Knowledge Base Resources
Use these resources “vetted” by the community. Additional Knowledge Base Resources are always welcome.
NCSA HPC Training Moodle
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Self-paced tutorials on high-end computing topics such as parallel computing, multi-core performance, and performance tools. Other related topics include 'Cybersecurity for End Users' and 'Developing Webinar Training.' Some of the tutorials also offer digital badges. Many of these tutorials were previously offered on CI-Tutor. A list of open access training courses are provided below.
Parallel Computing on High-Performance Systems
Profiling Python Applications
Using an HPC Cluster for Scientific Applications
Debugging Serial and Parallel Codes
Introduction to MPI
Introduction to OpenMP
Introduction to Visualization
Introduction to Performance Tools
Multilevel Parallel Programming
Introduction to Multi-core Performance
Using the Lustre File System
OnShape FeatureScripts: Custom features for everyone
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OnShape FeatureScripts allow users to create their own features via OnShape's programming language. The user can make these as simple or complex as they need, and they can save tons of time for heavy OnShape users or complex projects!
A visual introduction to Gaussian Belief Propagation
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This website is an interactive introduction to Gaussian Belief Propagation (GBP). A probabilistic inference algorithm that operates by passing messages between the nodes of arbitrarily structured factor graphs. A special case of loopy belief propagation, GBP updates rely only on local information and will converge independently of the message schedule. The key argument is that, given recent trends in computing hardware, GBP has the right computational properties to act as a scalable distributed probabilistic inference framework for future machine learning systems.
Introduction to Vizualization on HPC Using Python
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This workshop has an introduction to the concepts of visualization followed by hands on exercises. The concepts section has Speaker Notes, and the hands on section has an accompanying Jupyter notebook.
The workshop is one in a series of Introduction to HPC
CaRCC Data Facing Track
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The Data-Facing Track of the People Network brings together people from research computing groups, libraries, research institutes, and other organizations who support data-enabled research. Many of us are also Researcher-Facing, but this track is an opportunity to discuss the varied challenges of working with data.
Feed Forward NNs and Gradient Descent
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Feed-forward neural networks are a simple type of network that simply rely on data to be "fed-forward" through a series of layers that makes decisions on how to categorize datum. Gradient descent is a type of optimization tool that is often used to train machines. These two areas in ML are good starting points and are the easiest types of neural network/optimization to understand.
HPCwire
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HPCwire is a prominent news and information source for the HPC community. Their website offers articles, analysis, and reports on HPC technologies, applications, and industry trends.
Cyber Security
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learning cybersecurity is crucial for personal protection, safeguarding digital assets, financial security, and national security. It is important when it comes to consumer data protection for business, creating long lasting relationships with customers.
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
Data Analysis with R for Educators
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This webinar series is an orientation to R. We start with an overview of R’s history and place in the larger data science ecosystem. Next, we introduce the R Studio user interface and how to access R’s excellent documentation. Finally, we present the fundamental concepts you need to use the R environment and language for data analysis. Along the way, we compare R script files (.R) to R Notebook (.Rmd) files and show how the features of R Notebook support better communication and encourage more dynamic engagement with statistical analysis and code. It is helpful to be familiar with tabular data analysis using statistical software, database tools, or spreadsheet programs.
Workshop materials, including setup directions and slides are available at https://github.com/CornellCAC/r_for_edu/ The Rstudio Cloud project used in the workshop is https://rstudio.cloud/project/4044219.
OpenStack Tutorial For Beginners
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OpenStack Tutorial For Beginners
Advanced Compilers: The Self-Guided Online Course
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This is a self guided online course on compilers. The topics covered throughout the course include universal compilers topics like intermediate representations, data flow, and “classic” optimizations as well as more research focusedtopics such as parallelization, just-in-time compilation, and garbage collection.
ACCESS KB Guide - Expanse
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Expanse at SDSC is a cluster designed by Dell and SDSC delivering 5.16 peak petaflops, and offers Composable Systems and Cloud Bursting.
How to use Rclone
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Learn how to use Rclone to transfer data, specifically from your local drive to the Open Storage Network, vice versa.
Machine Learning with sci-kit learn
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In the realm of Python-based machine learning, Scikit-Learn stands out as one of the most powerful and versatile tools available. This introductory post serves as a gateway to understanding Scikit-Learn through explanations of introductory ML concepts along with implementations examples in Python.
Fairness and Machine Learning
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The "Fairness and Machine Learning" book offers a rigorous exploration of fairness in ML and is suitable for researchers, practitioners, and anyone interested in understanding the complexities and implications of fairness in machine learning.
Science Gateway Tool/Web App Template (Jupyter Notebook + ipywidgets)
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Use this template to turn any science gateway workflow into a web application!
Ask.CI Q&A Platform for Research Computing
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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.
Paraview UArizona HPC links (beginner)
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These links take you to visualization resources supported by the University of Arizona's HPC visualization consultant (rtdatavis.github.io). The following links are specific to the Paraview program and the workflows that have been used my researchers at the U of Arizona. Some of the pages linked are very beginner friendly: getting started, working with cameras and keyframes for rendering, visualizing external files (netcdf climate data), graphs and data exporting.
Many of the workflows involve using remote desktops via the Open On Demand interface, but if this isn't set up at your university you can use paraview locally on a desktop. Feel free to post on access ci https://ask.cyberinfrastructure.org/ if you need assistance getting a paraview gui open for your work on HPC.
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.
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.
Slurm User Group Mailing List
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Federated CI Resources
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Discussion about contributing cycles to the Open Science Grid.
Trinity Tutorial for Transcriptome Assembly
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Trinity is one of the most popular tool to assemble transcripts from RNA-Seq short reads. In this tutorial, we will cover the basic usage of Trinity, best practice and common problems.