/category/education
Instructional Use of Rivanna
Instructors can request instructional allocations on Rivanna 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 use a dedicated instructional partition. The standard 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. Instructional allocations come with 1TB of temporary project storage space. Data kept in the temporary project storage space will be automatically purged 2 weeks after the class ends unless the instructor requests an extension.
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.
Upcoming Workshops DATE WORKSHOP INSTRUCTOR Jun 3, 2024
Introduction to the Command Line for HPCPaul Orndorff, Hana Parece Jun 3, 2024
High Performance PythonCamden Duy, Katherine Holcomb Jun 4, 2024
Introduction to GPU Accelerated PythonAngela Boakye Danquah, Hana Parece Jun 5, 2024
Introduction to Deep Learning with HPCAngela Boakye Danquah, Kathryn Linehan Jun 6, 2024
education,
workshops
bioinformatics,
containers,
HPC,
image processing,
Ivy,
Matlab,
programming,
Python,
R,
Rivanna,
Shiny
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
