CRC Workshops Fall 2022
Fall workshops will be offered in a hybrid format, both in-person and online. Seating for in-person workshops is limited to 12 people.
Anyone who can't be accomodated in-person will receive a link via email to attend online.
In-person workshops will be at CRC's conference room at:
311A Schenley Place
4420 Bayard St.
Register for all workshops at: CRC Fall 2022 Workshop Registration
If you run into issues while interacting with content associated with the workshops, please submit a ticket and the CRC Team will assist.
Thursday Sept. 8, 1-4 pm
Prerequisite: A CRC account. Teachers: Kim Wong, Research Associate Professor; Nick Comeau, Research Computing Specialist
Version Control with Git and Data Management Perspectives
Thursday Sept. 29, 1-4 pm
An overview of modern software development workflows using the git version control software and GitHub.com. This workshop is designed to help attendees hit the ground running in collaborative software development projects.
Tearchers: Daniel Perrefort, Research Assistant Professor; Dominic Bordelon, Research Data Librarian
Workshop Video Recording (Panopto)
Density Functional Theory Calculations
Thursday, Oct. 13 1-4 pm
We will provide an introduction to the use of density functional theory (DFT) in electronic structure calculations on extended systems. The workshop will focus on the foundations, approximations, strengths and limitations of DFT. We will also give an overview of existing DFT software packages and their features, and provide practical guidelines on how to select and use efficiently these codes on the CRC cluster. Teacher: Leonardo Bernasconi, Research Assistant Professor
Introduction to Scientific Programming on GPUs
Thursday, Oct 20 1-4 pm
This workshop gives a hands-on introduction to writing codes that leverage the capabilities of GPU accelerators, mainly focusing on applications in numerical methods and scientific computing such as solving PDEs. We will use CUDA/C++ and compile/run practical examples on the HPC cluster. Teacher: Chengnian (Cheng) Xiao, Engineering HPC Consultant.
Workshop Video Recording (Panopto)
PDF Version of Slides
Friday, Nov. 4, 1-4 pm
In this whirlwind overview we will cover the basics of machine learning and deep learning using the Python packages sklearn and pytorch. We will go over several popular machine learning models and apply them in hands on exercises. We will construct and train a simple neural network with pytorch and discuss some of the advanced capabilities of deep neural networks. Teacher: David Koes, Associate Professor, Computational & Systems Biology