Understanding Program Performance: When Academia Meets the Practice
Explore challenges in applying academic performance analysis techniques to real-world software, focusing on experiences with the Linux kernel using Perun performance management system.
Effective Cost Attribution in Multi-User OpenShift Environment
Discover strategies for accurate cost attribution in multi-user OpenShift environments, focusing on cluster profiles, dedicated pools, and spend analysis for efficient resource management.
Towards Stronger Checks on Dependencies and Compose Inputs in Fedora
Explore advancements in Fedora's automated testing, focusing on critical path updates, integration tests, and potential improvements for package dependencies and compose inputs.
Open Education in the New England Research Cloud (NERC)
Explore the Open Education Project's architecture, leveraging open-source tech for accessible, high-quality educational materials. Learn about NERC infrastructure and OpenShift AI deployment for large-scale courses.
Observability and Instrumentation via OpenTelemetry - Lecture 1
Explore observability basics, instrumentation, and OpenTelemetry standardization. Learn to implement tracing in Python with open-source examples.
Super Accessible No Math Intro to Neural Networks for Beginners
Discover neural networks through intuitive examples, understanding their purpose, functionality, limitations, and pitfalls to better grasp the AI/ML landscape.
Cloud-Native Hardware in the Loop Testing with Jumpstarter
Explore cloud-native hardware-in-the-loop testing for edge devices using Jumpstarter, streamlining CI/CD pipelines and development workflows for software stacks on specific hardware.
Linux Kernel Dynamic CPU Isolation
Explore dynamic CPU isolation in Linux kernels for containerized environments, enhancing workload performance and resource utilization.
bpftrace: What It Is, What's New, and Where It's Going
Explore bpftrace, a high-level Linux tracing language. Learn about its features, recent updates, and future developments for debugging and observability in Linux systems.
(Less Than) 50 Ways to Build Multi-Arch Containers
Explore diverse methods for creating multi-architecture container images. Learn to build versatile containers that run on various platforms, simplifying development and deployment processes.
Porting and Generalizing Dynamic Privilege in Linux
Explore Dynamic Privilege in Linux: acquire and relinquish hardware privilege on-the-fly. Learn about porting core primitives to ARM64 and implications for system optimization.
How to Win Friends and Influence LLMs with Prompt Engineering
Master prompt engineering techniques to optimize large language model outputs for text and code tasks.
Jupyter Extension for Executing Kubeflow Pipeline Seamlessly
Explore Elyra's integration with Kubeflow 2.0, enhancing ML workflow development and deployment on Kubernetes with visual editing and multi-language support.
Going from Containers to Pods to Kubernetes - Help for Your Dev Environments
Explore Podman Desktop: streamline container workflows, bridge local-production gaps, and simplify Kubernetes adoption for developers. Learn to transition from apps to containers to pods efficiently.
Expanding on a Modern Java Developer's Toolkit
Explore Cryostat for profiling Java workloads in Kubernetes, enhancing cloud deployment performance analysis and resource optimization.
Phase Diagram of Stochastic Gradient Descent in High-Dimensional Two-Layer Neural Networks
Explore the phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks, focusing on its behavior and implications.
Recoupling Coefficients and Quantum Entropies in Condensed Matter Physics and Quantum Field Theory
Explore recoupling coefficients and quantum entropies in condensed matter physics and quantum field theory with insights from M. Christandl.
Machine Learning for Quantum Applications - Part 2
Explore advanced machine learning techniques for quantum applications, focusing on cutting-edge developments and practical implementations in quantum systems.
Is the Ground State of Anderson's Impurity Model a Recurrent Neural Network?
Explore the connection between Anderson's impurity model ground state and recurrent neural networks in condensed matter physics.
Deep Learning for Detecting Anthropogenic Global Warming Signal in Daily Precipitation
Explore deep learning's detection of climate change signals in daily precipitation, revealing human impact on global hydrological patterns through advanced neural network analysis.
Machine Learning and Resampling for Stochastic Parameterization with Memory
Explore machine learning and resampling techniques for stochastic parameterization with memory in multiscale dynamical systems, focusing on overcoming challenges in long-memory scenarios.