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At least 469 records · Page 26

rabpro: global watershed boundaries, river elevation profiles, and catchment statistics

River and Basin Profiler (rabpro) is a Python package to delineate watersheds, extract river flowlines and elevation profiles, and compute watershed statistics for any location on the Earth’s surface. As fundamental hydrologically-relevant units of surface area, watersheds are areas of land that drain via aboveground pathways to the same location, or outlet. Delineations of watershed boundaries are typically performed on digital elevation models (DEMs) that represent surface elevations as gridded rasters. Depending on the resolution of the DEM and the size of the watershed, delineation may be very computationally expensive. With this in mind, we designed rabpro to provide user-friendly workflows to manage the complexity and computational expense of watershed calculations given an arbitrary coordinate pair. In addition to basic watershed delineation, rabpro will extract the elevation profile for a watershed’s mainchannel flowline. This enables the computation of river slope, which is a critical parameter in many hydrologic and geomorphologic models. Finally, rabpro provides a user-friendly wrapper around Google Earth Engine’s (GEE) Python API to enable cloud-computing of zonal watershed statistics and/or time-varying forcing data from hundreds of available datasets. Altogether, rabpro provides the ability to automate or semi-automate complex watershed analysis workflows across broad spatial extents.

54 ENVIRONMENTAL SCIENCES↗

Scientific Computational Imaging Code (SCICO)

Scientific Computational Imaging Code (SCICO) is a Python package for solving the inverse problems that arise in scientific imaging applications. Its primary focus is providing methods for solving ill-posed inverse problems by using an appropriate prior model of the reconstruction space. SCICO includes a growing suite of operators, cost functionals, regularizers, and optimization routines that may be combined to solve a wide range of problems, and is designed so that it is easy to add new building blocks. SCICO is built on top of JAX rather than NumPy, enabling GPU/TPU acceleration, just-in-time compilation, and automatic gradient functionality, which is used to automatically compute the adjoints of linear operators. An example of how to solve a multi-channel tomography problem with SCICO is shown in Figure 1. The SCICO source code is available from GitHub, and pre-built packages are available from PyPI. It has extensive online documentation, including API documentation and usage examples, which can be run online at Google Colab and binder.

97 MATHEMATICS AND COMPUTING↗

WATTS: Workflow and template toolkit for simulation

Modeling and simulation in many science and engineering domains often involves the execution and/or iteration of a sequence of applications, with data transfer between applications typically required. These applications often do not have a formal application programming interface (API). Instead, executing an application requires first writing a text-based input file, the format of which is typically defined in a user’s manual. While text-based input files are suitable for simple one-off calculations, they can become cumbersome if a user wants to execute the applications multiple times and systematically vary input parameters, especially when a complex workflow is involved. In this case, they must resort to either manually making changes in the input file or developing their own script that modifies the input file and executes the application. Depending on the format of the input file, writing such a script can be a non-trivial and error-prone task.

97 MATHEMATICS AND COMPUTING↗

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗

hashin_shtrikman_mp: a package for the optimal design and discovery of multi-phase composite materials

hashin_shtrikman_mp is a tool for composites designers who have desired composite properties in mind, but who do not yet have an underlying formulation. The library utilizes the tightest theoretical bounds on the effective properties of composite materials with unspecified microstructure – the Hashin-Shtrikman bounds – to identify candidate theoretical materials, find real materials that are close to the candidates, and determine the optimal volume fractions for each of the constituents in the resulting composite. Its features include (i) leveraging of materials in the Materials Project database, (ii) integration with the Materials Project API, (iii) use of genetic machine-learning, (iv) agnosticism to underlying microstructure, and (v) ultimate engineering application, make it a tool with much broader applications than its predecessors.

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JAXtronomy: A JAX port of lenstronomy

Gravitational lensing is a phenomenon where light bends around massive objects, resulting in distorted images seen by an observer. Studying gravitationally lensed systems provides insights into cosmology and astrophysics, including constraints of the expansion rate of the Universe and the distribution of dark matter. Thus, we introduce JAXtronomy, a re-implementation of the gravitational lensing software package lenstronomy (Birrer, 2021; Birrer & Amara, 2018) using JAX (Bradbury et al., 2018). JAX is a Python library that uses an accelerated linear algebra (XLA) compiler to improve the performance of computing software. Our core design principle of JAXtronomy is to maintain an identical API to that of lenstronomy. The main JAX features utilized in JAXtronomy are just-in-time compilation, which can lead to significant reductions in execution time, and automatic differentiation, which allows for the implementation of gradient-based algorithms that were previously impossible. Additionally, JAX allows code to be run on GPUs or parallelized across CPU cores, further boosting the performance of JAXtronomy.

