High Performance Computing (HPC)

Revision as of 09:56, 10 October 2019 by Amy Yuen (talk | contribs)


High Performance Computing (HPC) is the aggregation of computing power and memory to perform complex calculations in parallel, increasing the speed and efficiency of computer simulations and data analysis. In 2018, a collaboration of faculty in the social and natural sciences and ITS staff successfully secured a $150,000 grant from the National Science Foundation to build Middlebury's first HPC cluster. Dubbed "Ada" in honor of Ada Lovelace, the famed 19th century mathematician, the cluster is a tool intended to support the research efforts of faculty who rely on access to expanded computing resources. We continue to add to our collaboration as resources become available.

This wiki describes the cluster structure and how to use it. The cluster is a shared resource, so we use queuing software (called Slurm) to manage job processing and to ensure fair access. Below are basic instructions for logging in to the cluster, accessing the queue and writing scripts to work efficiently and within best practices for a shared computing resource.

Cluster users must include an acknowledgement of NSF funding in any published research, as quoted below:

"This material is based upon work supported by the National Science Foundation under Grant No. 1827373.”

Please email the principal investigator, Professor Amy Yuen, with publication information for grant reporting purposes.



The HPC cluster consists of 17 computer nodes with a cumulative total of 556 processors. It includes 14 nodes with 96GB of RAM each and one additional node with 768GB of RAM. In addition, the HPC cluster has a dedicated graphics processing unit (GPU) with 96GB of RAM, along with a storage node with 60TB of hard drive storage.



  • R
  • Stata




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