Open Storage Network
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The Open Storage Network, a national resource available through the XSEDE resource allocation system, is high quality, sustainable, distributed storage cloud for the research community.
CMake Tutorials
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CMake is an open-source tool used to manage the build process in operating systems. This tutorial takes you through how to use CMake from the very basics with example projects.
Campus Champions Home Page
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Campus Champions foster a dynamic environment for a diverse community of research computing and data professionals sharing knowledge and experience in digital research infrastructure.
Introduction to Probabilistic Graphical Models
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This website summarizes the notes of Stanford's introductory course on probabilistic graphical models.
It starts from the very basics and concludes by explaining from first principles the variational auto-encoder, an important probabilistic model that is also one of the most influential recent results in deep learning.
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.
Better Scientific Software (BSSw)
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The Better Scientific Software (BSSw) project provides a community to collaborate and learn about best practices in scientific software development. Software—the foundation of discovery in computational science & engineering—faces increasing complexity in computational models and computer architectures. BSSw provides a central hub for the community to address pressing challenges in software productivity, quality, and sustainability.
Header-only C++ JSON library
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JSON is a lightweight format for storing and transporting data, for example in a config file. This library is header-only, and has easy-to-read documentation. It is a C++ library.
Advanced Mathematical Optimization Techniques
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Mathematical optimization deals with the problem of finding numerically minimums or maximums of a functions. This tutorial provides the Python solutions for the optimization problems with examples.
InsideHPC
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InsideHPC is an informational site offers videos, research papers, articles, and other resources focused on machine learning and quantum computing among other topics within high performance computing.
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.
GIS: What is a Geodetic Datums?
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Often when working with GIS, or spatial data, one encounters the word "datum" and it may require that you choose a "datum" when doing GIS computation tasks. Below is a short video on what are datums from NOAA and UCAR.
RMACC Website
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Rocky Mountain Advanced Computing Consortium Website
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.
File management of Visual Studio Code on clusters
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Visual Studio Code, commonly known as VSCode, is a popular tool used by programmers worldwide. It serves as a text editor and an Integrated Development Environment (IDE) that supports a wide variety of programming languages. One of its key features is its extensive library of extensions. These extensions add on to the basic functionalities of VSCode, making coding more efficient and convenient.
However, there's a catch. When these extensions are installed and used frequently, they generate a multitude of files. These files are typically stored in a folder named .vscode-extension within your home directory. On a cluster computing facility such as the FASTER and Grace clusters at Texas A&M University, there's a limitation on how many files you can have in your home directory. For instance, the file number limit could be 10000, while the .vscode-extension directory can hold around 4000 temporary files even with just a few extensions. Thus, if the number of files in your home directory surpasses this limit due to VSCode extensions, you might face some issues. This restriction can discourage users from taking full advantage of the extensive features and extensions offered by the VSCode editor.
To overcome this, we can shift the .vscode-extension directory to the scratch space. The scratch space is another area in the cluster where you can store files and it usually has a much higher limit on the number of files compared to the home directory. We can perform this shift smoothly using a feature called symbolic links (or symlinks for short). Think of a symlink as a shortcut or a reference that points to another file or directory located somewhere else.
Here's a step-by-step guide on how to move the .vscode-extension directory to the scratch space and create a symbolic link to it in your home directory:
1. Copy the .vscode-extension directory to the scratch space: Using the cp command, you can copy the .vscode-extension directory (along with all its contents) to the scratch space. Here's how:
cp -r ~/.vscode-extension /scratch/user
Don't forget to replace /scratch/user with the actual path to your scratch directory.
2. Remove the original .vscode-extension directory: Once you've confirmed that the directory has been copied successfully to the scratch space, you can remove the original directory from your home space. You can do this using the rm command:
rm -r ~/.vscode-extension
It's important to make sure that the directory has been copied to the scratch space successfully before deleting the original.
3. Create a symbolic link in the home directory: Lastly, you'll create a symbolic link in your home directory that points to the .vscode-extension directory in the scratch space. You can do this as follows:
ln -s /scratch/user/.vscode-extension ~/.vscode-extension
By following this process, all the files generated by VSCode extensions will be stored in the scratch space. This prevents your home directory from exceeding its file limit. Now, when you access ~/.vscode-extension, the system will automatically redirect you to the directory in the scratch space, thanks to the symlink. This method ensures that you can use VSCode and its various extensions without worrying about hitting the file limit in your home directory.
Discover Data Science
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Discover Data Science is all about making connections between prospective students and educational opportunities in an exciting new, hot, and growing field – data science.
Regulated Research Community of Practice
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The daily news clearly shows the increasing threat to safety and privacy of data, personal as well as intellectual property. While the requirements such as DFARS 7012, HIPAA, and Cybersecurity Maturity Model Certification (CMMC) improve the consistency of data handling between agencies and contractors and grantees, it leaves academic institutions to figure out how to meet such requirements in a cost-effective way that fits the research and education mission of the institution. Most institutions, agencies, and companies act in isolation with one-off contract language to address data security and safeguarding concerns. Even though cybersecurity has a clear and uniform goal of protecting data, a onesize solution does not fit all academic institutions.
By supporting this community with development of a community strategic roadmap, regular discussions and workshops, and a repository of generalized and specific resources for handling regulated research programs RRCoP lowers the barrier to entry for institutions handling new regulations.
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.
Metadata Systems
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Metadata is a vital topic in libraries and librarianship, encompassing structured information used for accessing digital resources. The definition of metadata varies but is essentially data about data. It has evolved beyond simply describing metadata schemas and now focuses on topics like interoperability, non-descriptive metadata (administrative and preservation metadata), and the effective application of metadata schemas for user discovery. Interoperability, the ability to seamlessly exchange metadata between systems, is a major concern. Different levels of interoperability are examined, including schema-level, record-level, and repository-level. Challenges to interoperability include variations in standards, collaboration barriers, and costs.Metadata management is discussed in terms of the holistic management of metadata across an entire library. Steps include analyzing metadata requirements, adopting schema, creating metadata content, delivery/access, evaluation, and maintenance. Administrative metadata, which encompasses ownership and production information, is becoming more critical, particularly for electronic resource licensing. Preservation metadata is also gaining importance in ensuring the long-term viability of digital objects.
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
Awesome Jupyter Widgets (for building interactive scientific workflows or science gateway tools)
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A curated list of awesome Jupyter widget packages and projects for building interactive visualizations for Python code
Data visualization with Matplotlib
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Data visualization is a critical aspect of data analysis. It allows for a clear and concise representation of data, making it easier for users to understand and interpret complex datasets. One of the most popular libraries for data visualization in Python is Matplotlib. The included website aims to provide a brief overview of Matplotlib, its features, and examples/exercises to dive deeper into its functionalities.
UNIX/command line basics tutorial
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Introductory training materials for working on the UNIX command line.
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
What are LSTMs?
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This reading will explain what a long short-term memory neural network is. LSTMs are a type of neural networks that rely on both past and present data to make decisions about future data. It relies on loops back to previous data to make such decisions. This makes LSTMs very good for predicting time-dependent behavior.
Creating a Mobile Application
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Goes through in detail on how to build an application that can run on Android and IOS devices, using Qt Creator to develop Qt Quick applications. Goes through the setting up, creation, configuration, optimization, and overall deployment. This provides the fundamental basis, need to click around on the site for more specifics.