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DOE OSTI2020

Open Source Platform for Live Processing of High Data Rate Electron Microscopy (SBIR Phase I Award Topic 15, Subtopic B, Final Scientific / Technical Report)

4D scanning transmission electron microscopy (STEM) is experiencing a revolution both in terms of data size, and data veracity, as are many other areas of experimental data acquisition. This presents enormous opportunities to make new scientific discoveries, but is also hamstrung by aging software that is no longer fit for purpose. Experimental data acquisition platforms must make the same investments in software infrastructure that the simulation community made decades ago to take advantage of high-performance computing (HPC) if they hope to realize the full potential of the latest advances made in detector technology. This requires rethinking the design and deployment of hardware, moving away from small, fixed commodity computing capabilities with closed interfaces to open, modular, scalable systems designed for parallelism and throughput. The technical approach will leverage best-in-class open source technologies, and develop new approaches strategically to build an open source platform for experimental data acquisition targeting high data rate electron microscopy. The project will develop a software platform that uses the power and performance of modern HPC resources to ingest the large volume of data produced by electron microscopes using direct electron detectors. A set of configurable pipelines will be used to enable the processing to be tailored to the problem being investigated with builtin visualization of data at interactive rates. This project will leverage investments made by the DOE and other agencies to provide a powerful software platform for the experimental data community working with most powerful electron microscopes, such as 4D STEM detectors. It will create a platform that serves as a reference for the community. Permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close to where it is generated. This advanced software platform will require customization for each deployment, creating opportunities for software services that are typically offered to extend open source software platforms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

Image-guided mass spectrometry (MS) profiling provides a facile framework for analyzing samples ranging from single cells to tissue sections. The fundamental workflow utilizes a whole-slide microscopy image to select targets of interest, determine their spatial locations, and subsequently perform MS analysis at those locations. Improving upon prior reported methodology, a software package was developed for working with microscopy images. microMS, for microscopy-guided mass spectrometry, allows the user to select and profile diverse samples using a variety of target patterns and mass analyzers. Written in Python, the program provides an intuitive graphical user interface to simplify image-guided MS for novice users. The class hierarchy of instrument interactions permits integration of new MS systems while retaining the feature-rich image analysis framework. microMS is a versatile platform for performing targeted profiling experiments using a series of mass spectrometers. The flexibility in mass analyzers greatly simplifies serial analyses of the same targets by different instruments. The current capabilities of microMS are presented, and its application for off-line analysis of single cells on three distinct instruments is demonstrated. The software has been made freely available for research purposes. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Enhancements supporting IC usage of PEM libraries on next-gen platforms

This milestone reports on the culmination of several years of effort by multiple PEM support software development teams to provide capabilities for use in LLNL-developed integrated codes on next-gen ASC platforms, including GPU support. We will provide a survey of relevant Application Program Interfaces (API) that are required to support LLNL IC code capability on relevant architectures, with a focus on Sierra and El Capitan. We will identify and summarize all dependencies between PEM supported libraries and IC supported physics codes. We will provide an assessment of algorithmic improvements that have been deployed, as well as future developments that are required to complete the GPU porting efforts. This assessment will include a description of programming models adopted by each of the PEM projects, distinct algorithmic challenges for each of the capabilities, and information about sharing GPU memory between the APIs and host codes. We will develop targeted test problems to assess computational performance. Finally, this milestone will result in identification of gaps in our effort to assist the LLNL ASC program in prioritization of effort for porting software to El Capitan.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Collaborative Project: Understanding Climate Model Biases in Tropical Atlantic and Their Impact on Simulations of Extreme Climate Events

