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

[jtk10001@login6jth10001@login6 ~]$

This would tell us the 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.

[jtk10001@login6jth10001@login6 ~]$ hostname
login6

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.

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                          location=local,skylake,cpuonly
class             up    4:00:00     19  down* cn[244-246,252,254-255,268-271 location=local,haswell,cpuonly
class             up    4:00:00      7  down* cn[335-337,339-340,373,406]    location=local,skylake,cpuonly
class             up    4:00:00      1   comp cn376                          location=local,skylake,cpuonly
class             up    4:00:00     10    mix cn[333,352,362,374-375,391,402 location=local,skylake,cpuonly
class             up    4:00:00     25  alloc cn[329-332,334,338,341-342,345 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)       GRES      STATE     PARTITION           ACTIVE_FEATURES
gpu06     13/23/0/36          gpu:1     mix       priority-gpu        location=local,gpu,v100,skylake
gpu20     36/28/0/64          gpu:3     mix       priority-gpu        location=local,epyc64,a100,gpu
gpu21     25/39/0/64          gpu:3     mix       priority-gpu        location=local,epyc64,a100,gpu
gpu22     3/61/0/64           gpu:3     mix       priority-gpu        location=local,epyc64,a100,gpu
gpu23     33/31/0/64          gpu:1     mix       priority-gpu        location=local,epyc64,a100,gpu
gtx02     2/18/0/20           gpu:3     mix       priority-gpu        location=local,gpu,gtx,skylake
gtx03     12/8/0/20           gpu:3     mix       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 “A” stands for Allocated, I “I” stands for Idle, and T “T” stands for Total. (O “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 my 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.

There are many reasons a job may fail. A good first step is to use the shist command to check the ExitCode SLURM gave it. The command follows this format: shist --starttime YYYY-MM-DD.

Here’s an example of the output for a job that failed immediately with an ExitCode of 1.

JobID         Partition        QOS    JobName      User      State    Elapsed   NNodes      NCPUS        NodeList ExitCode                 End 
------------ ---------- ---------- ---------- --------- ---------- ---------- -------- ---------- --------------- -------- -------------------
73088        priority-+ erm12009g+  submit_rx  jdt10005     FAILED   00:00:00        1         32           gtx21      1:0 2022-11-17T10:05:34 

The ExitCode of a job will be a number between 0 and 255. An ExitCode of 0 means that—as far as SLURM is concerned—the job ran and was completed properly. Any non-zero ExitCode will indicate that the job failed. One could then search the ExitCode on Google to investigate what SLURM thinks caused the job to fail. Sometimes this is helpful but not always. Either way, take note of what you find for future reference.

The next clue to investigate is the NodeList column. Sometimes a job fails because there is something wrong with the compute node our job was run on. If the compute node is the problem (and Storrs HPC staff haven’t fixed it already), the job should fail again with the same ExitCode. We can submit our job specifically to that same node to see if the job fails again. Try adding this to the #SBATCH header of your script to target a specific node. Here, we target gtx21 because that was the node listed in the NodeList column above.

#SBATCH --nodelist=gtx21

Once you see the job has failed multiple times on the same node, then you can feel confident that a faulty node is likely the cause. Please submit a help request to Storrs HPC including a screenshot from the shist output.

Short answer: If you received this error, your job most likely failed because the amount of memory (RAM) it needed was larger than the default. We can request more memory for your job using the --mem or --mem-per-cpu flag.

Long answer: There are several reasons a job may fail due to from insufficient memory. The most likely reason this problem is suddenly affecting you is because that Storrs HPC Admins had to implement a new change to HPC 2.0. We’ll explain why in a moment, but first, let me go over the solution (assuming this is the problem).

As of January 2023, the default amount of memory available per CPU is 2 gigabytes. But you can easily override the default using the --mem-per-cpu flag in your submission script. The new line will look like this:

#SBATCH -n 2                            # asks for 2 cores
#SBATCH --mem-per-cpu=3G                # asks for 3 GB or RAM per core, or 6  GB total

Adding this line will tell SLURM to use 3 gigabytes of RAM per CPU you request. That means if we ask for 2 cores (-n 2), then we’ll be allocated 6 gigabytes of RAM total. Please note that the --mem-per-cpu flag must be used with the -n flag specifying the number of cores you want. Alternatively, users can use the --mem flag to specify the total amount of RAM they need.

#SBATCH --mem=5G                        # asks for 10 gigabytes total

We encourage users to please adjust the --mem-per-cpu or --mem flags in a step-wise fashion. First, we try 3 gigabytes, then 4, then 5, etc. until our jobs start running without failing from memory errors. That strategy helps ensure that every HPC user's jobs get on quickly and run efficiently.

Now, we’ll explain why this problem is suddenly affecting our users. On HPC 1.0, the memory flags didn’t work so there wasn’t a great way to prevent jobs from failing due to insufficient memory. The problem didn’t happen super often though. The memory flags do work on HPC 2.0, and the default settings were a little too strict. SLURM was only letting 1 job run per node because it assumed all jobs needed the entire node’s memory. There were nodes with 128 cores and 500 GB of RAM where only 1 core and 1 gigabyte of RAM being were used. Tons of jobs were piling up in the queue and the job wait times were really long.

So, the Storrs HPC Admins reset the default memory available per core to be 2 gigabytes of RAM per core. We had to set it this low because some of the node architectures have much less memory than others. Resetting this variable enabled SLURM to allow more than one job to run on a node and reduced the job wait times, provided there is enough memory on that node. This default mem-per-cpu of 2 GB is okay for many users, but not for all. Some of our users run more RAM-intensive programs, meaning the default is not sufficient. So, now it has to be adjusted manually in the #SBATCH header. For more info on #SBATCH flags, see this link.

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 on from scratch, we understand that this news can come as a huge blow. We wish that we could backup 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.

There are many reasons this might be occurring, but a common problem with user-installed programs is that the module names and versions have changed slightly between the old HPC and HPC 2.0. It may be that the dependencies your program used to rely on are no longer available on HPC 2.0. For instance, the GCC compilers have been updated and some of the old ones are no longer available. In this case, the ideal situation would be to install your program again using the newer compilers---this is often a good idea anyway because newer compilers sometimes increase the performance and reduce the chance of bugs.

If there are extenuating circumstances that prevent you from using a program with new compilers , or are experiencing other module-related problems, we invite you to submit a request for assistance by emailing hpc@uconn.edu. Then we can discuss options of how to set up a module that meets your needs.

If the script could not run via sbatch. The errors usually looks look like this:

sh: -c: line 0: unexpected EOF while looking for matching `"'
sh: -c: line 1: syntax error: unexpected end of file

It is usually due to the wrong file format. Your file is still in the Windows format but not the Linux format.

$ file comsol.sh # with wrong format
comsol.sh: Little-endian UTF-16 Unicode English text, with CRLF line terminators
$ iconv -f utf-16 -t ascii comsol.sh -o comsol.sh # Convert to ASCII first.
$ dos2unix comsol.sh # change CRLF line terminators to Unix format
$ file comsol.sh
comsol.sh: Bourne-Again shell script text executable