Knowledge Base Resources
These resources have been contributed and “vetted” by the community of cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators) that are participating in programs such as this one, that are supported by the ConnectCI community management platform. Additional Knowledge Base Resources are always welcome!
Higher Ed Controlled Unclassified Information Slack (HigherEdCUI)
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Slack channel for the Higher Ed CUI community
AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure
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This technology lab contains a set of sessions to help a new user start an AI project on the ACES cluster, a composable accelerator testbed at Texas A&M University. You will learn how to create and activate a virtual environment, manipulate and visualize data with Pandas and Matplotlib, use Scikit-learn for linear regression and classification applications, and use Pytorch to create and train a simple image classification model with deep neural networks (DNN).
Setting up PyFR flow solver on clusters
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These instructions were executed on the FASTER and Grace cluster computing facilities at Texas A&M University. However, the process can be applied to other clusters with similar environments. For local installation, please refer to the PyFR documentation.
Please note that these instructions were valid at the time of writing. Depending on the time you're executing these, the versions of the modules may need to be updated.
1. Loading Modules
The first step involves loading pre-installed software libraries required for PyFR. Execute the following commands in your terminal to load these modules:
module load foss/2022b
module load libffi/3.4.4
module load OpenSSL/1.1.1k
module load METIS/5.1.0
module load HDF5/1.13.1
2. Python Installation from Source
Choose a location for Python 3.11.1 installation, preferably in a .local directory. Navigate to the directory containing the Python 3.11.1 source code. Then configure and install Python:
cd $INSTALL/Python-3.11.1/
./configure --prefix=$LOCAL --enable-shared --with-system-ffi --with-openssl=/sw/eb/sw/OpenSSL/1.1.1k-GCCcore-11.2.0/ PKG_CONFIG_PATH=$LOCAL/pkgconfig LDFLAGS=/usr/lib64/libffi.so.6.0.2
make clean; make -j20; make install;
3. Virtual Environment Setup
A virtual environment allows you to isolate Python packages for this project from others on your system. Create and activate a virtual environment using:
pip3.11 install virtualenv
python3.11 -m venv pyfr-venv
. pyfr-venv/bin/activate
4. Install PyFR Dependencies
Several Python packages are required for PyFR. Install these packages using the following commands:
pip3 install --upgrade pip
pip3 install --no-cache-dir wheel
pip3 install --no-cache-dir botorch pandas matplotlib pyfr
pip3 uninstall -y pyfr
5. Install PyFR from Source
Finally, navigate to the directory containing the PyFR source code, and then install PyFR:
cd /scratch/user/sambit98/github/PyFR/
python3 setup.py develop
Congratulations! You've successfully set up PyFR on the FASTER and Grace cluster computing facilities. You should now be able to use PyFR for your computational fluid dynamics simulations.
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.
Tutorial for OpenMP Building up and Utilization
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The following link elaborates the usage of OpenMP API and its related syntax. There are also several exercises available for learners to help them get familiar with this widely-used tool for multi-threaded realization.
Electric field analyses for molecular simulations
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Tool to compute electric fields from molecular simulations
NCSA HPC-Moodle
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Self-paced tutorials on high-end computing topics such as parallel computing, multi-core performance, and performance tools. Some of the tutorials also offer digital badges.
Introduction to GPU/Parallel Programming using OpenACC
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Introduction to the basics of OpenACC.
DELTA Introductory Video
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Introductory video about DELTA. Speaker Tim Boerner, Senior Assistant Director, NCSA
FSL Lectures
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This is the official University of Oxford FSL group lecture page. This includes information on upcoming and past courses (online and in-person), as well as lecture materials. Available lecture materials includes slides and recordings on using FSL, MR physics, and applications of imaging data.
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 Campus Champion Example Allocation
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ACCESS requests proposals to be written following NSF proposal guidelines. The link provides an example of an ACCESS proposal using an NSF LaTeX template. The request is at the DISCOVER level appropriate for Campus Champions. The file is 2 pages: the first page details the motivation, approach, and resources requested; and the second page is a 1-page bio.
