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DOE OSTI2025

Status of SPCA-ANL Software Development, Software Quality Assurance, and Application (FY2025)

SPCA-ANL is a simulation tool used to perform deterministic analyses of sodium spray and pool fires. Development of the SPCA-II (Spray Pool Combustion Analysis) code began in the mid- 1980s as part of the Clinch River Breeder Reactor (CRBR) Project. At that time, development of SPCA-II, which was led by Rockwell International, was focused on treatment of large-scale sodium spray, stream, and pool fires that were anticipated to be prototypic of the steam generator building cells in CRBR. Under more recent DOE NE programmatic activities, the SPCA-II code was recovered from existing literature and underwent minor modifications to generate a stable executable. This recovered version of the code was not formally released. As part of the Versatile Test Reactor (VTR) Project in the 2010s, the SPCA-II code underwent key modifications to improve stability, address modeling deficiencies, improve consistency between the code manual and software, and address numerous bugs. At this point, SPCA-II was renamed SPCA-ANL. Given that SPCA-II served as the original basis for SPCA-ANL, both codes share an integrated history. Following termination of the VTR Project, the DOE NE Fast Reactor Program resumed support of the software with the goal of building and maintaining software infrastructure that can enable commercial-grade dedication of SPCA-ANL by an end user. Version 1.0, the first external release of SPCA-ANL, was generated in June 2024. This report summarizes the development and maintenance activities completed for SPCAANL in FY2025. This year’s work was focused on improving quality and usability of the code. The provisional Software Quality Assurance (SQA) program has been established and was used to test the procedures for infrastructure improvements, code development, bug fixes, and code releases, as described in the following sections of this report. A code Version 1.0.1 was released in FY25, as described in Chapter 4.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Advancing Scientific Productivity through Better Scientific Software: Developer Productivity and Software Sustainability Report

The Exascale Computing Project (ECP) provides a unique opportunity to advance computational science and engineering (CSE) through an accelerated growth phase in extreme-scale computing. Central to the project is the development of next-generation applications and software technologies that can exploit emerging architectures for optimal performance and provide high-fidelity, multiphysics, multiscale capabilities. However, disruptive changes in computer architectures and the complexities of tackling new frontiers in extreme-scale modeling, simulation, and analysis present daunting challenges to the productivity of software developers and the sustainability of software artifacts. Members of the CSE community - especially at extreme scales but more broadly at all scales of computing - face an urgent need to improve developer productivity, positively impacting product quality, development time, and staffing resources, and software sustainability, reducing the cost of maintaining, sustaining, and evolving software capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Basic Research Needs in The Science of Scientific Software Development and Use: Investment in Software is Investment in Science

Increasingly powerful and affordable computing has revolutionized scientific and scholarly discovery across a broad range of fields. Computing relies on software, which has been rapidly growing in scope, diversity, and complexity. At the same time, the methods, processes, and tools used to produce and utilize this essential software are often ad hoc, and the study and improvement of them are often done without the benefit of direct funding or prioritization. Consequently, concerns are growing about the productivity of the developers and users of scientific software, its sustainability, and the trustworthiness of the results that it produces. Increased investment, especially in the characterization and improvement of how scientific software is developed and used, is important for sustaining and improving the impact of software as the scope and complexity of scientific efforts expand. Without this investment, we face the risk of diminishing returns on our software investments because the demands for increased functionality, usability, reliability, and more will not be sufficiently met. The US Department of Energy Office of Science (DOE/SC) is at the forefront of modern software-enabled scientific discovery across numerous areas of computational, experimental, and observational science, including major investments in national user facilities that support these activities. For many years, DOE/SC software investments have provided tremendous value to the scientific community. We want to continue and further improve the value of DOE/SC software efforts by using a scientific approach to understanding and improving how scientific software is developed and used. In December 2021, the DOE/SC Office of Advanced Scientific Computing Research (ASCR) convened a workshop on basic research needs for the Science of Scientific-Software Development and Use (SSSDU). Through keynote presentations, lightning talks, and breakout groups, which built on insights from 124 pre-workshop position papers, participants discussed the current practice of software development, maintenance, evolution, and use, and considered how the scientific method could be used to examine these practices and develop more evidence-based approaches to enhance the impact of software and computing on all areas of science. Workshop participants identified three priority research directions (PRDs) and three important crosscutting themes that center on the following overarching insight: Software has become an essential part of modern science, impacting discoveries, policy, and technological development. To maintain and improve confidence in science delivered via software, we must improve the processes and tools that help us create and use software, and this enhancement requires a deep understanding of the diverse array of teams and individuals doing the work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Position Papers for the ASCR Workshop on the Science of Scientific-Software Development and Use

Software is an increasingly important component in the pursuit of scientific discovery. Both its development and use are essential activities for many scientific teams. At the same time, very little scientific study has been conducted to understand, characterize, and improve the development and use of software for science. Computational science teams have diversified over time to include contributions from domain scientists who provide expertise in scientific and engineering disciplines, applied mathematicians and computer scientists who provide optimal algorithms and data structures, and software and data engineers who provide methodologies and tools adapted and adopted from other software domains. These diverse contributions have enabled tremendous advances in the pursuit of scientific discovery, even as models, computer architectures, and software environments have become more complicated. With this increasing diversity, we believe the next opportunity for qualitative improvement comes from applying the scientific method to understanding, characterizing, and improving how scientific software is developed and used. We believe that this pursuit requires expertise from computational scientists themselves, and from the cognitive and social sciences as well as the software engineering research community. As we look to increase the productivity and sustainability of the scientific-software-development-and-use cycle, a more systematic application of the scientific method to understand processes for software development and use will be a valuable tool to guide future work and result in more usable and sustainable software. This workshop will bring together computer scientists, software engineering researchers, computational scientists, applied mathematicians, social scientists, cognitive scientists, and others, to explore how we can conduct such systematic investigations, what can be learned, and how doing so will benefit the scientific enterprise. The workshop will be structured around a set of breakout sessions, with every attendee expected to participate actively in the discussions. Afterward, workshop attendees — from DOE, industry, and academia — will produce a report for ASCR that summarizes the findings of the workshop.

