TeraGrid and Physics Research

(1) Deep learning algorithms show potential for sea ice classification from SAR images but face challenges from scarce and inaccurate training data. (2) Researchers generated training datasets by manually labeling SAR image patches with ice types, assisted by optical images. (3) A modified VGG-16 network trained on augmented SAR patch data achieved 97.3% accuracy classifying ice vs water.

In this deck from DataTech19, Debbie Bard from NERSC presents: Supercomputing and the scientist: How HPC and large-scale data analytics are transforming experimental science. "Debbie Bard leads the Data Science Engagement Group NERSC. NERSC is the mission supercomputing center for the USA Department of Energy, and supports over 7000 scientists and 700 projects with supercomputing needs. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and has worked at Imperial College London as well as the Stanford Linear Accelerator Center (SLAC) in the USA, before joining the Data Department at NERSC, where she focuses on data-intensive computing and research, including supercomputing for experimental science and machine learning at scale." Watch the video: https://wp.me/p3RLHQ-kLV Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

This document provides an overview of distributed data management for the Large Hadron Collider (LHC) experiments at CERN. It discusses the worldwide computing grid that is used to store, process, and analyze the immense volumes of data produced by the LHC experiments each year. The grid consists of Tier 0, 1, and 2 computing centers around the world. It has enabled scientists from many collaborating institutions to work together on data from the LHC experiments.

05.02.04 Invited Talk to the NASA Jet Propulsion Laboratory Title: LambdaGrids--Earth and Planetary Sciences Driving High Performance Networks and High Resolution Visualizations Pasadena, CA

12.03.08 Information Technology Infrastructure Committee (ITIC)Report to the NAC Larry Smarr Chair ITIC

This document provides an overview of the NPOESS Program. NPOESS is a tri-agency program between NOAA, NASA, and the Department of Defense to develop the next generation of US polar-orbiting environmental satellites. The goal is to converge the DoD and NOAA satellite programs to achieve cost savings while incorporating new technologies. NPOESS will provide global environmental data for weather forecasting, climate monitoring, and other applications. The first NPOESS satellite is scheduled for launch in 2013 and the system is expected to operate through 2026. The NPP satellite launching in 2011 will help reduce risks for NPOESS.

The ADF Modeling Suite is a fully integrated software package for computational chemistry developed by Software for Chemistry & Materials. It contains modules for molecular and periodic DFT (ADF and BAND), semi-empirical methods (DFTB and MOPAC), reactive molecular dynamics (ReaxFF), and fluid thermodynamics (COSMO-RS). The suite has an excellent graphical user interface that makes setup and analysis easy across different calculation types on Windows, Mac, and Linux. It is used widely in academia and industry for research in areas like inorganic chemistry, materials science, pharma, and nanoscience.

This document provides an overview of an ExtremeEarth project that aims to apply deep learning techniques to classify sea ice in polar regions using satellite imagery. The project has received funding from the European Union. It discusses challenges in classifying sea ice from SAR imagery compared to optical imagery. It outlines user requirements for sea ice products, including high resolution (300m or better) and frequent updates (near real-time). The document describes workflows using the Polar Thematic Exploitation Platform (Polar TEP) for large-scale sea ice mapping using Copernicus satellite data and machine learning algorithms. It also discusses exploitation of results, including the impact of Polar TEP and efforts to facilitate the polar machine learning community.

Hybrid Cloud for CERN Experience with Open Telecom Cloud and OpenStack 1) CERN uses a hybrid cloud approach combining on-premise resources with public clouds like the Open Telecom Cloud to address its increasing computing needs for projects like the LHC. 2) A pilot project using the Open Telecom Cloud was successful but identified some issues around networking and storage. 3) CERN is now participating in the HNSciCloud project to jointly procure innovative hybrid cloud services that fully integrate commercial clouds with in-house and European e-infrastructure resources.

Key benefits ADF modeling suite One-stop modeling shop Excellent software suite for tackling the most challenging problems in materials science and chemistry. Easy set up and analysis with GUI. Fast computational toolbox Working with hardware vendors, we optimize our codes for desktop computers and parallel supercomputers. Latest algorithms. Heavy elements, spectroscopy, organic electronics High-quality all-electron Slater basis sets for all elements. Accurate relativity. Many spectroscopic properties, from NMR to X-ray. Unique organic electronics tools: charge transport, phosphorescence. Understand chemical bonding Unique insight in chemical bonds with many chemical analysis tools. Balanced charge decomposition schemes and density analysis tools. Hassle-free installation, free trial With parallel binaries for all popular platforms, the entire ADF suite installs out of the box. Try out our powerful modeling tools for free: http://www.scm.com/trial Discuss your science with experts With decades of experience, our expert support team (PhDs in chemistry & physics) will help you with any queries that may arise.

Introductory slides for workshop at George Washington University: ADF for molecular properties, BAND for periodic DFT, DFTB for large electronic structure calculations and ReaxFF for molecular dynamics. Get started with the excellent graphical interface.

This is a presentation by Prof. Anne Elster at the International Workshop on Open Source Supercomputing held in conjunction with the 2017 ISC High Performance Computing Conference.