Using Dask on HPC Systems
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A tutorial on the effective use of Dask on HPC resources. The four-hour tutorial will be split into two sections, with early topics focused on novice Dask users and later topics focused on intermediate usage on HPC and associated best practices. The knowledge areas covered include (but are not limited to):
Beginner section
High-level collections including dask.array and dask.dataframe
Distributed Dask clusters using HPC job schedulers
Earth Science data analysis using Dask with Xarray
Using the Dask dashboard to understand your computation
Intermediate section
Optimizing the number of workers and memory allocation
Choosing appropriate chunk shapes and sizes for Dask collections
Querying resource usage and debugging errors
Intro to Statistical Computing with Stan
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The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function. Here are some useful links to start your exploration of this statistical programming language, and a Python interface to Stan.
Jetstream2 Status
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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.
AI powered VsCode Editor
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**Cursor: The AI-Powered Code Editor**
Cursor is a cutting-edge, AI-first code editor designed to revolutionize the way developers write, debug, and understand code. Built upon the premise of pair-programming with artificial intelligence, Cursor harnesses the capabilities of advanced AI models to offer real-time coding assistance, bug detection, and code generation.
**How Cursor Benefits High-Performance Computing (HPC) Work:**
1. **Efficient Code Development:** With AI-assisted code generation, researchers and developers in the HPC realm can quickly write optimized code for simulations, data processing, or modeling tasks, reducing the time to deployment.
2. **Debugging Assistance:** Handling complex datasets and simulations often lead to intricate bugs. Cursor's capability to automatically investigate errors and determine root causes can save crucial time in the HPC workflow.
3. **Tailored Code Suggestions:** Cursor's AI provides context-specific code suggestions by understanding the entire codebase. For HPC applications where performance is paramount, this means receiving recommendations that align with optimization goals.
4. **Improved Code Quality:** With AI-driven bug scanning and linter checks, Cursor ensures that HPC codes are not only fast but also robust and free of common errors.
5. **Easy Integration:** Being a fork of VSCode, Cursor allows seamless migration, ensuring that developers working in HPC can swiftly integrate their existing VSCode setups and extensions.
In essence, for HPC tasks that demand speed, precision, and robustness, Cursor acts as an invaluable co-pilot, guiding developers towards efficient and optimized coding solutions.
It is free if you provide your own OPEN AI API KEY.
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.
Building the ArduPilot environment for Linux
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This article provides instructions for building AirSim, an open-source simulator for autonomous vehicles, on Linux. It outlines the steps to build Unreal Engine, clone and build the AirSim repository, and set up the Unreal environment. It also includes information on how to use AirSim and optional setups such as remote control for manual flight.
Pandas - Python
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pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. It lets you store data in easy to manage and display data frames, with column names and datatypes.
AI Institutes Cyberinfrastructure Documents: SAIL Meeting
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Materials from the SAIL meeting (https://aiinstitutes.org/2023/06/21/sail-2023-summit-for-ai-leadership/). A space where AI researchers can learn about using ACCESS resources for AI applications and research.
Harnessing the Power of Cloud and Machine Learning for Climate and Ocean Advances
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Documentation and presentation on how to use machine learning and deep learning framework using TensorFlow, Keras and sci-kit learn for Climate and Ocean Advances
Official Documentation for PyTorch and NumPy
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The official documentation for PyTorch, a machine learning tensor-based framework, and NumPy, which allows for support for ndarrays which is useful to make tensors when implementing NNs. Both libraries can be installed with pip.
Trusted CI
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The mission of Trusted CI is to lead in the development of an NSF Cybersecurity Ecosystem with the workforce, knowledge, processes, and cyberinfrastructure that enables trustworthy science and NSF’s vision of a nation that is a global leader in research and innovation.
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.
Molecular Dynamics Tutorials for Beginner's
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Links to MD tutorials for beginner's across various simulation platforms.