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The node you are on will normally be shown next to your netID when you log in to the Storrs HPC. For instance, if Jonathan the Husky’s netID was jtk10001, his terminal might look like this.

[jth10001@login6 ~]$

This would tell us that Jonathan is on the node called “login6.” Another way to check what node you are on is to use the hostname command. See below.

[jth10001@login6 ~]$ hostname
login6

Programs that are running on a login node (login.storrs.hpc.uconn.edu) without using the job scheduler are subject to certain restrictions. Programs that run on the login nodes for longer than 1 hour, use greater than 5% of CPU power, and/or use greater than 5% of RAM listed below may be throttled or terminated without notice.

Below is a list of programs that are allowed to run on the login node without restrictions:

Short answer: Faculty can purchase priority access for 5 years if they pay the upfront cost for the nodes.

Long answer: High-priority access is available under a “condo model,” where faculty are able to purchase semi-dedicated nodes which get made available to all users when there are unused compute cycles. Under the condo model, faculty researchers fund the capital equipment costs of individual compute nodes, while the university funds the operating costs of running these nodes for five years. Faculty who purchase compute nodes receive access to equivalent resources at a higher priority than other researchers. The faculty can designate others to receive access at the same priority level, such as their graduate students, postdoctoral researchers, etc. With priority access, computational jobs are moved higher in the queuing system, and in most cases begin execution within twelve hours, depending upon other FairShare factors. A priority user can utilize their resources indefinitely. All access to resources is managed through the cluster’s job scheduler. When a priority user is not using their assigned resources, the nodes are made available to all UConn researchers for general use.

Short answer: Email us at hpc@uconn.edu. We will set up an affiliate account so you can access those files.

Long answer: When folks leave the university, their netIDs are deactivated and access to their Storrs HPC resources and files are (typically) transferred to their supervisors. To regain access, Storrs HPC staff need to create an account affiliated with their former supervisor.

Since installing R packages is important to so many HPC users' research, we have created a brief guide on the installation of R packages which is linked here.

We have created a brief guide on the installation of python libraries which can be found here.

Long wait times for your jobs? Errors about unavailable resources? We’ve been there and understand how frustrating it can be for jobs to take a long time to run. It’s an unfortunate consequence of having such a strong computational research community at UConn. LOTS of incredible research happens here, but it also means that there are LOTS of people competing for resources. There’s no getting around that problem, but there are a couple of steps we can take to increase the odds that our jobs get on ASAP.

1. Check what resources are available before we submit a job. (And then)

2. Target our submission to those available resources.

This FAQ will offer guidance on how to do both of those things.

Checking for Available Resources

The below sinfo command will give you a high-level view of what nodes are fully available (“idle”), in partial use (“mix”), not available (“alloc”. for allocated), or otherwise in need of maintenance (all other states besides idle, mix, or alloc). The nodes will be broken down in order by partition.

sinfo -o '%14P %.5a %.10l %.6D %.6t %30N %b

The output for that command will look like this, but it will be much longer and provide info on every partition.

PARTITION      AVAIL  TIMELIMIT  NODES  STATE NODELIST                       ACTIVE_FEATURES
GeoSciMP          up 1-00:00:00     38    mix cn[410-447]                    location=local,epyc64,cpuonly
class             up    4:00:00      3  inval cn[407-409]                    location=local,skylake,cpuonly
class             up    4:00:00      1  maint cn348                    ACTIVE_FEATURES GeoSciMP     location=local,skylake,cpuonly
class    up 1-00:00:00     38   up mix cn[410-447]  4:00:00     19             down* cn[244-246,252,254-255,268-271 location=local,epyc64haswell,cpuonly
class             up    4:00:00      37  invaldown* cn[407-409]      335-337,339-340,373,406]              location=local,skylake,cpuonly
class             up    4:00:00      1  maint comp cn348cn376                          location=local,skylake,cpuonly
class             up    4:00:00     1910    down*mix cn[244-246,252,254-255,268-271333,352,362,374-375,391,402 location=local,haswellskylake,cpuonly
class             up    4:00:00     25 7 alloc down* cn[335-337,339-340,373,406]   329-332,334,338,341-342,345 location=local,skylake,cpuonly
class             up    4:00:00      1   comp cn376... (etc.)