Recent studies have revealed that among all the tropical oceans, the tropical Atlantic has experienced the most pronounced warming trend over the 20th century. Many extreme climate events affecting the U.S., such as hurricanes, severe precipitation and drought events, are influenced by conditions in the Gulf of Mexico and the Atlantic Ocean. It is therefore imperative to have accurate simulations of the climatic mean and variability in the Atlantic region to be able to make credible projections of future climate change affecting the U.S. and other countries adjoining the Atlantic Ocean. Unfortunately, almost all global climate models exhibit large biases in their simulations of tropical Atlantic climate. The atmospheric convection simulation errors in the Amazon region and the associated errors in the trade wind simulations are hypothesized to be a leading cause of the tropical Atlantic biases in climate models. As global climate models have resolutions that are too coarse to resolve some of the atmospheric and oceanic processes responsible for the model biases, we propose to use a high-resolution coupled regional climate model (CRCM) framework to address the tropical bias issue. We propose to combine the expertise in tropical coupled atmosphere-ocean modeling at Texas A&M University (TAMU) and the coupled land-atmosphere modeling expertise at Pacific Northwest National Laboratory (PNNL) to develop a comprehensive CRCM for the Atlantic sector within a general and flexible modeling framework. The atmospheric component of the CRCM will be the NCAR WRF model and the oceanic component will be the Rutgers/UCLA ROMS. For the land component, we will use CLM modified at PNNL to include more detailed representations of vegetation and soil hydrology processes. The combined TAMU-PNNL CRCM model will be used to simulate the Atlantic climate, and the associated land-atmosphere-ocean interactions at a horizontal resolution of 9 km or finer. A particular focus of the model development effort will be to optimize the performance of WRF and ROMS over several thousand of cores by focusing on both the parallel communication libraries and the I/O interfaces, in order to achieve the sustained throughput needed to perform simulations on such fine resolution grids. The CRCM model will be developed within the framework of the Coupler (CPL7) software that is part of the NCAR Community Earth System Model (CESM). Through efforts at PNNL and within the community, WRF and CLM have already been coupled via CPL7. Using the flux coupler approach for the whole CRCM model will allow us to flexibly couple WRF, ROMS, and CLM with each model running on its own grid at different resolutions. In addition, this framework will allow us to easily port parameterizations between CESM and the CRCM, and potentially allow partial coupling between the CESM and the CRCM. TAMU and PNNL will contribute cooperatively to this research endeavor. The TAMU team led by Chang and Saravanan has considerable experience in studying atmosphere-ocean interactions within tropical Atlantic sector and will focus on modeling issues that relate to coupling WRF and ROMS. The PNNL team led by Leung has extensive expertise in atmosphere-land interaction and will be responsible for improving the land surface parameterization. Both teams will jointly work on integrating WRF-ROMS and WRF-CLM to couple WRF, ROMS, and CLM through CPL7. Montuoro of the TAMU Supercomputing Center will be responsible for improving the MPI and Parallel IO interfaces of the CRCM. Both teams will contribute to the design and execution of the proposed numerical experiments and jointly perform analysis of the numerical experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗

DOE OSTI2018

Reverse image search for scientific data within and beyond the visible spectrum

The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Integration of RNTuple in ATLAS Athena

After using ROOT’s TTree I/O subsystem for over two decades and storing more than an exabyte of compressed High Energy Physics (HEP) data, advances in technology have motivated a complete redesign, RNTuple, which breaks backward-compatibility to take better advantage of these storage options. The RNTuple I/O subsystem has been designed to address performance bottlenecks and other shortcomings of TTree. Specifically, RNTuple comes with an updated, more compact binary data format that can be stored both in ROOT files and natively in object stores. It is designed for modern storage hardware (e.g. high-throughput low-latency NVMe SSDs), and provides robust and easy to use interfaces. The binary format of RNTuple is scheduled to become production grade in 2024, and recently has become mature enough to start exploring the integration into software used by HEP experiments. In this contribution, we discuss the developments to support the features as required by the ATLAS analysis Event Data Model (EDM) in RNTuple, which will enable its integration into the Athena software framework. With these developments in place, we evaluate the performance of the current most recent versions of RNTuple-based ATLAS data sets and compare this to that of TTree.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Development of flexible, scalable, low cost readout for beam tests of the high granularity calorimeter for the CMS endcap

As part of the development of the High Granularity Calorimeter for the CMS Endcap at HL-LHC, The CMS collaboration is conducting a comprehensive series of beam tests. The first beam test, with a single HGC module was carried out in March of 2016 at the Fermilab Test Beam Facility, continuing to a 16 module test in July of 2016. We describe here the development of a low cost readout system that is simple to implement and is able to grow with the system under test. The system is based on the low cost Zynq SoC that allows simple DAQ development in a Linux environment. For this application we used the Digilent ZedBoard, which allows high speed LVDS links and Linux software development on a single commercial board. A small custom FPGA board designed to comply with the VITA 57 Field Programmable Mezzanine Card standard implements the interface to the readout ASIC mounted on the HGC sensor modules and provides the LVDS links to the ZedBoard, either directly over the FMC connector or via a custom carrier card. This architecture provides scalability, ease of development and low cost.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

MO-F-CAMPUS-T-02: An Electronic Whiteboard Platform to Manage Treatment Planning Process

