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The easiest and fastest way to access Attic is via Globus. You can transfer files between your computer, our clusters ($HOME and $WORK on Crane, Tusker, and Sandhills), and Attic. Here is a detailed tutorial on how to set up and use Globus Connect. For Attic, use the Globus Endpoint hcc#attic.  Your Attic files are located at ~which is a shortcut for /attic/<groupname>/<username>.
Note: If you are accessing Attic files from your supplementary group, you should explicitly set the path to /attic/<supplementary_groupname>/. If you don't do that, by default the endpoint will try to place you in your primary group's Attic path, which access will be denied if the primary group doesn't have Attic allocation.

Transfer Files Using SCP/SFTP/RSYNC

The transfer server for Attic storage is named


Code Block
titleSFTP example
$ sftp <username>
Duo two-factor login for <username>
Connected to
sftp> pwd
Remote working directory: /attic/<groupname>/<username>
sftp> put source/file destination/file
sftp> exit

Code Block
titleRSYNC Example
# local to remote rsync command
$ rsync -avz /local/source/path <username>

# remote to local rsync command
$ rsync -avz <username> /local/destination/path

You can also access your data on Attic using our three high-speed transfer servers if you prefer. Simply use scp or sftp to connect to one of the transfer servers, and your directory is mounted at /attic/<groupname>/<username>.


The usage and quota information for your group and the users in the group are stored in a filed named "disk_usage.txt" in your group's directory (/attic/<groupname>). You can use either Globus Connect or scp to download it.  Your usage and expiration is also shown in the web interface (see below).

Use the web interface

For convenience, a web interface is also provided.  Simply go to and login with your HCC credentials.  Using this interface, you can see your quota usage and expiration, manage files, etc.  Please note we do not recommend uploading/downloading large files this way.  Use one of the other transfer methods above for large datasets.