HPC University
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A comprehensive list of training resources from the HPC University. HPCU is a virtual organization whose primary goal is to provide a cohesive, persistent, and sustainable on-line environment to share educational and training materials for a continuum of high performance computing environments that span desktop computing capabilities to the highest-end of computing facilities offered by HPC centers.
Cornell Virtual Workshop
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Cornell Virtual Workshop is a comprehensive training resource for high performance computing topics. The Cornell University Center for Advanced Computing (CAC) is a leader in the development and deployment of Web-based training programs. Our Cornell Virtual Workshop learning platform is designed to enhance the computational science skills of researchers, accelerate the adoption of new and emerging technologies, and broaden the participation of underrepresented groups in science and engineering. Over 350,000 unique visitors have accessed Cornell Virtual Workshop training on programming languages, parallel computing, code improvement, and data analysis. The platform supports learning communities around the world, with code examples from national systems such as Frontera, Stampede2, and Jetstream2.
Gentle Introduction to Programming With Python
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This course from MIT OpenCourseWare (OCW) covers very basic information on how to get started with programming using Python. Lectures are available, along with practice assignments, to users at no cost. Python has many applications in tech today, from web frameworks to machine learning. This course will also instruct users on how to get set up with an IDE, which will allow for way more efficient debugging.
Introduction to Deep Learning in Pytorch
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This workshop series introduces the essential concepts in deep learning and walks through the common steps in a deep learning workflow from data loading and preprocessing to training and model evaluation. Throughout the sessions, students participate in writing and executing simple deep learning programs using Pytorch – a popular Python library for developing, training, and deploying deep learning models.
Using Linux commands in a python script (and the difference between the subprocess and os python modules)
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Learn how to use Linux commands in a python script. Specifically, learn how to use the subprocess and os modules in python to run shell commands (which run Linux commands) in a python script that is run on a cluster.
DeapSECURE – Data-Enabled Advanced Computational Training Platform for Cybersecurity Research and Education
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DeapSECURE is a training program to infuse high-performance computational techniques into cybersecurity research and education. It is an NSF-funded project of the ODU School of Cybersecurity along with the Department of Electrical and Computer Engineering and the Information Technology Services at ODU. The DeapSECURE team has developed six non-degree training modules to expose cybersecurity students to advanced CI platforms and techniques rooted in big data, machine learning, neural networks, and high-performance programming. Techniques taught in DeapSECURE workshops are rather general and transferable to other areas including science, engineering, finance, linguistics, etc. All lesson materials are made available as open-source educational resources.
Version control with Git
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Understand the benefits of an automated version control system and the basics of how automated version control systems work. Configure git the first time it is used on a computer and understand the meaning of the --global configuration flag. Create a local Git repository and describe the purpose of the .git directory. Go through the modify-add-commit cycle for one or more files, explain where information is stored at each stage of that cycle, and distinguish between descriptive and non-descriptive commit messages.
Attention, Transformers, and LLMs: a hands-on introduction in Pytorch
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This workshop focuses on developing an understanding of the fundamentals of attention and the transformer architecture so that you can understand how LLMs work and use them in your own projects.
NERSC Training and Tutorials
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A comprehensive collection of NERSC developed training and tutorial events, offered on regular schedules. All sessions are archived, including slide decks, video recordings, and software examples as are available. Some examples of past training and tutorial topics are listed below
Deep Learning for Sciences Webinar Series
BerkeleyGW Tutorial Workshop
VASP Trainings
Timemory Software Monitoring Tutorial, April 2021
HPCToolkit to Measure and Analyzing GPU Applications Performance Tutorial
Totalview Tutorial
NVidia HPCSDK - OpenMP Target Offload Training
Parallelware Training Series
ARM Debugging and Profiling Tools Tutorial
Roofline on NVIDIA GPUs
GPUs for Science events
3-part OpenACC Training Series
9-part CUDA Training Series
Python
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Python course offered by Texas A&M HPRC
Performance Engineering Of Software Systems
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A class from MITOpenCourseware that gives a hands on approach to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, caching optimizations, parallel programming, and building scalable systems.
