Join the ASU Esports Lounge and the Friends of Esports club for our first ever Marvel Rivals tournament! We will be hosting a bracket style event on Friday, January 23rd at 6 p.m. The top 3 teams will receive a Marvel Rivals inspired, 3D printed trophy!
Don't have a team? Don't worry! Our staff will help you find a team on the day of the tournament. Feel free to register as a full 6 stack, a partial team or a free agent. Join us in-person at the ASU Esports Lounge or online!
Join Research Computing for Introducing the Gaudi2 and Demystifying AI Processors.
This workshop teaches beginners on how to utilize the ASU Research Computing supercomputer. This workshop will cover the supercomputer's configuration, batch and interactive access, and available software packages. Access has been greatly simplified with the web portal, a browser-based interface to the supercomputer which supports command-line shell, drag and drop file transfer, job submission, and MATLAB, RStudio, Jupyter, and other applications.
Join Research Computing for Data Transfers with ASU Research Computing.
Join Research Computing for Python for Data Handling, Machine Learning, and Deep Learning.
This workshop will detail low-level approaches, specifically use of OpenACC and Cuda, for accelerating existing or developing research codes with Graphical Processing Units (GPUs) on the ASU High Performance Computing supercomputer.
This workshop will describe different low and high level approaches to accelerating existing or developing research codes through the use of Graphical Processing Units (GPUs) on the ASU High Performance Computing supercomputer.
Join Research Computing for Introduction to the Command Line.
Join Research Computing for AI-Powered Programming with Large Language Models.
This workshop will focus on approaches to porting Matlab applications to a supercomputer environment such as that of ASU's Agave supercomputer. This is not an intro to Matlab course. The intended audience member will have developed Matlab code that runs on a desktop machine but now would like to run this code in a parallel environment. This may be implemented through either: