FAQ
chrisreidy@arizona.edu
sarawillis@arizona.edu
ric@arizona.edu
Account Access
- You haven't created an account yet.
- Your account isn't sponsored yet.
- You aren't using two-factor authentication (NetID+).
- You need to wait 15 minutes. If you just created your account, it takes time before you can log in.
- You're trying to connect using ssh NetID@login.hpc.arizona.edu. This will not work. Instead, use: ssh NetID@hpc.arizona.edu.
- You're using NetID@hpc.arizona.edu or NetID@email.arizona.edu as your username in PuTTY. Instead, use only your NetID.
- You've entered your password incorrectly too many times. After multiple failed password entries, the system will place a 60 minute ban on your account for security reasons.

This error appears when you have entered your password incorrectly too many times. A security lock is automatically put in place for 60 minutes. After multiple failed password entries, the system will place a 60 minute ban on your account for security reasons. If you wait until 60 minutes has elapsed, you can try to log in again.
This specific error shows up when your NetID has been locked, usually due to multiple failed login attempts when trying to access university services. Contact 24/7 to unlock your account: https://it.arizona.edu/get-support
- Ensure you are using the correct password. Sometimes typing your password into a plain text file and copying/pasting it into the terminal can help.
- You need to wait about 15 minutes after your account is approved for the account to be available
- You must enroll in NetId. Depending on the application you use to log in, you may not get the typical NetID+/DUO menu of options, or an error message indicating this is your problem
HPC uses the same NetID login credentials as all UA services. If you need to reset your NetID password you can do so using the NetID portal: https://netid-portal.iam.arizona.edu/
Faculty members who manage their own HPC groups can follow the instructions in our Research and Class Groups page.
Yes, if you are a former university affiliate or campus collaborator participating in research, you may register as a Designated Campus Colleague (DCC). Once your DCC status has been approved, you will receive a NetID+ which you may use to create an HPC Account. If you already have an HPC Account, no further action is required.
General Computing
When you log into HPC, the variable $COMMAND_PROMPT
is set to your current cluster (e.g.: (puma)). Sometimes this can cause formatting problems. If you'd prefer to modify your $PS1
, you can add the following to your ~/.bashrc:
if [ -n "${PROMPT_COMMAND}" -a -r /usr/local/bin/slurm-selector.sh ]; then SavePS1=${PS1} Cur_Cluster=$(eval ${PROMPT_COMMAND} 2>/dev/null) PS1="${Cur_Cluster}${SavePS1}" unset PROMPT_COMMAND for c in puma ocelote elgato; do alias ${c}="PS1=\"(${c}) ${SavePS1}\"; . /usr/local/bin/slurm-selector.sh ${c}; unset PROMPT_COMMAND" done unset Cur_Cluster SavePS1 fi
These files are called core dumps and may be created when a program terminates abnormally. Core dumps contain the system's memory at the time of the fault event as well as additional information that can be used with a debugger (e.g. gdb) to track down the source of the error.
One drawback to core dump files is they can be quite large. If you're working with limited space (e.g., your home directory or a nearly-full /groups or /xdisk) or are running many jobs, you may consider disabling them. To do this, include the following line in your batch script before your program's execution:
ulimit -c 0
Open OnDemand
This is most commonly seen with users who have Anaconda or Miniconda initialized in their accounts. For a permanent solution, you can run the following command from an interactive terminal session:
conda config --set auto_activate_base false
This will prevent conda from auto-activating when you first log in and allow you to have more control over your environment. When you'd like to activate anaconda, run conda activate
. See this example for information running anaconda workflows in batch with auto-activation disabled.
If you are trying to log in to Open Ondemand and are seeing the following:
- You have not yet been sponsored by a faculty member. See our Account Creation page for instructions on getting registered for HPC.
- If you are already registered for HPC, this may be a browser issue. Try logging in again in an incognito session or different browser to test. If this succeeds, clearing your browser's cookies should help.
If you are trying to log in to Open Ondemand and are seeing the following:
this may be a browser issue. Try logging in again in an incognito session or different browser to test. If this succeeds, clearing your browser's cache should help.
If you're trying to log into Open OnDemand but are getting an ambiguous error that looks something like the following:
check your storage usage in your home directory. You can do this by logging into HPC in a terminal session and using the command uquota
. If your storage usage is >50GB, OnDemand cannot create the temporary files necessary to give access to the website. Try clearing out some space in your home and then logging back into OnDemand.
Jobs and Scheduling
There are a few reasons your job may not be running, check below for some ideas on diagnosing the issue:
- Run
squeue --job <jobid>
and see if there is anything listed under "(REASON)". This may give an idea why your job is stuck in queue. We have a table in our SLURM documentation that describes what each Reason code means. - Due to the number of HPC users, it may not always be possible to run a submitted job immediately. If there are insufficient resources available, your job will be queued and it may take up to a few hours for it to begin executing.
