Notebook Server

Launches a Jupyter Notebook Server in a new tab. Specify your desired version, project name, hours, memory, cores, runtime directory, and partition (usually your PI’s uniqname):

Upon selecting “Launch”, your job will be queued on one of your nodes and shown on the “My Interactive Sessions” screen. As soon as the job’s status is “Running”, you can click on “Connect to Jupyter”:

For instructions on using Jupyter Notebook, see the official documentation.

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Software modules

The Great Lakes cluster uses the Lmod modules system to provide access to centrally installed software. If you used a cluster at UM previously, then you should review the documentation for the module system as we have changed the configuration to match that used at most national clusters and most other university clusters.

In particular, you should use the command module keyword to look for a module and do not use module available to search for software, as module available will only show software for which all the dependencies (or prerequisites) are already loaded.

So, to search for the software package FFTW, use

$ module keyword fftw

That will show which versions are installed and provide a command to determine what is needed to load it.

Please see our page on using the Lmod modules system for more details and examples.

There are two main categories of software available on the system: software that is installed as part of the installation of the operating system and software that is installed separately. No special action is needed to use the software installed with the operating system. The separately installed software is set up so that you will use a module to use it. The module will set up the environment and make the software available. We do it this way to enable having multiple versions of the same package and to avoid having conflicts between software packages that have mutually exclusive system requirements.

Requesting software licenses

Many of the software packages that are licensed for use on ARC clusters are licensed for a limited number of concurrent uses. If you will use one of those packages, then you must request a license or licenses in your submission script. As an example, to request one Stata license, you would use

#SBATCH --licenses=stata@slurmdb:1

The list of software licenses available for reservation on Great Lakes can be found by using the command

$ scontrol show licenses

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Partition Policies

Slurm partitions represent collections of nodes for a computational purpose, and are equivalent to Torque queues. For more Great Lakes hardware specifications, see the Configuration page.


  • debug: The goal of debug is to allow users to run jobs quickly for debugging purposes.
    • Maximum jobs per user: 1
    • Maximum walltime: 4 hours
    • Maximum processors per job: 8
    • Maximum memory per job: 40 GB
    • Higher scheduling priority
  • standard: Standard compute nodes used for most work.
    • Max walltime: 14 days
    • Default partition if none specified
  • standard-oc: These nodes will be configured with additional software that can only be used on-campus, but are otherwise identical to standard compute nodes.
    • Max walltime: 14 days
  • gpu: Allows use of NVIDIA Tesla V100 GPUs.
    • Max walltime: 14 days
  • largemem: Allows use of a compute node with 1.5 TB of RAM.
    • Max walltime: 14 days

Account/Association Limits

In order to facilitate fairness between accounts, we have set resource limits on each Great Lakes root account which are described here.

Limits can be set on a Slurm association or on an Slurm account. This allows a PI to limit individual users or the collective set of users in an account as the PI sees fit. The following values can be used to limit either an account or user association, unless noted otherwise below:

Current Great Lakes partition limits:

  • MaxJobs
    • Maximum number of jobs allowed to run at one time
    • Account example: testaccount can have 10 simultaneously running jobs (testuser1 has 8 running jobs and testuser2 has 2 running jobs for a total of 10 running jobs)
    • Association example: testuser can have 2 simultaneously running jobs
  • MaxWall
    • Maximum duration of a job
    • Account example: all users on testaccount can run jobs for up to 3 days
    • Association example: testuser’s jobs can run up to 3 days
  • MaxTRES (CPU, Memory, GPU or billing units)
    • Maximum number of TRES the running jobs can simultaneously use
    • Account example: testaccount’s running jobs can collectively use up to 5 GPUs (testuser1’s jobs are using 3 GPUs and testuser2’s jobs are using 2 GPUs for a total of 5 GPUs)
    • Association example: testuser’s running jobs can collectively use up to 10 cores
  • GrpTRESMins (billing units)
    • The total number of TRES minutes that can possibly be used by past, present and future jobs. This is primarily used for setting spending limits
    • Account example: all users on testaccount share a spending limit of $1000
    • Association example: testuser has a spending limit of $1000
  • GrpTRESRunMins
    • The total number of TRES minutes used by all running jobs. This takes into consideration the time limit of running jobs. If the limit is reached no new jobs are started until other jobs finish.
    • Account example: all users on testaccount share a pool of 1000 CPU minutes for running jobs (users have 10 serial jobs each with 100 minutes remaining to completion)
    • Association example: testuser can have up to 100 CPU minutes of running jobs (1 job with 100 CPU minutes remaining, 2 with 50 minutes remaining, etc.)

Periodic Spending Limits

The PI has the ability to set a monthly or yearly (fiscal year) spending limit on a Slurm account. Spending limits will be updated at the beginning of each month. As an example, if the testaccount account has a monthly spending limit of $1000 and this is used up on January 22nd, jobs will be unable to run until February 1st when the limit will reset with another $1000 to spend.

Please contact ARC-TS if you would like to implement any of these limits.

Billing Policies

A job will be charged based on the percentage of the node it uses (based on CPU, memory, and GPU usage). This is done by using the maximum of the weighted charges for CPU, memory (and GPU if appropriate). If you use 1 core and all the memory of the machine or all the cores and minimal memory, you’ll be charged for the entire machine.  

Multiple shortcodes, up to four, can be used. If more than one shortcode is used, the amount charged to each can be split by percentage.