GIS: Projections and their distortions
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In GIS, projections are helpful to take something plotted on a globe and convert it to a flat map that we can print or show on a screen. Unfortunately it also introduces distortions to the objects and features on the map. This not only distorts the objects visually, but the results for any spatial attribute calculations will also reflect this distortion (such as distance and area ). Below is a link to a quick primer on projections, types of distortions that can occur, and suggestions on how to choose a correct projection for your work.
Slurm Tutorials
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Introduction to the Slurm Workload Manager for users and system administrators, plus some material for Slurm programmers.
Git Branching Workflow and Maneuvers
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A couple of resources that:
1.) Presents and defends a git branching workflow for stable collaborative git based projects. ("A Successful Git Branching Model")
2.) Maps "What do you want to do?" to the commands necessary to accomplish it. ("Git Flight Rules")
OpenMP Tutorial
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OpenMP (Open Multi-Processing) is an API that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.
Bash shell tutorial
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Training materials for using the bash (and zsh) shell.
Thrust resources
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Thrust is a CUDA library that optimizes parallelization on the GPU for you. The Thrust tutorial is great for beginners. The documentation is helpful for anyone using Thrust.
Astronomy data analysis with astropy
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Astropy is a community-driven package that offers core functionalities needed for astrophysical computations and data analysis. From coordinate transformations to time and date handling, unit conversions, and cosmological calculations, Astropy ensures that astronomers can focus on their research without getting bogged down by the intricacies of programming. This guide walks you through practical usage of astropy from CCD data reduction to computing galactic orbits of stars.
Active inference textbook
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This textbook is the first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines including computational neurosciences, machine learning, artificial intelligence, and robotics. It was published in 2022 and it's open access at this time. The contents in this textbook should be educational to those who want to understand how the free energy principle is applied to the normative behavior of living organisms and who want to widen their knowledge of sequential decision making under uncertainty.
GDAL Multi-threading
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Multi-threading guidance when using GDAL.
C Programming
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"These notes are part of the UW Experimental College course on Introductory C Programming. They are based on notes prepared (beginning in Spring, 1995) to supplement the book The C Programming Language, by Brian Kernighan and Dennis Ritchie, or K&R as the book and its authors are affectionately known. (The second edition was published in 1988 by Prentice-Hall, ISBN 0-13-110362-8.) These notes are now (as of Winter, 1995-6) intended to be stand-alone, although the sections are still cross-referenced to those of K&R, for the reader who wants to pursue a more in-depth exposition." C is a low-level programming language that provides a deep understanding of how a computer's memory and hardware work. This knowledge can be valuable when optimizing apps for performance or when dealing with resource-constrained environments.C is often used as the foundation for creating cross-platform libraries and frameworks. Learning C can allow you to develop libraries that can be used across different platforms, including iOS, Android, and desktop environments.
How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev
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A tutorial entitled "How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev" presented at SciPy 2023 in Austin, TX. This tutorial is hosted in a series of Jupyter Notebooks which can be accessed in the click of a button using Binder. See the README for more information.
Regular Expressions
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Regular expressions (sometimes referred to as RegEx) is an incredibly powerful tool that is used to define string patterns for "find" or "find and replace" operations on strings, or for input validation. Regular Expressions are used in search engines, in search and replace dialogs of word processors and text editors, and text-processing Linux utilities such as sed and awk. They are supported in many programming languages, including Python, R, Perl, Java, and others.
R for Data Science
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R for Data Science is a comprehensive resource for individuals looking to harness the power of the R programming language for data analysis, visualization, and statistical modeling. Whether you're a beginner or an experienced data scientist, this guide will help you unlock the full potential of R in the realm of data science.
Oakridge Leadership Computing Facility (OLCF) Training Events and Archive
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Upcoming training events and archives of training materials detailing general HPC best practices as well as how to use OLCF resources and services.