Purpose: In an effort to improve patient safety and streamline the radiotherapy treatment planning (TP) process, a software based whiteboard had been developed and put in use in our facility Methods: The electronic whiteboard developed using SQL database (DB) and PHP/JavaScript based web interface, is published via department intranet and login credentials. The DB stores data for each TP process such as patient information, plan type, simulation/start dates, physician, dosimetrist, QA and the current status in planning process. Users interact with the DB per plan and perform status updates in real time as the planning process progresses. All user interactions with the DB are recorded with timestamps so as to calculate statistical information for TP process management such as contouring times, planning and review times, dosimetry, physics and therapist QA times. External beam and brachytherapy plans are categorized according to complexity (ex: IMRT, 3D, HDR, LDR etc) and treatment types and applicators. Each plan category is assigned specific timelines for each planning process. When a plan approaches or passes the predetermined timeline, users are alerted via color coded graphical cues. When certain process items are not completed in time, pre-determined actions are triggered such as a delay in treatment start date. Results: Our institution has been using the electronic whiteboard for two years. Implementation of pre-determined actions based on the statistical information collected by the whiteboard improved our TP process. For example, the average time for normal tissue contouring decreased from 0.73±1.37 to 0.24±0.33 days. The average time for target volume contouring decreased from 3.2±2.84 to 2.37±2.54 days. This increase in efficiency allows more time for quality assurance processes, improving patient safety. Conclusion: The electronic whiteboard has been an invaluable tool for streamlining our TP processes. It facilitates timely and accurate communication between all parties involved in the TP process increasing patient safety.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

RUScal : Software for the analysis of resonant ultrasound spectroscopy measurements

Resonant ultrasound spectroscopy is used to nondestructively measure the elastic resonances of small solids to elucidate the material's elastic properties or other qualities like size, shape, or composition. In this work, we introduce the software RUScal for the purpose of determining elastic properties by analyzing the eigenfrequencies of solid specimens with common shapes, such as rectangular parallelepipeds, cylinders (solid and hollow tube), ellipsoids, and octahedrons, as well as irregularly shaped ellipsoids that can be described analytically. All symmetry classes are supported, from isotropic to triclinic, along with the option to add or remove up to three orthogonal mirror planes as well as the ability to reorient the crystal axes with respect the sample edges via Euler angles. Additional features include tools to help find initial sets of elastic constants, including grid exploration and Monte Carlo methods, a tool to analyze frequencies as a function of sample length or crystal orientation, an error analysis tool to assess fit quality, and formatting of the input and output files for batch fitting, e.g., as a function of temperature. This software was validated with published resonant ultrasound spectroscopy data for various materials, shapes, and symmetries with noted improvements in calculation time compared to finite element methods.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

LK Scripting Language [SWR-16-07]

The LK scripting language is a simple and fast computer programming language designed for easy integration with existing software to enable automation of tasks. The LK language is used by NREL’s System Advisor Model (SAM), the SAM Software Development Kit (SDK), and SolTrace products. LK is easy extensible and adaptable to new software due to its small footprint and is designed to be statically linked into other software. It is written in standard C++, is cross-platform (Windows, Linux, and OSX), and includes optional portions that enable direct integration with graphical user interfaces written in the open source C++ wxWidgets Version 3.0+ toolkit.

Source record↗

DOE OSTI2019

NEAMS Workbench and Bison Fuel Performance Remote Application Configuration

The mission of the US Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished using computing architectures that range from laptops to leadership class facilities. The NEAMS Workbench [1,2,3] initiative will facilitate the transition from conventional computer codes to high-fidelity ones by providing a common analysis environment and interface for model creation, review, execution, output review, and visualization for integrated codes. The BISON fuel performance code [4] is a finite element method (FEM)-based software being developed by multiple organizations led by the Idaho National Laboratory for the US Department of Energy Office of Nuclear Energy. BISON can simulate a variety of fuel and cladding compositions and geometries in varying dimensionalities, depending upon the problem type and complexity, including one-dimensional (1D), 1.5D, 2D axially symmetric (2D-rz), 2D radial-azimuthal (2D-rθ), or 3D models. The NEAMS Workbench allows execution of the BISON fuel performance code on local and remote compute resources. In this report, the detailed instructions of configuring the NEAMS Workbench for remote execution of the BISON fuel performance code on a portable batch system (PBS)-scheduled compute resource, as well the transfer of associated Bison files, is illustrated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Addressing qubits with a software-defined radio FPGA (Full Technical Final Report)

Superconducting transmons can be configured as qubits and can also be used for weak signal axion searches. During a prior LDRD (17-ERD-006), a robust capability for system simulation and analysis, algorithm development, algorithm to FPGA workflow and experimental measurements was developed. During that project it was determined that a new software-defined radio FPGA would be a significant improvement in cost, simplicity, and software maintainability over the X6-1000M FPGA plus RF front-end system that had been used. In this Feasibility Study, we successfully developed the interface to the new NI USRP-2954R SDR platform, generated and optimized the VHDL of the existing algorithms, and experimentally tested the new FPGA system on real qubit in the laboratory. These tests showed it had the same SNR on weak measurements as the prior FPGA.