astronomy↗

Opal: A Centralized Memory Manager for Investigating Disaggregated Memory Systems

Many modern applications have memory footprints that are increasingly large, driving system memory capacities higher and higher. Moreover, these systems are often organized where the bulk of the memory is collocated with the compute capability, which necessitates the need for message passing APIs to facilitate information sharing between compute nodes. Due to the diversity of applications that must run on High-Performance Computing (HPC) systems, the memory utilization can fluctuate wildly from one application to another. And, because memory is located in the node, maintenance can become problematic because each node must be taken offline and upgraded individually. To address these issues, vendors are exploring disaggregated, memory-centric, systems. In this type of organization, there are discrete nodes, reserved solely for memory, which are shared across many compute nodes. Due to their capacity, low-power, and non-volatility, Non-Volatile Memories (NVMs) are ideal candidates for these memory nodes. This report discusses a new component for the Structural Simulation Toolkit (SST), Opal, that can be used to study the impact of using NVMs in a disaggregated system in terms of performance, security, and memory management. This page intentionally left blank.

97 MATHEMATICS AND COMPUTING↗

ASC CSSE Level 2 Milestone #6362: Resilient Asynchronous Many Task Programming Model

This report is an outcome of the ASC CSSE Level 2 Milestone 6362: Analysis of Re- silient Asynchronous Many-Task (AMT) Programming Model. It comprises a summary and in-depth analysis of resilience schemes adapted to the AMT programming model. Herein, performance trade-offs of a resilient-AMT prograrnming model are assessed through two ap- proaches: (1) an analytical model realized by discrete event simulations and (2) empirical evaluation of benchmark programs representing regular and irregular workloads of explicit partial differential equation solvers. As part of this effort, an AMT execution simulator and a prototype resilient-AMT programming framework have been developed. The former permits us to hypothesize the performance behavior of a resilient-AMT model, and has undergone a verification and validation (V&V) process. The latter allows empirical evaluation of the perfor- mance of resilience schemes under emulated program failures and enabled the aforementioned V&V process. The outcome indicates that (1) resilience techniques implemented within an AMT framework allow efficient and scalable recovery under frequent failures, that (2) the abstraction of task and data instances in the AMT programming model enables readily us- able Application Program Interfaces (APIs) for resilience, and that (3) this abstraction enables predicting the performance of resilient-AMT applications with a simple simulation infrastruc- ture. This outcome will provide guidance for the design of the AMT programming model and runtime systems, user-level resilience support, and application development for ASC's next generation platforms (NGPs).

97 MATHEMATICS AND COMPUTING↗

2018 Bryan Mound Baseline Casing Analysis

This report analyzes data from multi-arm caliper (MAC) surveys taken at the Bryan Mound Strategic Petroleum Reserve site to determine baseline statistics for the original innermost cemented casing or the subsequent installed liner. Along with analyzing the internal diameters from the MAC surveys, this analysis looks to approximate casing weight, an important metric for determining the strength of well sections. Casing weight is calculated for each section, survey, and well. Results from the analysis show most wells reflect the dimensions in the original as-built drawings. There are, however, a few exceptions. Some well sections have calculated wall thicknesses outside API tolerance. In addition, some well section depths differ from the as-built drawings. All results are discussed on a well-by-well basis. Where applicable, information from this report should be used to update as-built drawings and aid in creating more accurate well models for future studies.

02 PETROLEUM↗

Policy, Permit, Perform: Using City Benchmarking Data and Building Construction Permit History to Identify Energy Performance Improvements