The above command gives us an overarching picture of usage on the cluster, and from there, we can use a more targeted command to get more information on individual nodes within a partition, like how many cores or GPUs are in use and how many are available. The base sinfo command is below and it targets the priority-gpu partition but we could amend it to target any other partition, like hi-core for example.

sinfo -p priority-gpu -t idle,mix -o%10n%20C%10G%10t%20R%b
HOSTNAMES CPUS(A/I/O/T)       GRES      STATE     PARTITION          location=local,skylake,cpuonly
class  ACTIVE_FEATURES
gpu06     13/23/0/36       up    4:00:00gpu:1     10mix    mix cn[333,352,362,374-375,391,402 location=local,skylake,cpuonly
class priority-gpu        location=local,gpu,v100,skylake
gpu20    up 36/28/0/64   4:00:00        gpu:3     mix    25  alloc cn[329-332,334,338,341-342,345priority-gpu        location=local,skylake,cpuonly
... (etc.)

The above command gives us an overarching picture of usage on the cluster, and from there, we can use a more targeted command to get more information on individual nodes within a partition, like how many cores or GPUs are in use and how many are available. The base sinfo command is below and it targets the priority-gpu partition but we could amend it to target any other partition, like hi-core for example.

sinfo -p priority-gpu -t idle,mix -o%10n%20C%10G%10t%20R%b
HOSTNAMES CPUS(A/I/O/T)epyc64,a100,gpu
gpu21     25/39/0/64          gpu:3     mix       priority-gpu        location=local,epyc64,a100,gpu
gpu22     3/61/0/64       GRES    gpu:3  STATE   mix  PARTITION      priority-gpu       ACTIVE_FEATURES
gpu06 location=local,epyc64,a100,gpu
gpu23     1333/2331/0/3664          gpu:1     mix       priority-gpu        location=local,gpuepyc64,v100a100,skylakegpu
gpu20gtx02     362/2818/0/6420           gpu:3     mix       priority-gpu        location=local,epyc64gpu,a100gtx,gpuskylake
gpu21gtx03     2512/398/0/6420           gpu:3     mix       priority-gpu        location=local,epyc64gpu,a100gtx,gpuskylake
gpu22gtx08     31/6119/0/6420           gpu:32     mix       priority-gpu        location=local,epyc64gpu,a100gtx,gpuskylake
gpu23gtx11     332/3118/0/6420           gpu:12     mix       priority-gpu        location=local,epyc64gpu,a100gtx,gpuskylake
gtx02gtx15     228/184/0/2032           gpu:38     mix       priority-gpu        location=local,gpu,gtxrtx,skylake
gtx03gtx09     120/820/0/20           gpu:32     mixidle       priority-gpu        location=local,gpu,gtx,skylake
gtx08     1/19/0/20           gpu:2     mix       priority-gpu        location=local,gpu,gtx,skylake
gtx11     2/18/0/20           gpu:2     mix       priority-gpu        location=local,gpu,gtx,skylake
gtx15     28/4/0/32           gpu:8     mix       priority-gpu        location=local,gpu,rtx,skylake
gtx09     0/20/0/20           gpu:2     idle      priority-gpu        location=local,gpu,gtx,skylake

The column titled “CPUS (A/I/O/T)” tells us how many cores are available. “A” stands for Allocated, “I” stands for Idle, and “T” stands for Total. (“O” stands for Other but you can ignore that column) Since there are 39 cores in the “Idle” column for GPU21, that means 39 cores are available to use. But all 3 of the GPUs on GPU21 are in use so we can’t use any GPUs on that node. So, that gives us an idea of the resources. If I only needed cores and no GPUs, I could target GPU21.

In summary, these two commands can give us a picture of what partitions have resources available, and then what resources are available on individual nodes within that partition.

