Machine Learning in Astrophysics
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Machine learning is becoming increasingly important in field with large data such as astrophysics. AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy allowing for a range of statistical and machine learning routines to analyze astronomical data in Python. In particular, it has loaders for many open astronomical datasets with examples on how to visualize such complicated and large datasets.
Implementing Markov Processes with Julia
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The following link provides an easy method of implementing Markov Decision Processes (MDP) in the Julia computing language. MDPs are a class of algorithms designed to handle stochastic situations where the actor has some level of control. For example, used at a low level, MDPs can be used to control an inverted pendulum, but applied in higher level decision making the can also decide when to take evasive action in air traffic management. MDPs can also be extended to the partially observable domain to form the Partially Observable Markov Decision Process (POMDP). This link contains a wealth of information to show one can easily implement basic POMDP and MDP algorithms and apply well known online and offline solvers.
Educause HEISC-800-171 Community Group
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The purpose of this group is to provide a forum to discuss NIST 800-171 compliance. Participants are encouraged to collaborate and share effective practices and resources that help higher education institutions prepare for and comply with the NIST 800-171 standard as it relates to Federal Student Aid (FSA), CMMC, DFARS, NIH, and NSF activities.
Framework to help in scaling Machine Learning/Deep Learning/AI/NLP Models to Web Application level
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This framework will help in scaling Machine Learning/Deep Learning/Artificial Intelligence/Natural Language Processing Models to Web Application level almost without any time.
Factor Graphs and the Sum-Product Algorithm
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A tutorial paper that presents a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes either exactly or approximately various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms
Jetstream Home
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Jetstream2 makes cutting-edge high-performance computing and software easy to use for your research regardless of your project’s scale—even if you have limited experience with supercomputing systems.Cloud-based and on-demand, the 24/7 system includes discipline-specific apps. You can even create virtual machines that look and feel like your lab workstation or home machine, with thousands of times the computing power.
OpenHPC: Beyond the Install Guide
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Materials for the "OpenHPC: Beyond the Install Guide" half-day tutorial, first offered at PEARC24. The goal of this repository is to let instructors or self-learners to construct one or more OpenHPC 3.x virtual environments, for those environments to be as close as possible to the defaults from the OpenHPC installation guide, and to then use those environments to demonstrate several topics beyond the basic installation guide.
Topics include:
1. Building a login node that's practically identical to a compute node (except for where it needs to be different)
2. Adding more security to the SMS and login node
3. Using node-local storage for the OS and/or scratch
4. De-coupling the SMS and the compute nodes (e.g., independent kernel versions)
5. GPU driver installation (simulated/recorded, not live)
6. Easier management of node differences (GPU or not, diskless/single-disk/multi-disk, Infiniband or not, etc.)
7. Slurm configuration to match some common policy goals (fair share, resource limits, etc.)
Introductory Tutorial to Numpy and Pandas for Data Analysis
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In this tutorial, I present an overview with many examples of the use of Numpy and Pandas for data analysis. Beginners in the field of data analysis can find It incredibly helpful, and at the same time, anyone who already has experience in data analysis and needs a refresher can find value in it. I discuss the use of Numpy for analyzing 1D and 2D multidimensional data and an introduction on using Pandas to manipulate CSV files.
Fundamentals of R Programming
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This course is an introduction to the R programming language and covers the fundamental concepts needed to operate in the R environment. This course was taught for the ACCESS community on September 26, 2023, but the materials for the course are still available on the ACES cluster and can be completed independently. All materials are presented as learnR notebooks and cover several topics, including data types, variables, built-in functions, data structures, and plotting.
ACCESS Getting Started Quick-Guide
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A step-by-step guide to getting your first allocation for Access computing and storage resources.
Bridges-2 Home Page
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Landing Page for Bridges-2 information
The Use of High-Performance Computing Services in University Settings: A Usability Case Study of the University of Cincinnati’s High-Performance Computing Cluster
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This presentation gives a detailed breakdown of the outcome of my master's thesis which was focused on making HPC Clusters accessible across all disciplines in a university setting "Our Case Study was the university of Cincinnati".
Resource to active inference
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Active inference is an emerging study field in machine learning and computational neuroscience. This website in particular introduces "active inference institute", which has established a couple of years ago, and contains a wide variety of resources for understanding the theory of active inference and for participating a worldwide active inference community.