- Your group may have run out of standard hours. You can check your allocation using the command
va
. - Your group/job has reached a resource usage limit (e.g., number of GPUs that may be used concurrently by a group, or a job has requested more than the 10 day max walltime). Try running
job-limits <group_name>
to see what limits you're subject to and if there are any problem jobs listed. - You may be requesting a rare resource (e.g., 4 GPUs on a single node on Puma or a high memory node).
- If you are requesting a single GPU on Puma and are frustrated with the wait times, you might consider checking if Ocelote will work for your analyses. There are more GPU nodes available on that cluster with shorter wait times.
- If you are trying to run a job on a standard node and have been waiting for a very long time, try checking its status using
job-history <jobid>
. If you see Allocated RAM/CPU above 5gb on Puma or above 6gb on Ocelote, then you are queued for the high memory node which can have very long wait times. To queue for a standard node, cancel your job and check that your script has the correct ratios.
If your job is in queue, sometimes SLURM will give you information on why it's not running. This may be for a number of reasons, for example there may be an upcoming maintenance cycle, your group's allocation may be exhausted, you may have requested resources that surpass system limits, or the node type you've requested may be very busy running jobs. We have a list of reason codes in our Running Jobs With SLURM page that will give more comprehensive information on what these messages mean. If you don't see the reason code listed, contact our consultants.
va
. To see more information on your allotted hours and the different job queues, see: Allocation and Limits.
Specific Errors
This is most commonly seen with users who have Anaconda or Miniconda initialized in their accounts. For a permanent solution, you can run the following command from an interactive terminal session:
conda config --set auto_activate_base false
This will prevent conda from auto-activating when you first log in and allow you to have more control over your environment. When you'd like to activate anaconda, run conda activate
. See this example for information running anaconda workflows in batch with auto-activation disabled.
Scripts created in a Windows environment and transferred to HPC retain hidden carriage returns (^M). You can convert your Windows file to Unix format with:
$ dos2unix <filename>
This happens most frequently for new users. It takes a while to propagate new accounts to all the right places. Come back after a coffee break. However, this can occur in other circumstances. Open a support ticket with hpc-consult.
Add this variable definition before executing the application:
export CUDA_FORCE_PTX_JIT=1
Software and Modules
Yes, when you start an interactive terminal session or submit a batch script, the modules ohpc, gnu8, openmpi3, and cmake are automatically loaded. If your code uses Intel compilers, you will want to manually unload gnu8 and openmpi3 to prevent conflicts.
The exception: If you are working in a terminal in an Open OnDemand interactive desktop session, nothing is loaded by default and you will need to manually load any necessary modules.
R installations can sometimes be frustrating. We have instructions for how to set up a usable R environment, how to diagnose and troubleshoot problems, and steps to help with known troublesome packages documented in in our Using and Customizing R Packages section.
You may also want to take a look under our Popular Packages section in the page mentioned above. This includes instructions for packages that are known to cause issues for users.
Load the module, find the path to the executable by checking the $PATH variable, then list the contents. For example:
module load lammps echo $PATH ls /opt/ohpc/pub/apps/lammps/3Mar20/bin lmp_mpi
There are a few different possibilities:
- You are not in an interactive session. Modules are not available on the login nodes. You may request an interactive session by using the command interactive.
- Your shell is not set to bash. If this is the case, contact our consultants so that they can reset it for you.
You have modified or deleted your ~/.bashrc. If this is the case, open (if the file exists) or create and open (if the file is missing) the file .bashrc in your home directory and add the lines:
if [ -f /etc/bashrc ]; then . /etc/bashrc fi
If you are able to compile your software you can take advantage of most of the AMD Zen architecture.
Compiler | Arch-Specific | Arch-Favorable |
---|---|---|
GCC 9 | -march=znver2 | -mtune=znver2 |
LLVM 9 | -march=znver2 | -mtune=znver2 |
Neither of these compiler versions (GCC 9 or LLVM 9) is available on Puma so you will have to build that first. If you use GCC 8.3 you can set znver1 instead.
Unfortunately, Windows applications can't be run on HPC. However, AWS has been used successfully for Windows software with GPU needs. It’s easy to set up, cost effective, and very scalable. Amazon also has a cloud credit for research program available
https://aws.amazon.com/government-education/research-and-technical-computing/cloud-credit-for-research/ [aws.amazon.com]
You may also consider trying Jetstream2, a national resource where you can create and use Windows virtual machines. More information can be found here: https://jetstream-cloud.org/
Intel compilers are optimized for Intel processors. There is some debate around the concept of unfair CPU dispatching in Intel compilers. By default, software on the HPC clusters is built with GCC (on Puma it is GCC 8.3). This is in keeping with our preference for community software.