Targeting a specific partition

The next step is submitting a job targeting a specific partition. If you’re not sure how to target a specific partition, please visit our SLURM Guide where you will see examples of submission scripts that target different partitions and architectures. Another key part of targeting a specific partition is knowing what partitions you are allowed to use and what “account” and “QOS” you must use to access them. To check what partitions you’re allowed to use and how to access them you can use this command.

sacctmgr list assoc user=`whoami` -o format=user,account,partition%20,qos%20
      User    Account            Partition                  QOS 
---------- --

The column titled “CPUS (A/I/O/T)” tells us how many cores are available. “A” stands for Allocated, “I” stands for Idle, and “T” stands for Total. (“O” stands for Other but you can ignore that column) Since there are 39 cores in the “Idle” column for GPU21, that means 39 cores are available to use. But all 3 of the GPUs on GPU21 are in use so we can’t use any GPUs on that node. So, that gives us an idea of the resources. If I only needed cores and no GPUs, I could target GPU21.

In summary, these two commands can give us a picture of what partitions have resources available, and then what resources are available on individual nodes within that partition.

Targeting a specific partition

The next step is submitting a job targeting a specific partition. If you’re not sure how to target a specific partition, please visit our SLURM Guide where you will see examples of submission scripts that target different partitions and architectures. Another key part of targeting a specific partition is knowing what partitions you are allowed to use and what “account” and “QOS” you must use to access them. To check what partitions you’re allowed to use and how to access them you can use this command.

sacctmgr list assoc user=`whoami` -o format=user,account,partition%20,qos%20
      User    Account            Partition                  QOS 
---------- ---------- -------------------- -------------------- 
net10004    jth10001               hi-core              general 
net10004    jth10001           general-gpu              general 
net10004    jth10001                 debug              general 
net10004    jth10001               lo-core              general 
net10004    jth10001               general              general
net10004    jth10001          priority-gpu          jth10001gpu

This tells me that I have access to 6 partitions. To access the priority-gpu partition, I need to include the below three flags in the #SBATCH header of my submission script. This will be different for every individual so you will have to modify this with the partitions you have access to and the account and QOS that are associated with your account.

#SBATCH -p priority-gpu     # partition I want to access
#SBATCH -A jth10001         # account I am associated with
#SBATCH -q jth10001gpu      # QOS needed to access partition

If you have further questions about how to check what resources are available and how to target them, please feel free to contact the Storrs HPC admins by sending an email to hpc@uconn.edu.

Short answer: Possibly, it depends on where the files were stored and how long ago they were deleted. Send us an email at hpc@uconn.edu explaining the situation so we can discuss options.

Long Answer: Files stored in the /shared/ and /home/ directories are backed up daily at 5:00 am and the data is stored for 30 days. If the accidentally deleted files were stored within either of those directories, and you contact us in under 30 days, then there’s a chance we will be able to help you restore those files. Please contact us at hpc@uconn.edu so we can discuss a way forward.

Unfortunately, if the files that were deleted had been stored on /scratch/, we will not be able to restore them. The /scratch/ directory is not backed up ever. If you’re reading this after accidentally deleting files from scratch, we understand that this news can come as a huge blow. We wish that we could back up /scratch/, but we only have enough room to back /shared/ and /home/. We cannot help you fix the current situation, but there are steps we can take to prevent this from happening again. You can follow these links for more info. on HPC data storage, backing up files, or using Globus.

Short answer: One of the login nodes is most likely not working properly. Try to ssh into any of the three login nodes directly.

Long Answer: When you ssh into hpc2.storrs.hpc.uconn.edu, you are directed to one of our three login nodes (login4, login5, or login6). Occasionally, one of these three login nodes will become faulty. If you ssh into the cluster and your account is directed to the faulty node, then you may be given a “Permission denied” error message. If you experience this problem, we recommend you try to ssh directly into one of the login nodes. Here are the three commands one can use to login into our three login nodes. Please replace netID with your own netID.

login4

ssh -Y netID@login4.storrs.hpc.uconn.edu

login5

ssh -Y netID@login5.storrs.hpc.uconn.edu

login6

ssh -Y netID@login6.storrs.hpc.uconn.edu

If one of these allows you to log in but another gives you a permission denied error, then we can be sure that there is something wrong with one of the login nodes. If you have the time, we’ll ask that you please send us a screenshot of the login node which is giving you a problem so that we can tend to any problems on that faulty node. This will help us ensure that this problem is fixed as soon as possible.

If you receive this “Permission denied” error when ssh-ing directly into all three login nodes, then the problem may be with your netID. You may have to reset your netID password which can be done at this link.