The City of Portland (City) and Multnomah County 2015 Climate Action Plan targets a 40 percent reduction in carbon emissions below 1990 levels by 2030. To reach this goal, the plan includes a key objective to reduce the total energy use of existing buildings by 25 percent. Buildings are responsible for one-half of carbon emissions in Portland, Oregon, U.S., and improving their performance is critical to achieving the City’s climate goals. As part of its climate action planning, the City passed an ordinance on April 22, 2015, requiring commercial buildings 20,000 ft2 and larger to benchmark and disclose annual energy performance metrics through ENERGY STAR® Portfolio Manager® (ESPM), including greenhouse gas emissions, building energy use intensity (EUI) and ENERGY STAR scores. Twenty-five cities in the U.S. have adopted similar policies requiring energy benchmarking. These policies are generating a treasure trove of data about the drivers of building performance, but practitioners are only beginning to link benchmarking data to other datasets to produce actionable insight into improving building performance. In 2016, the City received a financial assistance award from the U.S. Department of Energy (DOE) through the Cities Leading through Energy Analysis and Planning project (Award Number DE-0007737). Portland’s project evaluated the application of the DOE Building Energy Asset Score Tool and rating system developed by Pacific Northwest National Laboratory (PNNL) to commercial buildings that report to the City. The City used building construction permit history to develop Asset Scores for a set of office buildings that reported energy performance for 2015 – the first year the City mandated energy benchmarking and reporting for commercial buildings 50,000 ft2 and larger. The primary project objective was to link energy benchmarking data and commercial building permit data to analyze commercial buildings systems that present the best, specific opportunities to improve energy performance. Integrating these datasets from two City departments, the Bureau of Development Services and the Bureau of Planning and Sustainability, could expand Portland’s use of analytics to inform climate policy decisions and strategic targeting of financial incentives for building owners and managers who are best positioned to improve the performance of their buildings. The project began with the commercial benchmarking data received for 340 commercial buildings that submitted calendar year 2015 energy reports. The City retained Research Into Action and Energy 350 (RIA team) to evaluate this self-reported benchmarking data. Based on a survey of 53 building managers, the RIA team found that most respondents used the City’s compliance tools, including a How-to Guide and Help Desk, and found access to utility data to be straightforward. However, almost one-third of the respondents encountered challenges when estimating the gross floor area (GFA), and some experienced confusion regarding the input of property use types and details into ESPM. Based on the RIA team’s recommendations, the City refined its How-to Guide, created online answers to frequently asked questions, and hosted office hour sessions to help building managers accurately characterize their building’s physical characteristics and operations in ESPM. Concurrently with the RIA team’s evaluation, the City worked with PNNL to estimate DOE Asset Score Preview (Preview) scores for 181 of the 340 commercial buildings that submitted ESPM reports to the City. Asset Score Tool is a web-based tool developed by PNNL for DOE to evaluate and rate the as-built energy efficiency of a building. Asset Score Tool’s full analysis capability runs a detailed energy model based on building geometry, age, envelope, lighting and mechanical systems using standard operating assumptions to give the building a score, similar to a miles per gallon rating for a car. Preview, a simplified deployment of the full Asset Score Tool, provides a preliminary score range using only seven data points, six of which are readily available in the ESPM benchmarking data. Analysis of the combined Preview scores and the operational ESPM scores identified buildings with a high potential for energy improvements through either retro-commissioning and retuning of existing operations or capital investments into building systems. The City contracted Whole Building Solutions (WBS) to mine building permit history and develop full Asset Scores for a subset of 26 office buildings that were eligible for both ENERGY STAR scores and Asset Scores. WBS researched permits and building plans to identify mechanical, lighting and envelope systems and other building characteristics necessary to complete Asset Scores. This historical data was recorded using the web-based Asset Score Tool, which evaluates the as designed energy efficiency of existing buildings. Much of this City information was available online after 2010; however, WBS had to search through microfiche files and the City’s database to obtain permit and plan history prior to 2010. Although building geometry, mechanical system type and basic envelope information was consistently identifiable, WBS found permit data gaps that prevented complete and accurate Asset Scores for most of the office buildings. If the City decides to develop Asset Scores based on building permit information in the future, WBS recommended requiring lighting plans, control diagrams and as-built drawings that reflect how the buildings are actually constructed. In addition, WBS recommended uploading plans and permits from microfiche storage into a more comprehensive, searchable database consistent with the City’s current online permit information. To identify the actual lighting types, WBS and City staff conducted site visits of all 26 office buildings. As part of managing the flow of ESPM and Asset Score data for the project, Earth Advantage, a sub-recipient of the Cities-LEAP award, created a DOE Standard Energy Efficiency Data (SEED) (USDOE 2018) instance for the City and used Asset Score’s Application Programming Interface (API) (Asset Score 2018) capabilities to develop two new features in the SEED Platform™: (1) automated Asset Score Preview ranges calculated based on benchmarking data and the City’s estimate of number of building floors; and (2) storage of full Asset Score information. Leveraging the construction permit history and site visit information for the 26 office buildings, The Cadmus Group (Cadmus) analyzed benchmarking data and Asset Score information to identify opportunities and drivers to improve performance. Correlation and regression analysis of the combined dataset was completed to determine whether building operations or specific systems – mechanical, lighting or envelope – present the best opportunities to improve energy performance. The central outcome of this project was Cadmus’ detailed analysis of the richer dataset that was created by combining the City of Portland’s commercial building permit activity and building-specific energy performance information based on metrics from ESPM and Asset Score Tool. Some of their findings collaborate with well-known facts, such as buildings with less efficient lighting systems are good targets for efficiency improvements through lighting upgrades. This analysis also helped identify that individual heating, ventilation, and air conditioning (HVAC) equipment efficiency does not necessarily correlate with building performance. However, overall HVAC system performance, as measured with the Total System Performance Ratio (TSPR) metric, is a better indicator of building performance (Goel et al. 2014). As a metric, TSPR considers whole system performance that includes equipment efficiency and system controls, such as resets for static pressure and chilled water setpoints, which can be more effective and financially feasible options for improving building performance. Given the high cost associated with envelope retrofits, opportunities to upgrade envelope insulation are likely to be limited. However, buildings with single-pane and non-thermal break windows are good candidates for window replacements, such as upgrading to double-pane windows with thermal breaks. This research project determined that developing Asset Scores based on site visits is preferable to mining City building permit data. In addition, the use of benchmarking data to develop Asset Score Preview Scores is recommended as a first step in engaging building managers to develop full Asset Scores. Asset Score Tool outputs can help building managers quickly identify opportunities for improvement and then work with the utility incentive administrator and energy service providers to develop their retrofit plan. Where full Asset Scores are available, analysis using a quadrant matrix with ENERGY STAR scores is recommended to identify buildings that present the best opportunities for operational-behavioral improvements versus ones with opportunities for upgrades to physical building systems. This workflow and the tools will help cities, utility incentive administrators, building portfolio managers and energy service providers develop energy programs and strategies for screening and analysis of buildings with the greatest energy savings potential. This report describes the project approach and research results to inform future coordination of ESPM and Asset Score data, including the use of benchmarking data to generate Preview scores. Given better awareness of energy performance, building managers can make more informed decisions to reduce energy consumption and carbon emissions. With cities, states and jurisdictions adopting benchmarking and auditing ordinances, there are substantial amounts of data submitted to cities that could be used to inform investments in energy efficiency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

