This course introduces participants to the computing environment found in UH high performance computing clusters such as Opuntia, including how to prepare work-flows, submit jobs to the queuing systems, and retrieve results. Other topics covered include general HPC concepts, Opuntia’s system architecture, system access, customizing your user environment, compiling and linking codes for CPUs or GPUs, the PBS/SLURM batch scheduling system, batch job scripts, Matlab jobs, submission of serial or interactive or parallel (gpu/cpu) jobs to the batch system. 

Topics in Linux covered include user accounts, file permissions, file system navigation, the Command Line Interface (CLI), command line utility programs, file & folder manipulation, and common text editors. 

Topics covered in Shell scripting include built-in commands, control structures, file descriptors, functions, parameters & variables, and shell scripting.

Prerequisites: None.

Date: 8th June 2020 - 1st July 2020

Time: Mon Wed Fri 9:00 AM - 10:30 AM 

Instructor: Dr. Pablo Guillen-Rondon

Location: AERB Room 200, 202

Evaluation – 2 homework assignments: 25% each (50% total) – 1 final exam: 50% (last day of class)



C++ is one of the most widely used programming languages, particularly in the STEM fields. Various C++ compilers are available for the majority of computer architectures and operating systems. This tutorial will provide skills to understand and write C++ code starting with the basics. There will be many hands-on time sessions to write code. You will learn how to write, compile and debug some C code comfortably. You will understand and use the basic con-structs of C++; manipulate C++ datatypes, such as arrays, strings, containers, and pointers; isolate and fix common errors in C++ programs; use memory appropriately, including proper allocation/deallocation procedures; apply object-oriented approaches to software problems in C++, making use of structs, classes and objects. Several C++ problems will be presented and solved. Some of the newest feature of C++ will also mentioned/looked at. 

Prerequisites: Participants are expected to have familiarity with a low level programming language such as C/C++, or Fortran, and working comfortably in a UNIX/Linux environment.

Date: 8th June 2020 - 1st July 2020

Time : Mon Wed Fri 01:00 PM - 2:30 PM

Instructor: Dr. Jerry Ebalunode

Location: AERB 200 & 202


This course will teach you a basic understanding of how to program in R. You will learn how to use the R Studio software necessary for a statistical programming environment. The course covers reading data into R, accessing R packages, writing R functions, debugging, and commenting R code. We will introduce visualization concepts in R and you will also learn how to run R code on the HPE DSI HPC clusters.
Course is on the following dates. 

Prerequisites: None.

Intro to Linux/Cluster computing course provided by HPE DSI is recommended.

Instructor: Dr. Amit Amritkar

Location: AERB 200 & 202

Date: 9th June 2020 - 14th July 2020

Time : Tue Thurs 9:00 AM to 10:30 AM

Class Capacity: 44

Attendees who are not current UH student, staff or faculty, will need to make a payment for the course prior to its commencement using the following link:

 https://mynsmstore.uh.edu/index.php?route=product/product&search=211&product_id=27592



Python is an easy to learn, powerful programming language. It has efficient high-level data structures that make it suitable rapid application development. Topics covered in this session will include data types, conditional and loop statements, functions, input/output, modules, classes and exceptions. Upon completion of this tutorial series, participants should be able to understand existing scientific python codes as well as write their own simple python applications. This training session also introduces participants to scientific computing extensions of python like numpy for use in high-performance computing. Using advanced python libraries like regular expressions, scipy, pandas, seaborn, scikit-learn, etc for every day scientific computing are also taught.

Prerequisites: Participants are expected to have a working knowledge of the UNIX/Linux environment or should have taken Cluster computing course from HPE-DSI dept.

Date: 9th June 2020 - 14th July 2020 

Time: Tue Thurs 10:30 AM to 12:00 PM

Instructor: Dr. Jerry Ebalunode

Class Capacity:  44

Location: AERB 200, 202


This course is an introduction to the Julia programming language. Julia is the fastest modern open-source language for data science, machine learning, and scientific computing. Julia provides the functionality, ease-of-use and intuitive syntax of R, Python, Matlab, SAS or Stata combined with the speed, capacity, and performance of C, C++ or Java. Julia also provides parallel and distributed computing capabilities out of the box, and unlimited scalability with minimal effort.

Prerequisites: None.

Instructor: Dr. Amit Amritkar

Location: online

Date: 10th June 2020 - 3rd July 2020

Time : Mon Wed Fri 10:30 AM to 12:00 PM



Machine learning is the science of developing statistical methods that quantify relationships within data. This branch of mathematics/computer science has seen an explosive growth over the past decade as our ability to store and process digital data has dramatically increased. Prediction, classification, regression, and identification are the aims of learning from data. All of these problems are routinely performed in data analytic’s.

To obtain an overview of the literature in learning-based methods and applications.

To obtain an understanding of a variety of machine learning techniques for classification, regression, and prediction.

To obtain the ability to implement and experiment with a wide range of machine learning algorithms in Python with examples.

To apply: Unsupervised and Supervised learning and clustering concepts, Dimensional reduction, Kernels and kernel-based classifiers such as SVM, and Deep Learning algorithms.

To understand and implement learning-based methods for classification of images, signals and features.

Prerequisites: Participants are expected to have a working knowledge of the UNIX/Linux environment or should have taken Cluster computing course from HPE DSI dept.

Dates: 9th June 2020 - 14th July 2020

Time: Tue Thurs 1:00 PM to 2:30 PM

Instructor: Dr. Pablo Guillen-Rondon

Location: AERB 200, 202

Class Capacity: 44