High-Performance Computing Training Series

 

 

 

 

To register for a course and other upcoming training courses: 

Visit the UTSA Human Resources Training & Development website.

Choose Search/Enroll/Withdraw, select the CT (computer/web) category from the dropdown menu, double-click on the class you wish to attend and choose Enroll to register.

 

If you are a student with no appointment:

Please contact the Research Computing Support Group to register rcsg@utsa.edu.

 

**Attendees should request an account on Shamu prior to attending by filling out the questionaire that pertains to your status below. **

 

 

 

 

An Introduction to High-Performance Computing (HPC) System at UTSA - CT0993

 

Date:

September 6, 2019 (9/6/2019)

November 1, 2019 (11/1/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

This course is for faculty, staff researchers, and student researchers.

This is a hands-on introduction training workshop to learn the basic concepts of High Performance Computing (HPC) and the basic skills of using Shamu, the HPC system at UTSA.

Attendees should request an account on Shamu prior to attending:  https://www.utsa.edu/oit/AboutUs/RCSG/HPC.html

 

Objectives:

By the end of this workshop, participants will understand the structure of the Shamu, know how to log onto Shamu from Linux, Mac, Windows operating systems, and know how to execute interactive and batch jobs on Shamu.


 

 

Parallel Programming in C on HPC - CT0998

 

Date:

September 20, 2019 (9/20/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

*This course is for faculty, staff researchers, and student researchers.*

This is a hands-on training course to learn the basic parallel programming skills on High Performance Computing (HPC) environment.

The following contents will be covered in the training:

  • Multi-Process Programming: the fundamental parallel programming model of Unix like operating systems.
  • Multiple Thread Programming: the light-weighed share-memory  parallel programming model.
  • Message Passing Interface (MPI) Programming: the distributed parallel programming model on HPC systems.


The basic programming knowledge in C is required for this training. Attendees should request an account on Shamu prior to attending:  https://www.utsa.edu/oit/AboutUs/RCSG/HPC.html.

By the end of this training, participants will have the basic knowledge to write simple parallel programs on Shamu, the HPC cluster, at UTSA.


 

 

Accelerating MATLAB Code with Multi-Core, GPU, and Cloud - CT0956

 

Date:

September 27, 2019 (9/27/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

*This course is for faculty, staff researchers, and student researchers.*

This is a hands-on training course to learn the basic skills to accelerate your MATLAB programs using the MATLAB Parallel Computing Toolbox on regular desktop computers, GPU enabled computers, High-Performance Computing clusters, and cloud-based MATLAB parallel computing environment.

The basic programming knowledge in MatLab is required for this training. Attendees should request an account on Shamu prior to attending:  https://www.utsa.edu/oit/AboutUs/RCSG/HPC.html.

By the end of this training, participants will have the basic knowledge to parallelize their traditional MatLab programs.


 

GPU Accelerated Computing with C - CT0956

 

Date:

October 18, 2019 (10/18/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

*This course is for faculty, staff researchers, and student researchers.*

CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose computing. In this hands-on training course, we will introduce the basic programming skills about CUDA programming.

A basic knowledge in C programming language is required for this training. Attendees should request an account on Shamu prior to attending:  https://www.utsa.edu/oit/AboutUs/RCSG/HPC.html.
 
Objectives:

By the end of this training, participants will have the basic knowledge to write simple CUDA programs to run on the GPU-enabled cluster nodes on Shamu, the High Performance Computing cluster at UTSA. To successfully complete this course, you should have some basic C/C++ competency.


 

Deep Learning with Tensorflow on an HPC Cluster - CT0948

Date:

October 25 , 2019 (10/25/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

*This course is for faculty, staff researchers, and student researchers.*

TensorFlow is one of the best libraries to implement Deep Learning (DP) models. In this training workshop, you will learn the basic concepts of TensorFlow, and use it to build a simply DP model. Then you will learn how to train the model efficiently on a HPC cluster with GPU enabled nodes. The basic knowledge in Python and Artificial Neural Network is required for this training.

Attendees should request an account on Shamu prior to attending:  https://www.utsa.edu/oit/AboutUs/RCSG/HPC.html.
 
By the end of this training, participants will have the basic knowledge to build DP models using Tensorflow and train the models efficiently on Shamu HPC cluster.


 

 An Intro to Parallel Computing in R  - CT0947

 

Date:

November 8, 2019 (11/8/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

*This course is for faculty, staff researchers, and student researchers.*

R is a programming language and environment for statistical computing and graphics, and it is widely used among statisticians and data miners for statistical and data analysis software development. In this training workshop, you will learn the techniques to accelerate the applications written in R on a variety of parallel computing environments including personal multi-core desktop computers, GPU enabled workstations, and Linux clusters.

Attendees should request an account on Shamu prior to attending:  https://www.utsa.edu/oit/AboutUs/RCSG/HPC.html.

By the end of this training, participants will be able to write parallel R programs for different parallel computing platforms.


 

An Intro to Big Data Analytics with Hadoop

 

Date:

November 22, 2019 (11/22/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

*This course is for faculty, staff researchers, and student researchers.*

Hadoop is an open-source software framework written in Java for storing and analyzing large datasets efficiently on a distributed computing system. It is designed to scale up from a single computer to thousands of computers. In this training, you will learn the basic concept of Big Data and the techniques for storing and processing large datasets using the Hadoop platform. 

Attendees should request an account on Shamu prior to attending:  https://www.utsa.edu/oit/AboutUs/RCSG/HPC.html.

By the end of this training participants will be able to write parallel R programs for different parallel computing platforms.


 

Introduction to Python Programming  - CT838

 

Date:

September 18, 2019 (9/18/2019)

October 2, 2019 (10/2/2019)

October 23, 2019 (10/23/2019)

November 6, 2019 (11/6/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

This is a hands-on introduction to the Python programming language. It will cover some general programming concepts, constructs of the Python language, and a walk-through of a use case. Some basic unix skills with the command line and text editors are required.

Attendees should request an account on Shamu prior to attending:  https://www.utsa.edu/oit/AboutUs/RCSG/HPC.html.

Participants will learn how to perform basic programming and debugging in Python as well as how to utilize it in the Linux environment available on the Shamu cluster.


 

Introduction to Jupyter Notebook  - CT0752

 

Date:

October 14, 2019 (10/14/2019)

Time:

10 a.m. - 12 p.m.

Location:

DexLab (AET 0.202)

Description:

*This course is for faculty, staff researchers and student researchers.*

Jupyter Notebook (formerly IPython Notebooks) is a web-based interactive computational environment for creating Jupyter notebook documents, which contain live code, equations, visualizations, and text. Jupyter comes with the IPython kernel for Python programming, but there are currently over 100 other kernels that you can also use, including R, Julia, etc. In this training, instead of focusing on one supported programming language, we are going to focus on the fundamental techniques for creating Notebook documents.

Objectives:

The attendees will be able to create and share Jupyter notebook documents using their own Jupyter Notebook installed on their laptops or desktops or using a public cloud base one like Microsoft Azure Notebook.

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