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At least 343 records · Page 19

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Year-end report FY15 Q4.

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

97 MATHEMATICS AND COMPUTING↗

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem. Mid-year report FY16 Q2

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

97 MATHEMATICS AND COMPUTING↗

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Mid-year report FY17 Q2

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

97 MATHEMATICS AND COMPUTING↗

XVIS: Visualization for the Extreme-Scale Scientific-Computation Ecosystem Final Scientific/Technical Report

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. The XVis project brought together collaborators from predominant DOE projects for visualization on accelerators and combining their respective features into a new visualization toolkit called VTK-m.

97 MATHEMATICS AND COMPUTING↗

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Year-end report FY17.

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

54 ENVIRONMENTAL SCIENCES↗

XVis: Visualization for the Extreme-Scale Scientific Computation Ecosystem (Final Report)

Scientific computing is no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. The XVis project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis. This report reviews the accomplishments of the XVis project to prepare scientific visualization for Exascale computing.

97 MATHEMATICS AND COMPUTING↗

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem (Year-end report FY16 Q4)

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

97 MATHEMATICS AND COMPUTING↗

XVis: Visualization for the Extreme-Scale Scientific Computation Ecosystem. Final Report

Scientific computing is no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/0 limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. The XVis project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis. This report reviews the accomplishments of the XVis project to prepare scientific visualization for Exascale computing.

97 MATHEMATICS AND COMPUTING↗

A self-referential HOWTO on release engineering

Release engineering is a fundamental part of the software development cycle: it is the point at which quality control is exercised and bug fixes are integrated. The way in which software is released also gives the end user her first experience of a software package, while in scientific computing release engineering can guarantee reproducibility. For these reasons and others, the release process is a good indicator of the maturity and organization of a development team. Software teams often do not put in place a release process at the beginning. This is unfortunate because the team does not have early and continuous execution of test suites, and it does not exercise the software in the same conditions as the end users. I describe an approach to release engineering based on the software tools developed and used by the GNU project, together with several specific proposals related to packaging and distribution. I do this in a step-by-step manner, demonstrating how this very paper is written and built using proper release engineering methods. Because many aspects of release engineering are not exercised in the building of the paper, the accompanying software repository also contains examples of software libraries.

42 ENGINEERING↗

Handling Large and Complex Data in a Photovoltaic Research Institution Using a Custom Laboratory Information Management System

Twenty-five years ago the desktop computer started becoming ubiquitous in the scientific lab. Researchers were delighted with its ability to both control instrumentation and acquire data on a single system, but they were not completely satisfied. There were often gaps in knowledge that they thought might be gained if they just had more data and they could get the data faster. Computer technology has evolved in keeping with Moore’s Law meeting those desires; however those improvements have of late become both a boon and bane for researchers. Computers are now capable of producing high speed data streams containing terabytes of information; capabilities that evolved faster than envisioned last century. Software to handle large scientific data sets has not kept up. How much information might be lost through accidental mismanagement or how many discoveries are missed through data overload are now vital questions. An important new task in most scientific disciplines involves developing methods to address those issues and to create the software that can handle large data sets with an eye towards scalability. This software must create archived, indexed, and searchable data from heterogeneous instrumentation for the implementation of a strong data-driven materials development strategy. At the National Center for Photovoltaics in the National Renewable Energy Laboratory, we began development a few years ago on a Laboratory Information Management System (LIMS) designed to handle lab-wide scientific data acquisition, management, processing and mining needs for physics and materials science data, and with a specific focus towards future scalability for new equipment or research focuses. We will present the decisions, processes, and problems we went through while building our LIMS system for materials research, its current operational state and our steps for future development.

14 SOLAR ENERGY↗

Science & Technology Review: The Road to Exascale Computing

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world. The Department of Energy’s Exascale Computing Project (ECP) and Lawrence Livermore’s RADIUSS (Rapid Application Development via an Institutional Universal Software Stack) initiative benefit from strategically developed software tools. The front cover shows a simulation of advection under twisting rotation that uses high-order finite elements from Livermore’s Modular Finite Element Methods (MFEM) software library and GLVis visualization tool. On the back cover, the logo (also created with GLVis) for the MFEM project illustrates the curved mesh and sub-element resolution used in high-order simulations. MFEM and GLVis are key components of the ECP’s co-design Center for Efficient Exascale Discretizations (CEED) and RADIUSS. MFEM is also part of ECP’s Extreme-Scale Scientific Software Development Kit (xSDK).

97 MATHEMATICS AND COMPUTING↗

First Look Fund - An Early-Stage Aligned Intermediary to Enhance the Flow of Private Capital into Energy Companies (Final Technical Report)

