Courses

Last Updated: March 15, 2024

Every semester, Texas A&M High Performance Research Computing (HPRC) offers short courses ranging in topics for beginning, intermediate, and advanced researchers. The semesters start with hour long primer courses. These primers cover material that is prerequisite to ALL other short courses. These, along with courses on the clusters and schedulers form the basis for using the respective clusters effectively. All courses will be delivered in an interactive style through a live login session. In general, slides and other supplemental materials are available on each course page.

Registration is required for each primer or short course. Attendees will need to use their own device. Workstations are not provided. The typical short course runs for 2.5 hours, unless otherwise noted. Each primer runs for 1 hour.

In-class short courses have a seating limit of about 45 students.

For our course offerings from previous semesters, please consult this page.

Short Course List for Spring 2024

ACES: Fundamentals of Containers

Instructor: Richard Lawrence

Time: Tuesday, March 19, 2024 — 10:00AM-12:30PM CT

Location: Online using Zoom

Description: This course introduces concepts of containers and covers common containerization tasks using the Charliecloud and Singularity container engines on the ACES cluster, a composable accelerator testbed at Texas A&M University.

Prerequisites: Current ACCESS ID, basic Linux/Unix skills

View Details Remote Attendee Registration

ACES: AI/ML on Intel PVC GPUs

Instructor: Zhenhua He, Richard Lawrence

Time: Tuesday, March 19, 2024 — 1:30PM-4:00PM CT

Location: online using Zoom

Description: This course provides an overview of Intel PVC GPUs, guidance on accessing these GPUs on the ACES cluster at Texas A&M High Performance Research Computing, and demonstrations of running AI/ML models with the GPUs using PyTorch and Tensorflow.

Prerequisites: Current ACCESS ID; basic Linux/Unix skills; basic understanding of machine learning concepts, neural networks, and deep learning; familiarity with deep learning frameworks TensorFlow and/or PyTorch

View Details Remote Attendee Registration

RNA-seq and Differential Expression

Instructor: Wesley Brashear

Time: Friday, March 22, 2024 — 10:00AM-12:30PM CT

Location: Blocker 220

Description: Covers a brief introduction to RNA-seq technology, NGS QC, and differential expression analysis/data visualization.

Prerequisites: Current HPRC account

View Details In-Person Attendee Registration

Introduction to OpenFOAM

Instructor: Björn Windén

Time: Friday, March 22, 2024 — 1:30PM-4:00PM CT

Location: Blocker 220

Description: Introduction to OpenFOAM, the open source CFD Toolbox, and how to use it on HPRC clusters.

Prerequisites: Current HPRC account, basic Linux/Unix skills. Recommended: C++ experience

View Details In-Person Attendee Registration

ACES: Introduction to PyFR, a Scalable Open-source CFD Flow Solver

Instructor: Sambit Mishra

Time: Tuesday, March 26, 2024 — 10:00AM-12:30PM CT

Location: online using Zoom

Description: Beginner-level skills for running PyFR simulations on clusters

Prerequisites: Current ACCESS ID, basic Linux/Unix skills, basic Python skills.

View Details Remote Attendee Registration

ACES: Introduction to CryoSPARC for Cryo-EM Data Processing in Collaboration with the Laboratory for Biomolecular Structure and Dynamics

Instructor: Michael Dickens, Gaya Yadav

Time: Tuesday, March 26, 2024 — 1:30PM-4:00PM CT

Location: online using Zoom

Description: This course will cover aspects of using CryoSPARC on the ACES cluster followed by a training session using example image files.

Prerequisites: Current ACCESS ID, CryoSPARC Academic License ID

View Details Remote Attendee Registration

Using MATLAB on the ACES Cluster

Instructor: Marinus Pennings

Time: Tuesday, April 2, 2024 — 1:30PM-4:00PM CT

Location: Online using Zoom

Description: This course introduces different ways to use MATLAB on the ACES cluster and how to leverage its parallel resources.

Prerequisites: Current ACCESS ID, basic knowledge of MATLAB

View Details