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.
Introduction to GPU/Parallel Programming using OpenACC
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Introduction to the basics of OpenACC.
Contributing cycles to the Open Science Grid
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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.
Horovod: Distributed deep learning training framework
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Horovod is a distributed deep learning training framework. Using horovod, a single-GPU training script can be scaled to train across many GPUs in parallel. The library supports popular deep learning framework such as TensorFlow, Keras, PyTorch, and Apache MXNet.
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.
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!
GDAL Multi-threading
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Multi-threading guidance when using GDAL.
MATLAB bioinformatics toolbox
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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.
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.
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
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.
Application Fundamentals (Android)
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The provided text discusses various aspects of Android app development fundamentals. It covers key concepts related to app components, the AndroidManifest.xml file, and app resources. Android apps are built using various components, including Activities, Services, Broadcast Receivers, and Content Providers. These components serve different purposes and have distinct lifecycles. Activities are used for user interaction, services for background tasks, broadcast receivers for system-wide event handling, and content providers for managing shared data.The AndroidManifest.xml file is essential for declaring app components, permissions, and other settings. It informs the Android system about the app's components and capabilities. For instance, it specifies the minimum API level, declares hardware and software requirements, and defines intent filters to enable components to respond to specific actions.It's crucial to declare app requirements, such as device features and minimum Android API levels, to ensure compatibility with different devices and configurations. These declarations help in filtering the app's availability on Google Play for users with compatible devices.Android apps rely on resources separate from code, including images, layouts, strings, and more. These resources are stored in various directories and can be tailored for different device configurations. Providing alternative resources allows for optimization across different languages, screen sizes, orientations, and other factors.
Understanding these fundamentals is essential for developing Android applications effectively, ensuring compatibility, and providing a consistent user experience across a wide range of devices and configurations.
Containerization Explained
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Containerization is a software development method in which applications are packaged into standard units for development, shipment, and deployment.
Use Windows Subsystem for Linux for HPC Command Line Access from Windows
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Windows Subsystem for Linux (WSL) provides a Linux environment for Windows users to access HPC resources fast and efficiently.
The Theory Behind Neural Networks (Very Simplified)
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This video by the YouTube channel 3Blue1Brown provides a very simplified introduction to the theory behind neural networks. This tutorial is perfect for those that don't have much linear algebra or machine learning background and are eager to step into the realm of ML!
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.
Recommended Libraries for Cyberinfrastructure Users Developing Jupyter Notebooks
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This repository contains information about Jupyter Widgets and how they can be used to develop interactive workflows, data dashboards, and web applications that can be run on HPC systems and science gateways. Easy to build web applications are not only useful for scientists. They can also be used by software engineers and system admins who want to quickly create tools tools for file management and more!
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.
Raftlib: Open Source library for concurrent data processing pipelines
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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.
Campus Research Computing Consortium (CaRCC)
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CaRCC – the Campus Research Computing Consortium – is an organization of dedicated professionals developing, advocating for, and advancing campus research computing and data and associated professions.
Vision: CaRCC advances the frontiers of research by improving the effectiveness of research computing and data (RCD) professionals, including their career development and visibility, and their ability to deliver services and resources for researchers. CaRCC connects RCD professionals and organizations around common objectives to increase knowledge sharing and enable continuous innovation in research computing and data capabilities.
Machine Learning in R online book
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The free online book for the mlr3 machine learning framework for R. Gives a comprehensive overview of the package and ecosystem, suitable from beginners to experts. You'll learn how to build and evaluate machine learning models, build complex machine learning pipelines, tune their performance automatically, and explain how machine learning models arrive at their predictions.
fast.ai
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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.
Mechanism and Implementation of Various MPI Libraries
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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.
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.