Biopython Tutorial
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The Biopython Tutorial and Cookbook website is a dedicated online resource for users in the field of computational biology and bioinformatics. It provides a collection of tutorials and practical examples focused on using the Biopython library.
The website offers a series of tutorials that cover various aspects of Biopython, catering to users with different levels of expertise. It also includes code snippets and examples, and common solutions to common challenges in computational biology.
Developer Stories Podcast
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As developers, we get excited to think about challenging problems. When you ask us what we are working on, our eyes light up like children in a candy store. So why is it that so many of our developer and software origin stories are not told? How did we get to where we are today, and what did we learn along the way? This podcast aims to look “Behind the Scenes of Tech’s Passion Projects and People.” We want to know your developer story, what you have built, and why. We are an inclusive community - whatever kind of institution or country you hail from, if you are passionate about software and technology you are welcome!
Introduction to MP
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Open Multi-Processing, is an API designed to simplify the integration of parallelism in software development, particularly for applications running on multi-core processors and shared-memory systems. It is an important resource as it goes over what openMP and ways to work with it. It is especially important because it provides a straightforward way to express parallelism in code through pragma directives, making it easier to create parallel regions, parallelize loops, and define critical sections. The key benefit of OpenMP lies in its ease of use, automatic thread management, and portability across various compilers and platforms. For app development, especially in the context of mobile or desktop applications, OpenMP can enhance performance by leveraging the capabilities of modern multi-core processors. By parallelizing computationally intensive tasks, such as image processing, data analysis, or simulations, apps can run faster and more efficiently, providing a smoother user experience and taking full advantage of the available hardware resources. OpenMP's scalability allows apps to adapt to different hardware configurations, making it a valuable tool for developers aiming to optimize their software for a range of devices and platforms.
Jetstream2 Docs Site
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Jetstream2 makes cutting-edge high-performance computing and software easy to use for your research regardless of your project’s scale—even if you have limited experience with supercomputing systems.Cloud-based and on-demand, the 24/7 system includes discipline-specific apps. You can even create virtual machines that look and feel like your lab workstation or home machine, with thousands of times the computing power.
Examples of Thrust code for GPU Parallelization
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Some examples for writing Thrust code. To compile, download the CUDA compiler from NVIDIA. This code was tested with CUDA 9.2 but is likely compatible with other versions. Before compiling change extension from thrust_ex.txt to thrust_ex.cu. Any code on the device (GPU) that is run through a Thrust transform is automatically parallelized on the GPU. Host (CPU) code will not be. Thrust code can also be compiled to run on a CPU for practice.
Warewulf documentation
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Warewulf is an operating system provisioning platform for Linux that is designed to produce secure, scalable, turnkey cluster deployments that maintain flexibility and simplicity. It can be used to setup a stateless provisioning in HPC environment.
Beautiful Soup - Simple Python Web Scraping
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This package lets you easily scrape websites and extract information based on html tags and various other metadata found in the page. It can be useful for large-scale web analysis and other tasks requiring automated data gathering.
How to Build a Great Relationship with a Mentor
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Emphasizes benefits of being mentored. Describes how to identify and choose a mentor. Suggests a path forward. Not mentor or two-way focused.
PetIGA, an open-source code for isogeometric analysis
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This documentation provides an overview of the PetIGA framework, an open source code for solving multiphysics problems with isogeometric analysis. The documentation covers some simple tutorials and examples to help users get started with the framework and apply it to solve real-world problems in continuum mechanics, including solid and fluid mechanics.
Info about retiring of R GIS packages rgdal, rgeos, maptools in 2023
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R GIS packages "rgdal", "rgeos", and "maptools" are package set to be archived and no longer supported by end of 2023. Many other R GIS packages are build on top of these packages, including "sp" and "raster". The packages recommended as replacement for "sp" is "sf" and the replacement for "raster" is "terra". Below are links to published articles regarding this transition. Additionally, I am including links to the documentation for the new packages recommended to be used "sf" and "terra".
PyTorch Introduction
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This is a very barebones introduction to the PyTorch framework used to implement machine learning. This tutorial implements a feed-forward neural network and is taught completely asynchronously through Stanford University. A good start after learning the theory behind feed-forward neural networks.
Building Anaconda Navigator applications
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This tutorial explains how to create an Anaconda Navigator Application (app) for JupyterLab. It is intended for users of Windows, macOS, and Linux who want to generate an Anaconda Navigator app conda package from a given recipe. Prior knowledge of conda-build or conda recipes is recommended.