/category/education

  • Instructional Use of High Performance Computing

     Instructors can request instructional allocations on Rivanna and Afton for classes and extended workshops. These allocations are time-limited and generally allow access to a restricted set of nodes and only one special Slurm partition, but are otherwise equivalent to any allocation.
    Resource Availability Hardware and Partition Instructional allocations may use interactive partition. The instructional allocation is 100,000 SUs for the semester during which the course is conducted. For workshops, the allocation will persist during the workshop and for two days afterwards. RC offers several low-cost storage options to researchers, including 10TB of Research Standard storage for each eligible PI at no charge.
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  • Workshops

     UVA Research Computing provides training opportunities covering a variety of data analysis, basic programming and computational topics. All of the classes listed below are taught by experts and are freely available to UVa faculty, staff and students.
    New to High-Performance Computing? We offer orientation sessions to introduce you to the Afton & Rivanna HPC systems on Wednesdays (appointment required).
    – Wednesdays 3:00-4:00pm Sign up for an “Intro to HPC” session Upcoming Workshops DATE WORKSHOP INSTRUCTOR There are currently no training events scheduled. Please check back soon! Research Computing is partnering with the Research Library and the Health Sciences Library to deliver workshops covering a variety of research computing topics.
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  • Courses

     In addition to providing free, in-person workshop training, UVA Research Computing staff teach for-credit courses. Below is a selection of courses that members of our group have taught, co-taught or provided guest lectures:
    BIMS 8382: Introduction to Biomedical Data Science Spring 2017, Spring 2018
    This course introduces methods, tools, and software for reproducibly managing, manipulating, analyzing, and visualizing large-scale biomedical data. Specifically, the course introduces the R statistical computing environment and packages for manipulating and visualizing high-dimensional data, covers strategies for reproducible research, and culminates with analysis of data from a real RNA-seq experiment using R and Bioconductor packages.
    CS 6501: Distributed & Cloud Computing Spring 2017, Spring 2018
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