Great Lakes accounts can be initiated by sending an email to

Terms of Usage and User Responsibility

  1. Data is not backed up. None of the data on Great Lakes is backed up. The data that you keep in your home directory, /tmp or any other filesystem is exposed to immediate and permanent loss at all times. You are responsible for mitigating your own risk. ARC-TS provides more durable storage on Turbo, Locker, and Data Den.  For more information on these, look here.
  2. Your usage is tracked and may be used for reports. We track a lot of job data and store it for a long time. We use this data to generate usage reports and look at patterns and trends. We may report this data, including your individual data, to your adviser, department head, dean, or other administrator or supervisor.
  3. Maintaining the overall stability of the system is paramount to us. While we make every effort to ensure that every job completes with the most efficient and accurate way possible, the stability of the cluster is our primary concern. This may affect you, but mostly we hope it benefits you. System availability is based on our best efforts. We are staffed to provide support during normal business hours. We try very hard to provide support as broadly as possible, but cannot guarantee support on a 24 hours a day basis. Additionally, we perform system maintenance on a periodic basis, driven by the availability of software updates, staffing availability, and input from the user community. We do our best to schedule around your needs, but there will be times when the system is unavailable. For scheduled outages, we will announce them at least one month in advance on the ARC-TS home page; for unscheduled outages we will announce them as quickly as we can with as much detail as we have on that same page. You can also track ARC-TS on Twitter (@ARC-TS ).
  4. Great Lakes is intended only for non-commercial, academic research and instruction. Commercial use of some of the software on Great Lakes is prohibited by software licensing terms. Prohibited uses include product development or validation, any service for which a fee is charged, and, in some cases, research involving proprietary data that will not be made available publicly. Please contact if you have any questions about this policy, or about whether your work may violate these terms.
  5. You are responsible for the security of sensitive codes and data. If you will be storing export-controlled or other sensitive or secure software, libraries, or data on the cluster, it is your responsibility that is is secured to the standards set by the most restrictive governing rules.  We cannot reasonably monitor everything that is installed on the cluster, and cannot be responsible for it, leaving the responsibility with you, the end user.
  6. Data subject to HIPAA regulations may not be stored or processed on the cluster.


Users must manage data appropriately in their various locations:

  • /home
    • 80 GB quota, mounted on Turbo
    • Flux home directories will be mounted read-only on Great Lakes through 2019. Please move any needed data elsewhere during this time.
  • /scratch (more information below)
  • /tmp
  • /tmpssd
  • customer-provided NFS


Every user has a /scratch directory for every Slurm account they are a member of.  Additionally for that account, there is a shared data directory for collaboration with other members of that account.  The account directory group ownership is set using the Slurm account-based UNIX groups, so all files created in the /scratch directory are accessible by any group member, to facilitate collaboration.


File quotas on /scratch are per root account (a PI or project account) and shared between child accounts (individual users):

  • 10 TB storage limit
  • 1 million file (inode) limit

If needed, these limits may be increased by contacting HPC support with an acceptable reason.

Users should keep in mind that /scratch has an auto-purge policy on unaccessed files, which means that any unaccessed data will be automatically deleted by the system after 60 days. Scratch file systems are not backed up. Critical files should be backed up to another location.


Appropriate uses for the Great Lakes login nodes include:

  • Transferring small files to and from the cluster
  • Ordinary data management tasks, such as moving files, creating directories, etc.
  • Creating, modifying, and compiling code and submission scripts
  • Submitting and monitoring the status of jobs
  • Testing executables to ensure they will run on the cluster and its infrastructure.

You should limit your use of Great Lakes login nodes to programs using 4 processors or fewer, less than 16 GB of memory, and that do not run longer than 5 minutes. Larger or longer processes may cause problems for other users of the login nodes. We reserve the right to terminate processes if we think they are causing or may cause a problem. If your program needs to run for more than 5 minutes or more extensive resources, you should use an interactive job.

Any other uses of the login nodes may result in the termination of the process in violation. Any production processes (including post processing) should be submitted through the batch system to the cluster. If interactive use is required then you should submit an interactive job to the cluster.


Applications and data are protected by secure physical facilities and infrastructure as well as a variety of network and security monitoring systems. These systems provide basic but important security measures including:

  • Secure access – All access to Great Lakes is via SSH or Globus. SSH has a long history of high-security.
  • Built-in firewalls – All of the Great Lakes servers have firewalls that restrict access to only what is needed.
  • Unique users – Great Lakes adheres to the University guideline of one person per login ID and one login ID per person.
  • Multi-factor authentication (MFA) – For all interactive sessions, Great Lakes requires both a UM Kerberos password and Duo authentication. File transfer sessions require a Kerberos password.
  • Private subnets – Other than the login and file transfer computers that are part of Great Lakes, all of the computers are on a network that is private within the University network and are unreachable from the Internet.
  • Flexible data storage – Researchers can control the security of their own data storage by securing their storage as they require and having it mounted via NFSv3 or NFSv4 on Great Lakes. Another option is to make use of Great Lakes’ local scratch storage, which is considered secure for many types of data. Note: Great Lakes is not considered secure for data covered by HIPAA.

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Citing and Grants

Researchers are urged to acknowledge ARC in any publication, presentation, report, or proposal on research that involved ARC hardware (Great Lakes or other resources) and/or staff expertise.

“This research was supported in part through computational resources and services provided by Advanced Research Computing at the University of Michigan, Ann Arbor.”

Researchers are asked to annually submit, by October 1, a list of materials that reference ARC, and inform its staff whenever any such research receives professional or press exposure ( This information is extremely important in enabling ARC  to continue supporting U-M researchers and obtain funding for future system and service upgrades.

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Updates and Notices

This section will be updated when system level changes are made to Great Lakes. There are currently no updates.

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