Della is intended as a platform for running both parallel and serial production jobs. The system has grown over time and includes groups of nodes using different generations of Intel processor technology.
Some Technical Specifications:
Della is a 4-rack Intel computer cluster, originally acquired through a joint effort of Astrophysics, the Lewis-Sigler Institute for Integrative Genomics, PICSciE and OIT. All nodes are connected via FDR Infiniband high bandwidth low latency network. For more hardware details, see the Hardware Configuration information below.
How to Access the Della Cluster
To use the Della cluster you have to enable your Princeton Linux account, request an account on Della, and then log in through SSH.
Enabling Princeton Linux Account
Della is a Linux cluster. If your Della account is your first Princeton OIT Linux account, then you need to enable your Linux account. If you need help, the process is described in the Knowledge Base article Unix: How do I enable/change the default Unix shell on my account? For more on Unix, you can see Introduction to Unix at Princeton. Once you have access, you should not need to register again unless your account goes unused for more than six months.
Requesting Access to Della
Access to the large clusters like Della is granted on the basis of brief faculty-sponsored proposals (see For large clusters: Submit a proposal or contribute).
If, however, you are part of a research group with a faculty member who has contributed to or has an approved project on Della, that faculty member can sponsor additional users by sending a request to firstname.lastname@example.org. Any non-Princeton user must be sponsored by a Princeton faculty or staff member for a Research Computer User (RCU) account.
Logging into Della
Once you have been granted access to Della, you should be able to SSH into it using the command below.
$ ssh <YourNetID>@della.princeton.edu
For more on how to SSH, see the Knowledge Base article Secure Shell (SSH): Frequently Asked Questions (FAQ).
If you prefer to navigate Della through a graphical user interface rather than the Linux command line, there is also a web portal called MyDella at mydella.princeton.edu which provides access to the cluster through a web browser. This enables easy file transfers and interactive jobs: RStudio, Jupyter, Stata and MATLAB. A VPN is required to access the web portal from off-campus. We recommend using the GlobalProtect VPN service.
How to Use the Della Cluster
Since Della is a Linux system, knowing some basic Linux commands is highly recommended. For an introduction to navigating a Linux system, view the material associated with our Intro to Linux Command Line workshop.
Using Della also requires some knowledge on how to properly use the file system, module system, and how to use the scheduler that handles each user's jobs. For an introduction to navigating Princeton's High Performance Computing systems, view the material associated with our Getting Started with the Research Computing Clusters workshop. Additional information specific to Della's file system, priority for job scheduling, etc. can be found below.
The head node, della5, should be used for interactive work only, such as compiling programs, and submitting jobs as described below. No jobs should be run on the head node, other than brief tests that last no more than a few minutes. Where practical, we ask that you entirely fill the nodes so that CPU core fragmentation is minimized.
If you'd like to run a Jupyter notebook, we have a few options for running Jupyter notebooks so that you can avoid running on Della's head node.
Wording of Acknowledgement of Support and/or Use of Research Computing Resources
"The author(s) are pleased to acknowledge that the work reported on in this paper was substantially performed using the Princeton Research Computing resources at Princeton University which is consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology's Research Computing."
"The simulations presented in this article were performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology's High Performance Computing Center and Visualization Laboratory at Princeton University."