Knowledge Base Resources

Discover Data Science is all about making connections between prospective students and educational opportunities in an exciting new, hot, and growing field – data science.

This tutorial explains how to use Python for GPU acceleration with libraries like CuPy, PyOpenCL, and PyCUDA. It shows how these libraries can speed up tasks like array operations and matrix multiplication by using the GPU. Examples include replacing NumPy with CuPy for large datasets and using PyOpenCL or PyCUDA for more control with custom GPU kernels. It focuses on practical steps to integrate GPU acceleration into Python programs.

An AWS Tutorial for Beginners is a course that teaches the basics of Amazon Web Services (AWS), a cloud computing platform that offers a wide range of services, including compute, storage, networking, databases, analytics, machine learning, and artificial intelligence.

These links take you to visualization resources supported by the University of Arizona's HPC visualization consultant ([rtdatavis.github.io](http://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. These links are distinct from the others posted in the beginner paraview access ci links from the University of Arizona in that they are for more complex workflows. The links included explain how to use the terminal with paraview (pvpython), and the steps to leverage HPC resources for headless batch rendering. The batch rendering tutorial is significantly more complex than the others so if you find yourself stuck please post on the https://ask.cyberinfrastructure.org/ and I will try to troubleshoot with you.

A lecture and notes with the goal of teaching introductory python. Starting by understanding how to download and start using python, then expanding to basic syntax for lists, arrays, loops, and methods.

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.

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.

It's not uncommon to see beautiful visualizations in HPC center galleries, but the majority of these are either rendered off the HPC or created using programs that run on OpenGL or custom rasterization techniques. To put it simply the next generation of graphics provided by OpenGL's successor Vulkan is strangely absent in the super computing world. The aim of this survey of available resources is to determine the systems that can support Vulkan workflows and programs. This will assist users in getting past some of the first hurdles in using Vulkan in HPC contexts.

Making a neural network has never been easier! The following link directs users to the Flux.jl package, the easiest way of programming a neural network using the Julia programming language. Julia is the fastest growing software language for AI/ML and this package provides a faster alternative to Python's TensorFlow and PyTorch with a 100% Julia native programming and GPU support.

Raftlib is an open-source C++ Library that provides a framework for implementing parallel and concurrent data processing pipelines. It is designed to simplify the development of high-performance data processing applications by abstracting away the complexities of parallelism, concurrency, and data flow management. It enables stream/data-flow parallel computation by linking parallel compute kernels together using simple right shift operators, similar to C++ streams for string manipulation. RaftLib eliminates the need for explicit usage of traditional threading libraries such as pthreads, std::thread, or OpenMP, which can lead to non-deterministic behavior when misused.

Workshop for beginners and intermediate students in MPI which includes helpful exercises. Open MPI documentation.

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.

Fastai offers many tools to people working with machine learning and artifical intelligence including tutorials on PyTorch in addition to their own library built on PyTorch, news articles, and other resources to dive into this realm.

Running QGIS tools from the command line

Background: Big data, defined as having high volume, complexity or velocity, have the potential to greatly accelerate research discovery. Such data can be challenging to work with and require research support and training to address technical and ethical challenges surrounding big data collection, analysis, and publication. Methods: The present study was conducted via a series of semi-structured interviews to assess big data methodologies employed by CU Boulder researchers across a broad sample of disciplines, with the goal of illuminating how they conduct their research; identifying challenges and needs; and providing recommendations for addressing them. Findings: Key results and conclusions from the study indicate: gaps in awareness of existing big data services provided by CU Boulder; open questions surrounding big data ethics, security and privacy issues; a need for clarity on how to attribute credit for big data research; and a preference for a variety of training options to support big data research.

Pandas is one of the most essential Python libraries for data analysis and manipulation. It provides high-performance, easy-to-use data structures, and data analysis tools for the Python programming language. The official documentation serves as an in-depth guide to using this powerful tool including explanations and examples.

A master’s degree in data science helps prepare professionals to take the next career step. This article will focus primarily on data science, a graduate degree in this field, and a data scientist or data analyst career. With many employers preferring a master’s degree in data science for those seeking to fill roles as data scientists or analysts, we will discuss the data science master’s degree in detail.

Numpy is a python package that leverages types and compiled C code to make many math operations in Python efficient. It is especially useful for matrix manipulation and operations.

Listing of upcoming ACCESS related events and training activities.

Bioinformatics Toolbox provides algorithms and apps for Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. Using toolbox functions, you can read genomic and proteomic data from standard file formats such as SAM, FASTA, CEL, and CDF, as well as from online databases such as the NCBI Gene Expression Omnibus and GenBank.

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.

Spack is a package manager for supercomputers that can help administrators install scientific software and libraries for multiple complex software stacks.

This article discusses the importance of fairness in machine learning and provides insights into how Google approaches fairness in their ML models. The article covers several key topics: Introduction to fairness in ML: It provides an overview of why fairness is essential in machine learning systems, the potential biases that can arise, and the impact of biased models on different communities. Defining fairness: The article discusses various definitions of fairness, including individual fairness, group fairness, and disparate impact. It explains the challenges in achieving fairness due to trade-offs and the need for thoughtful considerations. Addressing bias in training data: It explores how biases can be present in training data and offers strategies to identify and mitigate these biases. Techniques like data preprocessing, data augmentation, and synthetic data generation are discussed. Fairness in ML algorithms: The article examines the potential biases that can arise from different machine learning algorithms, such as classification and recommendation systems. It highlights the importance of evaluating and monitoring models for fairness throughout their lifecycle. Fairness tools and resources: It showcases various tools and resources available to practitioners and developers to help measure, understand, and mitigate bias in machine learning models. Google's TensorFlow Extended (TFX) and What-If Tool are mentioned as examples. Google's approach to fairness: The article highlights Google's commitment to fairness and the steps they take to address fairness challenges in their ML models. It mentions the use of fairness indicators, ongoing research, and partnerships to advance fairness in AI. Overall, the article provides a comprehensive overview of fairness in machine learning and offers insights into Google's approach to building fair ML models.

There is a detailed explanation about communication routines and managing methods of different MPI libraries, as well as several exercises designed for users to get familiar with the implementation of MPI build process.