The vast majority of the calculations we run are carried out on supercomputers, including those at Princeton. You should bookmark the Princeton Research Computing Knowledge Base , which has many excellent guides for using the high-performance computing (HPC) clusters. The most important rule is to treat the research computing staff with respect. Abide by the rules, when reporting issues do so with a , and be both kind and thankful. They are our colleagues, and you should treat them as such even if you have not met them before in person. Anyone found being rude or disrespectful to a computing staff member will have their compute access temporarily revoked.
A list of the various compute resources we currently use in the group is shown below:
- Tiger : The main on-campus cluster we use, which is meant for parallel jobs that use at least one full node. Serial calculations and calculations using less than a full node should not be run on Tiger unless multiple are packed within a single job. Tiger is especially useful for running "wide" jobs where many CPU nodes are used — such jobs have priority on the machine.
- At the time of writing, we have contributed 13 CPU nodes (2x Intel 8480s w/ 112 cores per node and 1 TB memory) to Tiger3, which increases our priority.
- We also have a dedicated login node at
<NetID>@tiger-arrk.princeton.edu - Della : A general-purpose, mixed CPU/GPU cluster. You should mainly use this for GPU codes of any size and for CPU calculations using less than a full node (e.g. serial jobs).
- Neuronic : A small GPU cluster for members of SEAS.
- Adroit : A cluster meant for debugging and testing purposes. If you're developing new software and running simple tests, this can be a useful resource to do rapid prototyping. Production calculations are never run here. Adroit is where our production tests for
quacc are run.
Some on-campus resources are don't currently have access to but likely can if we push for it is shown below:
- Stellar : A PPPL-managed cluster we can access by submitting a brief proposal .
- AI Lab Cluster : A GPU cluster currently run by Princeton Language and Intelligence (PLI) but that will become part of the AI Lab.
If you ever feel resource-constrained in the group, let Andrew know so that the problem can be resolved. In addition to national computing allocations mentioned above, the group also has dedicated funds that can be used to contribute to the Princeton HPC resources, thereby increasing our priority in the queue.