Our Mission
UA High Performance Computing (HPC) is an interdisciplinary research center focused on facilitating research and discoveries that advance science and technology. We deploy and operate advanced computing and data resources for the research activities of students, faculty, and staff at the University of Arizona. We also provide consulting, technical documentation, and training to support our users.
This site is divided into sections that describe the High Performance Computing (HPC) resources that are available, how to use them, and the rules for use.
Highlighted Research
Faster Speeds Need Faster Computation - Hypersonic Travel
Quick News
We only keep a reasonably current version of Singularity. Prior versions have been removed. Only the latest one is considered secure. Singularity is installed on all of the system's compute nodes and can be accessed without using a module. Singularity will be renamed Apptainer as the project is brought into the Linux Foundation. An alias will be created so you can continue to invoke "singularity". | |
Anaconda is very popular and is available as a module. It expands the capability of Jupyter with Jupyter Labs; includes RStudio, and the Conda ecosystem. To access GUI interfaces available through Conda (e.g., JupyerLab), we recommend using an Open OnDemand Desktop session. See these instructions. As a note, Anaconda likes to own your entire environment. Review those instructions to see what problems that can cause and how to address them. | |
Have you tried Puma yet? Our latest supercomputer is larger, faster and has bigger teeth than Ocelote (ok, maybe not the last bit). Puma Quick Start Since we upgraded Ocelote it has the same software suite as Puma. It is generally not as busy as Puma. So if your work does not need the capabilities of Puma, consider using Ocelote instead. This applies to GPU's also, if the P100s will work for you. Now that we are into the second year of use, we have determined that we can increase the standard allocation. From the end of April 2022 the standard allocation of cpu hours is increased from 70,000 to 100,000. |