# . {% hint style="danger" %} **WARNING: These are the archives, not current information** These pages contain information about Legacy, a USD supercomputer that is no longer available, and other information that is no longer in use. It is kept as a reference for the maintenance of records, and in case of future need. {% endhint %} The Legacy Supercomputer was acquired in 2006 through USD’s [Institutional Development Award](https://www.nigms.nih.gov/Research/CRCB/IDeA/Pages/inbre_map.aspx)from the National Institutes of Health. It was expanded in 2009 and 2011 with additional NIH funding, and the last expansion in 2013 was funded by the college of Arts and Sciences. Legacy's user base has grown from a small cohort of bioinformatics faculty in 2006 to virtually all Science Technology Engineering and Mathematics disciplines with emerging use cases in the humanities. Legacy is named after the 'Legacy'[ sculpture](http://www.usd.edu/fine-arts/art/success-stories) on USD's Vermillion campus. Legacy runs the CentOS Linux operating system and is made up of 680 AMD Opteron CPU cores with 70TB of shared network storage. The hardwear specifications for Legacy vary by node and are as follows: ## Overview 680 Processing cores, 29 compute nodes, 1 login node, 1 management node ## Processors 4-core, 12-core, and 16-core AMD Opteron ## RAM 32GB - 128GB per node ## Storage 133GB -890GB local scratch disk per node 70TB centrally-shared network storage ## Network Dedicated gigabit Ethernet compute and storage networks ## Details **Compute nodes 16 through 23** Each node contains: 2x Quad Core AMD Opteron 2356 processors (8 total cores) 32GB RAM 133GB local scratch disk **Compute nodes 24 through 30** Each node contains: 2x 12-Core AMD Opteron 6172 processors (24 total cores) 96GB RAM 890GB local scratch disk **Compute nodes 31 through 44** Each node contains: 2x 16-Core AMD Opteron 6272 processors (32 total cores) 128GB RAM 890GB local scratch disk --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://usdrcg.gitbook.io/docs/practice/about-legacy.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.