The technologies required to advance society into a cleaner, better future will come from a broad range of scientific disciplines including materials science, chemistry, physics, and biology. The innovations will rely on scientific breakthroughs rather than software innovation and can be referred to as “hardtech.” These new technology developments will have vast implications across most industrial sectors: energy, materials, manufacturing, electronics, life sciences, etc. The majority of these scientific breakthroughs will be produced and developed by startup companies, which are best suited for high-risk, early-stage development work. However, a large funding gap exists between lab-scale scientific research of the startup world and industrial-scale commercialization. Activate Global, Inc (Activate), a 501(c)3 nonprofit, fills one part of this gap with the Activate Fellowship program1.To fully bridge the gap between lab and market, additional and aligned capital sources are needed. Through this project, Activate’s goals were: Research the recent history (2005-2015) of hardtech investing to discover the driving forces behind positive and negative outcomes from the viewpoint of investors.; Create a database of “technology pathways” categorized by specific technology areas and showing what technology milestones would be needed to be attractive to a partner or acquirer and what resources were available to achieve those goals.; Build a financial model supported by the data acquired that would show attractive returns were possible in early-stage hardtech investing.; Create an investment thesis around the financial model to show how attractive returns could be achieved in an early-stage hardtech-focused fund.; Publish a paper with a qualitative comparative analysis to help investors better understand driving factors of outcomes.; Share these findings with asset managers not currently investing in early-stage hardtech to attract partners to execute the investment strategy.; Implement the investment strategy and begin investing in early-stage hardtech companies through a new vehicle: First Look Fund. Through this research, Activate created a database of nearly 200 hardtech companies that exited via an M&A transaction between 2005 and 2015. Much of the data Activate obtained was nonpublic information and therefore, the database was anonymized. Activate also created the technology pathways database to help new early-stage hardtech companies define milestones, technology objectives and potential resources. Activate created a financial model with a legal structure showing that market-rate returns were possible, created an investment thesis around that model, and then shared these findings with potential partners. Finally, Activate’s research was used to publish a paper with Stanford on driving factors behind financial outcomes that will help investors better guide their portfolios. While Activate was not able to launch the First Look Fund during the project timeline, mostly due to the fallout from COVID-19 (global economic shocks decreasing interest in first time funds, inability to meet potential investors in person), the research has created enough interest for Activate to continue pursuing the goals of the project, showing the potential feasibility of the endeavor. Regardless of Activate’s ability to execute on the investment strategy, the resulting research has been downloaded, viewed, and referenced by investors and becomes another tool to encourage investment in the early-stage hardtech space.

14 SOLAR ENERGY↗

Developing satellite ground control software through graphical models

This paper discusses a program of investigation into software development as graphical modeling. The goal of this work is a more efficient development and maintenance process for the ground-based software that controls unmanned scientific satellites launched by NASA. The main hypothesis of the program is that modeling of the spacecraft and its subsystems, and reasoning about such models, can--and should--form the key activities of software development; by using such models as inputs, the generation of code to perform various functions (such as simulation and diagnostics of spacecraft components) can be automated. Moreover, we contend that automation can provide significant support for reasoning about the software system at the diagram level.

Bailin, Sidney↗

Development of output user interface software to support analysis

Data processing software packages such as VSOP and MCNPX are softwares that has been scientifically proven and complete. The result of VSOP and MCNPX are huge and complex text files. In the analyze process, user need additional processing like Microsoft Excel to show informative result. This research develop an user interface software for output of VSOP and MCNPX. VSOP program output is used to support neutronic analysis and MCNPX program output is used to support burn-up analysis. Software development using iterative development methods which allow for revision and addition of features according to user needs. Processing time with this software 500 times faster than with conventional methods using Microsoft Excel. PYTHON is used as a programming language, because Python is available for all major operating systems: Windows, Linux/Unix, OS/2, Mac, Amiga, among others. Values that support neutronic analysis are k-eff, burn-up and mass Pu{sup 239} and Pu{sup 241}. Burn-up analysis used the mass inventory values of actinide (Thorium, Plutonium, Neptunium and Uranium). Values are visualized in graphical shape to support analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Singularity: Scientific containers for mobility of compute

Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.

97 MATHEMATICS AND COMPUTING↗

Envision: An interactive system for the management and visualization of large geophysical data sets

Envision is a software project at the University of Illinois and Texas A&M, funded by NASA's Applied Information Systems Research Project. It provides researchers in the geophysical sciences convenient ways to manage, browse, and visualize large observed or model data sets. Envision integrates data management, analysis, and visualization of geophysical data in an interactive environment. It employs commonly used standards in data formats, operating systems, networking, and graphics. It also attempts, wherever possible, to integrate with existing scientific visualization and analysis software. Envision has an easy-to-use graphical interface, distributed process components, and an extensible design. It is a public domain package, freely available to the scientific community.

Searight, K. R.↗

The DIARieS Ecosystem – A Software Ecosystem to Simplify Discovery, Implementation, Analysis, Reproducibility, and Sharing of Scientific Results and Environments in Heliophysics

The infrastructure of the Heliophysics discipline has promising components but with several missing gaps, drastically reducing research and development efficiency. Developing an online discovery and analysis software ecosystem will close several of these gaps. The five main focuses on this ecosystem should be Discovery, Implementation, Analysis, Reproducibility, and Sharing of results (DIARieS). In this paper, we give a detailed description of how the proposed software ecosystem should operate, and point out the large range of possible applications to benefit many disparate groups, such as researchers, operational staff, decision-makers, and educators. The infrastructure components and technological capabilities necessary for its completion are either currently available or in development, making such an ecosystem possible for the first time. One main focus of current infrastructure investments must be to adapt and connect these pieces together into a cohesive whole to increase our research and development efficiency.

infrastructure↗

Supply Chain Binary Program Assessment Annual Report

Secure environments face a steadily increasing threat from the introduction of scientific equipment that includes extensive software components. Devices such as powerful microscopes, 3d printers, programmable logic controllers, and many others, increase our capability and scientific output. Unfortunately, they now frequently include an entire computing stack with custom software bundled along with entire operating systems. Our ability to introspect and understand binary software is extremely limited, opening up the possibility of malicious third-parties including software that compromises our critical missions.

42 ENGINEERING↗