ATDM/ECP Milestone Memo WBS 2.3.4.04 / SNL ATDM Data and Visualization Projects STDV04-21 - [MS1/YR2] Q3: Prototype Catalyst/ParaView in-situ viz for unsteady RV flow on ATS-1

ParaView Catalyst is an API for accessing the scalable visualization infrastructure of ParaView in an in-situ context. In-situ visualization allows simulation codes to access data post-processing operations while the simulation is running. In-situ techniques can reduce data post-processing time, allow computational steering, and increase the resolution and frequency of data output. For a simulation code to use ParaView Catalyst, adapter code needs to be created that interfaces the simulations data structures to ParaView/VTK data structures. Under ATDM, Catalyst is to be integrated with SPARC, a code used for simulation of unsteady reentry vehicle flow.

97 MATHEMATICS AND COMPUTING↗

Umpire: Integration with Other Software Technologies

Umpire is an open-source C++ library that provides memory and resource management functions for use with heterogeneous memory spaces. The Umpire library is being developed to simplify coordination and sharing of limited memory resources for complex integrated applications developed in the ECP/ATDM and ASC programs, and other multi-physics codes that combine many physics packages and libraries. The overarching goal of Umpire is to provide a single application-facing API for memory management on all platforms by decoupling memory operations from platform-specific memory spaces. It provides operations for: memory space discovery, memory provisioning, allocation and transfers, and introspection. Umpire development began in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory (LLNL). Umpire is funded primarily by LLNL ASC and ATMD portfolios and receives a small amount of support as part of ECP ST Project 2.3.1.06 STMP08-ISC. The source code is hosted and developed on GitHub. In this report, we explore possible integrations with other memory hierarchy management software. We examine both projects supported by ECP, as well as third-party and vendor provided tools.

97 MATHEMATICS AND COMPUTING↗

SunSpec Open Solar Data Exchange (oSDX) (Final Report)

Orange Button is a data interchange standard for solar finance and operations practitioners designed to drive down soft costs. To achieve its goals, SunSpec convened 330+ companies to validate Orange Button functional requirements. Approximately 4,000 industry-expert hours were invested to dissect 25 priority use cases and develop 4,500 Orange Button taxonomy entries and accompanying APIs. Orange Button 1.0 is now available on GitHub royalty-free to the general public. Early adopters include highly influential companies such as Aurora Solar, BayWa r.e., Clean Power Research, DNV-GL, LG, Mercatus, Oracle, Salesforce, sPower, SunPower, and Wells Fargo. Finally, the project was completed on time and on budget, including $2M match funding provided by industry.

