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This course introduces participants to the computing environment found in UH high performance computing clusters such as Maxwell and Opuntia, including how to prepare work-flows, submit jobs to the queuing systems, and retrieve results. Other topics covered include general HPC concepts, Maxwell’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: June 10, 12, 14, 17, 19, 21, 24, 26, 28 July 01, 03

Time: Mon Wed Fri 9:00 AM - 10:30 AM

Instructor: Dr. Peggy Lindner & Dr. Amit Amritkar

Location: CBB 110 (please bring your laptop with you)

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

The course is free for members of the UH community (faculty, students, staff). All others please pay via the NSM store link.

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: June 12, 14, 17, 19, 21, 24, 26, 28 July 01, 03, 05

Time: M W F  1- 2:30 pm

Instructor: Dr. Martin Huarte-Espinosa

Class Capacity: 36

Location: MREB 200


The course is free for members of the UH community (faculty, students, staff). All others please pay via the NSM store link.

This course will teach you a basic understanding of how to program in R for Data Science and it is suitable for beginning programmers. We will utilize the R Studio as a programming environment. The course covers reading data into R, accessing R packages, writing R functions, statistical analysis, debugging, and commenting R code. We will introduce visualization concepts in R and you will also learn how to run R code in parallel in desktops and HPC clusters.

Room No: MREB 200 CBB 110

Capacity - 70

Time: 9:00 - 10:30 AM

Dates: June 18, 20, 25, 27 July 02, 09, 11, 16, 18, 23

Instructor: Peggy Lindner

Prerequisites: None.

Evaluation: 20% attendance, 30% homework, 50% project

The course is free for members of the UH community (faculty, students, staff). All others please enroll here and pay via the NSM store link.

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: June 10, 12, 14, 17, 19, 21, 24, 26, 28 July 01, 03 

Time: Mon Wed Fri 10:30 - 12: 00 PM

Instructor: Dr. Jerry Ebalunode.

Class Capacity: 36

Location: MREB 200.

The course is free for members of the UH community (faculty, students, staff). All others please pay via the NSM store link.

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: June 11, 13, 18, 20, 25, 27 July 02, 09, 11, 16

Time: Tu Thur 1:00 PM - 2:30 PM

Instructor: Dr. Pablo Guillen-Rondon

Location: MREB 200

The course is free for members of the UH community (faculty, students, staff). All others please pay via the NSM store link.

Part1: This part of the course will help you get started with debugging and using the gdb/idb debuggers. The topics covered include, understanding debugging, naive debugging, an introduction of debugging tools, serial code debugging, parallel code (OpenMP and MPI) debugging.

Part2:In an ideal case, parallelization would lead to a speed-up which scales linearly with the number of processors used compared to the original serial program running on a single processor. What if a program’s performance does not meet these expectations? Indeed, there are good reasons why these expectations most likely will not be met and we will explore those reasons and their remedies in this hands-on course. The part of the course will cover, understanding of serial and parallel performance (benchmarking), optimizing sequential programs - serial code profiling and analysis, tuning of parallel programs, parallel code profiling, collecting runtime information, and evaluation, analysis and presentation of the collected data.

Prerequisites: Familiarity with a low level programming language such as C/C++, or Fortran, Matlab and working comfortably in a UNIX/Linux environment or completed corresponding HPE DSI courses (cluster computing and C++).

Date: June 11, 13, 18, 20, 25, 27 July 02, 09, 11, 16

Time: Tu Thu 10:30 AM - 12 Noon

Instructor: Dr. Amit Amritkar

Location: MREB 200

Class Capacity: 36

The course is free for members of the UH community (faculty, students, staff). All others please pay via the NSM store link.