42 ENGINEERING↗

DOE OSTI2024

Myna

The additive manufacturing (AM) community has been developing digital factory tools over the past decade to better leverage the multi-modal process data coming out of the advanced manufacturing process. As a result, numerous databases of additive manufacturing process data exist in the literature and in the archival storage of disparate research groups. While some efforts have been made to create a standard ontology for storing and sharing AM data, in practice a variety of data structures are used to store AM build data, even within a single institution. This causes many problems for maintainability and extensibility when attempting to integrate computational modeling tools with experimental data to either validate models or to provide further insight into results and trends. Myna is a Python-based framework that aims to decrease the effort needed to connect individual computational models to the variety of AM process data that exist in different research groups and institutions. This type of software is sometimes referred to as "middleware" or “glueware,” in that it connects disparate databases and applications into a single computational ecosystem. Instead of maintaining unique interfaces between each application and each database, developers can create a single interface from each application to Myna and thereby gain access to the implemented database connections. Similarly, developing a database connection in Myna provides access to the developed simulation applications. This framework greatly simplifies the maintainability of model applications that rely on experimental data. Using external simulation tools, users will also be able to run pre-configured workflows using the built-in workflow manager. Several examples of input files are provided with Myna for different workflows, including melt pool geometry predictions and detailed melt pool and solidification microstructure predictions.

Knapp, GerryL. [Oak Ridge National Laboratory (ORN↗

DOE OSTI2016

LK Scripting Language

Source record↗

DOE OSTI2016

Short Distance of Nuclei - Mining the Wealth of Existing Jefferson Lab Data - Final Report

Over the last fifteen years of operation, the Jefferson Lab CLAS Collaboration has performed many experiments using nuclear targets. Because the CLAS detector has a very large acceptance and because it used a very open (i.e., nonspecific) trigger, there is a vast amount of data on many different reaction channels yet to be analyzed. The goal of the Jefferson Lab Nuclear Data Mining grant was to (1) collect the data from nuclear target experiments using the CLAS detector, (2) collect the associated cuts and corrections used to analyze that data, (3) provide non-expert users with a software environment for easy analysis of the data, and (4) to search for interesting reaction signatures in the data. We formed the Jefferson Lab Nuclear Data Mining collaboration under the auspices of this grant. The collaboration successfully carried out all of our goals. Dr. Gavalian, the data mining scientist, created a remarkably user-friendly web-based interface to enable easy analysis of the nuclear-target data by non-experts. Data from many of the CLAS nuclear target experiments has been made available on servers at Old Dominion University. Many of the associated cuts and corrections have been incorporated into the data mining software. The data mining collaboration was extraordinarily successful in finding interesting reaction signatures in the data. Our paper Momentum sharing in imbalanced Fermi systems was published in Science. Several analyses of CLAS data are continuing and will result in papers after the end of the grant period. We have held several analysis workshops and have given many invited talks at international conferences and workshops related to the data mining initiative. Our initiative to maximize the impact of data collected with CLAS in the 6-GeV era was very successful. During the hiatus between the end of 6-GeV experiments and the beginning of 12-GeV experiments, our collaboration and the physics community at large benefited tremendously from the Jefferson Lab Nuclear Data Mining effort.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Software Requirements of SNAP for Editing MELCOR 2.2 Models

Applications of the severe accident analysis code MELCOR, developed for the U.S. Nuclear Regulatory Commission (NRC) by Sandia National Laboratories (SNL), have been supported by the graphical user-interface and post-processing suite Symbolic Nuclear Analysis Package (SNAP), developed for the NRC by Applied Programming Technology (APT). With the release of MELCOR 2.2, new user functionality and models have been introduced and an update to the SNAP MELCOR plugin user interface is necessary to access these new features. This document relates all new features introduced into MELCOR to the development team at APT as well as the NRC.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

OpenFlow arbitrated programmable network channels for managing quantum metadata

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-defined network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. Here, we conclude by discussing near-term experimental efforts that can realize SDN’s principles for quantum communication.

97 MATHEMATICS AND COMPUTING↗