HCC Newsletter - May 2025

Common Retirement Reminder

As a quick reminder, the $COMMON filesystem is retiring on July 1st, 2025. Any data that has not been transferred or backed up before July 1st, 2025 will be permanently lost. Please note that HCC will not transfer data from $COMMON automatically, and every HCC user with data in $COMMON is responsible for data backup-up and relocation. However, HCC would be more than happy to provide guidance with the process if needed.

NRDStor, HCC's newest filesystem, provides a good substitute for the $COMMON filesystem. NRDStor is available both from Swan and any computer connected to the NU Campus and is free to use with an initial 50TB quota per group. As with $COMMON before it, NRDStor is maintained with our best effort toward data integrity, but we do not provide backups.

More information can be found on the Common Retirement FAQ Page

Upcoming Downtime

On Tuesday May 27th, HCC will be conducting maintenance and updates on all HCC resources. This downtime will last approximately 3 days with HCC resources returning to service on May 30th. For more details on the maintenance, please visit https://status.hcc.unl.edu/maintenance/6cff24b9-167b-4566-b2e0-5900b58defc4 .

Any jobs on Swan that can not start and finish by May 27th will be held in queue. More information on this is available in our maintenance FAQ section.

https://hcc.unl.edu/docs/faq/#why-my-job-is-showing-reqnodenotavail-reserved-for-maintenance-before-a-downtime

Student Class Data Reminder

With the end of the semester approaching, HCC would like to remind students using HCC for classwork in a class group should backup any data from the group folder they wish to keep. Class group directories will be cleaned up on the last day of the semester, May 16th.

More details on this are available in the Class Group section of HCC’s Policies.

AlphaFold 3 is now available!

AlphaFold and AlphaFold 3 are transforming the understanding of the biological world and drug discovery.

You can now accurately predict the structure and interactions of proteins, DNA, RNA and more using the updated and improved AlphaFold 3 model on HCC Swan by utilizing CPU and GPU nodes.

AlphaFold is AI model developed by Google DeepMind used to generate structure predictions of complexes such as proteins, nucleic acids, ligands. AlphaFold 3 is a substantially updated and improved model capable of predicting the joint structure of biomolecules with very high accuracy.

In addition to AlphaFold, Alphafold 3 and its models and databases are now available for use on HCC Swan for all HCC researchers from University of Nebraska.

To request access to AlphaFold 3 on Swan, please check the access requirements and complete the steps listed in the page below:

https://hcc.unl.edu/request-alphafold3-access

Upcoming Training Opportunities!

On May 27th, HCC will be hosting a webinar on utilizing the National Research Platform for classroom use. We will cover what the National Research Platform is, how it can be used in classrooms as a computational resource for students’ coursework, and a demonstration of starting and customizing a JupyterHub instance for use in a classroom. Registration will open in early May.

HCC will be hosting its annual June Workshop Series every Thursday in the month of June. Keep an eye on your email for the announcement if you are interested.

Looking to reduce your wait time on Swan?

HCC wants to hear more about your research! If you acknowledge HCC in your publications, posters, or journal articles, you can receive a boost in priority on Swan!

Checkout the links below for more information!

Submit a request for the priority boost

https://hcc.unl.edu/acknowledgement-submission

Details on Acknowledgement Credits

https://hcc.unl.edu/docs/submitting_jobs/hcc_acknowledgment_credit/

HCC Summary for the last Month:

CPU Hours Utilized

GPU Hours Utilized

New Software Installs

Data Stored

6.9 Million Hours

79,400 Hours

16 Software Packages

~5,600 TiB

Training Events in the last month:

Introduction to Artificial Intelligence and Machine Learning using HCC