14 SOLAR ENERGY↗

Next-Generation Analysis and Visualization Platform for Manufacturing and Engineering Workflows (Final Report)

Advanced manufacturing and engineering are fundamental to the economic strength and national security of the United States. With the introduction of advanced modeling and simulation practices in manufacturing and engineering over the last several decades, we are producing better products, more quickly, and at decreased cost. Through the adoption of high-performance computing (HPC) enabled workflows, in small- to medium-sized manufacturers and engineering firms, which employ fewer than 500 people, is renewing United States manufacturing and engineering. Despite successes, there are significant barriers to widespread adoption of these tools. Our Phase I SBIR project, Next-Generation Analysis and Visualization Platform for Manufacturing and Engineering Workflows, aims to address several of these barriers. Our project helped us evaluate the feasibility and benefits and mitigate risks of asynchronous processing for analysis and visualization workflows. We evaluated the impact of asynchronous, non-blocking communication can have on the user experience in a complex visualization application. Besides the impact on users, we wanted to understand the impacts on developers (development application programming interfaces (APIs))and generated applications (robustness and stability). Any asynchronous platform implementation is bound to rely on multiple threads which can make the programming model extraordinarily complex and prone to bugs. If not managed carefully, the complexity and resulting instability would be an unacceptable evolution for widely used production tools like ParaView and putting the success of the Phase II project at risk.

42 ENGINEERING↗

From Data to Models and Analytics: An Integrated Scientific Computing Platform to Accelerate Environmental Research SBIR Phase I (Final Technical Report)

Phase I research was conducted to investigate the technical feasibility of an integrated open source platform GeoLib to provide turnkey solutions for model-data integration for environmental systems science (ESS), using modern web technologies, advances in HPC and cloud computing and high-performance visualization. A key feature of our platform is that it minimizes and automates data movement between data storage and computing systems. Another critical feature of the design is that it enables users to perform high-fidelity post-processing by performing high-performance visualization server-side and in-situ on the compute cluster close to the data. The investigation, which was performed over a 12-month period ending April 2019, centered on developing prototype software to demonstrate the key enabling technologies required for this system. This included: (i) rapid access to data from multiple remote sources, via a unified data interface; (ii) software for scalable geospatial visualization of raster, vector, and point cloud data on the web; (iii) development of an easy-to-use API for creating simulation and modeling workflows; (iv) remote data visualization and processing on middle-tier servers; and (v) remote data processing on high-performance computing systems. A critical part of this work is that all of these capabilities be accessible to end users through standard desktop tools such as Project Jupyter, which is widely adopted for ESS simulation and modeling.

97 MATHEMATICS AND COMPUTING↗

PHISICS User Manual

PHISICS (Parallel and Highly Innovative Simulation for INL Code System) is a reactor physics package developed at the Idaho National Laboratory. It is composed of several modules: a nodal and semi-structured transport core solver (INSTANT), a depletion module (MRTAU), a time-dependent solver (TimeIntegrator), a cross-section interpolation and manipulation framework (MIXER), a SuperHomogenization scheme (SPH) a criticality search module (CRITICALITY), a fuel management and shuffling component (SHUFFLE) and a coupling API with RELAP5-3D. PHISICS has been built in a highly modular fashion so that different physical models can be activated or deactivated depending on the physics relevant to the particular type of analysis. This approach simplifies development as well as mid- and long-term maintenance of the code. PHISICS is entirely written in FORTRAN 95/2003. Each of its components is fully parallelized and designed to take full advantage of shared-memory computers (multiprocessor desktops and workstations) and middle to large high-performance computing systems (10 to 1000 processors). The aim of this document is to detail the input requirements for PHISICS (and all its modules) focusing on the input structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Sub-Libraries in ORIGEN

Libraries produced for the ORIGEN depletion code have the ability to label nuclides with a sublibrary integer tag. While this is useful for tracking different sources of the nuclides, it adds some complexity, increasing memory usage and computational effort. This report documents the effort of making the sublibraries optional in an ORIGEN calculation, with a focus on the creation of libraries using the LibraryBuilder application programming interface (API). ORIGEN will gain the ability to either use the normal sublibrary set, a reduced, unique, sublibrary set, or no sublibrary at all.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