You need to belong to a special group called g03
. You can request to be added by submitting a help ticket. This is a constraint in Gaussian that other modules do not have.
Ansys has the Distributed capability built in to increase performance. Ansys uses the Intel compiler and so uses Intel MPI. By default, we load OpenMPI, so you will need to do this:
module unload gnu8 openmpi3 module load intel module load ansys
Instructions on accessing custom packages are under Accessing Custom Packages from a Jupyter Session in our documentation on Using Python & Python Packages.
Data Storage and Transfer
After creating your HPC Account, your home directory will not be created until you log in for the first time. Without your home directory, you will not be able to transfer your data to HPC. If you are struggling and receiving errors, sign into your account either using the CLI through the bastion or logging into OnDemand and then try again.
If you are using something like SCP and are receiving errors, make sure your hostname is set to filexfer.hpc.arizona.edu (not hpc.arizona.edu).
Unfortunately, no. Backups are not made and anything deleted is permanently erased. It is impossible for us to recover it. To ensure your data are safe, we recommend:
- Make frequent backups, ideally in three places and two formats. Helpful information on making backups can be found on our page Transferring Data.
- Use rm and rm -r with caution as these commands cannot be undone! Consider using rm -i when removing files/directories. The -i flag will prompt you to manually confirm file removals to make really sure they can be deleted.
- You can open a support ticket to request assistance. Files that are deleted may not have been removed from the storage array immediately (though this is not guaranteed), don't wait more than a few days.
Endpoint too busy: This is most commonly seen when users are transferring directories to Google Drive. This is because Google has user limits restricting the number of files that can be transferred per unit time. When many files are being transferred at once, that limit may be exceeded. Globus will automatically hold the transfer until the limit is reset at which point it will continue. One way to avoid this is to archive your work prior to the transfer (e.g. in .tar.gz form). Additionally, archiving will also speed up your transfers considerably, sometimes by orders of magnitude.
Fatal FTP Response, PATH_EXISTS: Globus is finicky about the destination endpoint. If you get this error, check to see whether duplicate files/directories exist at the destination. This can happen frequently with Google Drive as multiple files/directories can exist in the same location with the same name. If duplicates exist, try moving, removing, or renaming them and reinitiate the transfer.
In our last maintenance update on July 20th, one of the changes was to ensure HIPAA compliance on the Data Transfer Nodes (DTNs). This change included the insertion of required text:
Authorized uses only. All activity may be monitored and reported.
This change breaks SCP activity. Not in all cases but frequently with WinSCP, Filezilla and from a terminal. Terminal activity will likely still work from Linux or MacOS.
The solution is to not use SCP (SCP is considered outdated, inflexible, and not readily fixed) and to use a more modern protocol like SFTP and rsync. Info on using SFTP:
- Putty supports SFTP with the “PSFTP” command
- For FileZilla, in the Toolbar, click on Edit and Settings, then click on SFTP
- For Cyberduck, choose SFTP in the dropdown for protocols.
Choose SFTP (SSH File Transfer Protocol), and use filexfer.hpc.arizona.edu as the Server. May enter Username and Password, and unselect “Add to KeyChain”.
For Mac, Cyberduck can be used. For version 8.4.2, select Go then Disconnect before clicking on Quit Cyberduck.
The recent Cyberduck patch 8.4.4 has created problems with two-factor authentication which is required to access filexfer.hpc.arizona.edu. Adding a SSH Keys is suggested.
For Windows, WinSCP and FileZilla are recommended.
If your home directory is full and you can't find what is taking up all the space, it's possible the culprit is a hidden file or directory. Hidden objects are used for storing libraries, cached singularity/apptainer objects, saved R session, anaconda environments, configuration files, and more. It's important to be careful with hidden, or "dot", files since they often control your environment and modifying them can lead to unintended consequences.
To view the sizes of all the objects (including hidden) in your home, one quick command is du -hs $(ls -A ~)
, for example:
[netid@junonia ~]$ du -hs $(ls -A ~) 32K Archives 192M bin 4.7G Software 46M .anaconda 1.9M .ansys 4.0K .apptainer 16K .bash_history 4.0K .bash_logout 4.0K .bash_profile 12K .bashrc 20M ondemand
Clearing out unwanted objects, moving data to a location with more space (e.g. /groups or /xdisk), and setting different defaults for data storage (e.g., resetting your apptainer cache directory or setting new working directories for Python/R) can help manage your home's space.
Unfortunately, without active university credentials it is not possible to access HPC compute or storage resources. External collaborates who need ongoing access may apply for Designated Campus Colleague, or DCC, status. This is a process done through HR and will give the applicant active university credentials allowing them to