42 ENGINEERING↗
DOE OSTI2021

Containerization in ATLAS Software Development and Data Production

The ATLAS experiment's software production and distribution on the grid benefits from a semi-automated infrastructure that provides up-to-date information about software usability and availability through the CVMFS dis-tribution service for all relevant systems. The software development process uses a Continuous Integration pipeline involving testing, validation, packag-ing and installation steps. For opportunistic sites that can not access CVMFS, containerized releases are needed. These standalone containers are currently created manually to support Monte-Carlo data production at such sites. In this paper we will describe an automated procedure for the containerization of AT-LAS software releases in the existing software development infrastructure, its motivation, integration and testing in the distributed computing system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Providing a Flexible and Comprehensive Software Stack Via Spack, an Extreme-Scale Scientific Software Stack, and Software Development Kits

To manage the complex demands of modern high-performance computing (HPC), software applications increasingly depend on software developed by other teams, often at other institutions. An HPC software ecosystem approach is required to support dependencies on third-party scientific software. An ecosystem approach provides layers of activity above the individual software product level that promote interoperability, quality improvement, porting, testing, and deployment. The U.S. Exascale Computing Project (ECP) developed its HPC software ecosystem using a three-pronged approach. First, the ECP adopted and invested in Spack, a package manager designed to handle complex HPC package dependencies. Second, the ECP created the Extreme Scale Scientific Software Stack, an effort that supports developing, deploying, and running scientific applications on HPC platforms. Third, the ECP supported software product communities, or software development kits, to develop and promote best practices, improve software interoperability, and other collaborative efforts. This article describes ECP contributions to HPC software ecosystem challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Perspective on Sustainable Computational Chemistry Software Development and Integration

The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Better together: Elements of successful scientific software development in a distributed collaborative community

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Griffin Software Development Plan

Griffin is a MOOSE-based reactor physics application for advanced reactor multiphysics modeling and simulation. The application is developed in a consistent multiphysics environment with strong software quality assurance. Griffin inherited most of the capabilities of MAMMOTH/Rattlesnake and is adopting the capabilities from PROTEUS that are needed in the code. The toolset includes a variety of deterministic radiation transport solvers for fixed source, k-eigenvalue, ad-joint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. The code contains the cross-section preparation capabilities applicable to fast and thermal reactors, including TRISO-fueled reactors. Core management capabilities include core performance, fuel depletion and shuffling, equilibrium core calculation, pebble-bed reactor run-in and equilibrium core, molten-salt reactor delayed neutron precursor drift, and control rod and drum movement with cusping correction. This software development plan presents the current and future capabilities and features in Griffin for the design and analysis of non-light-water reactor systems in steady-state and transient conditions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

An Agile/XP software development process for modernizing the accelerator control system at Fermilab

Fermilab is undergoing the most ambitious upgrade to its accelerator control system of the 21st century. As part of the ACORN project, hundreds of legacy control system applications written in C/C++ will be re-imagined and developed from the ground up. In addition, applications to support Fermilab’s new super-conducting linear accelerator are already under construction. To manage the development of modern controls applications, the Controls department has adopted an Agile software development process based on eXtreme Programming. In this paper we will describe our process and detail our experience applying it to the development of two case studies.

Diamond, John [Fermilab]↗
DOE OSTI2020

URBANopt: An Open-Source Software Development Kit for Community and Urban District Energy Modeling: Preprint

Urban building modeling tools are developing rapidly; these tools use emerging simulation workflows for specific urban environmental design tasks, such as assessing the impacts of energy efficiency technologies at a district scale. However, with the emergence of new environmental design tasks, addressing all possible use cases and tasks is challenging and cannot be covered by a single tool. Urban-scale analysis at this level of complexity often requires linking multiple emerging tools, rather than using a single tool, to adequately evaluate a variety of possible fields in urban environmental design. To achieve this, flexible platforms are needed to support multiple input formats (e.g., geometric and non-geometric building properties), enabling the mapping of such inputs to underlying simulation engines. This paper provides an overview of the open-source URBANopt Software Development Kit (SDK) for modeling high-performance buildings and energy systems at a district scale. URBANopt's flexible SDK is composed of several modules that can be customized to integrate with other tools and generate new workflows to perform urban environmental design tasks, such as capturing interactions between individual buildings, district energy systems, distributed energy resources, and the electric distribution grid. We describe the functionality of the core SDK modules in URBANopt (called Core Gem, GeoJSON Gem, and Scenario Gem) and discuss the flexibility of these modules as a means of integration with a variety of tools. We also document and demonstrate technical details of writing and combining new modules to create customized workflows. Finally, we present a case study that uses the URBANopt SDK to model a hypothetical mixed-use urban project and simulate various scenarios to meet district energy performance goals.

buildings↗
DOE OSTI2022

The Science of Scientific Software Development and Use

Increasingly powerful and affordable computing has revolutionized scientific and scholarly discovery across a broad range of fields. Computing relies on software, which has been rapidly growing in scope, diversity, and complexity. At the same time, the methods, processes, and tools used to produce and utilize this essential software are often ad hoc, and the study and improvement of them is often done without the benefit of direct funding or prioritization. Consequently, concerns are growing about the productivity of the developers and users of scientific software, its sustainability, and the trustworthiness of the results that it produces. The US Department of Energy Office of Science (DOE-SC) is at the forefront of modern software-enabled scientific discovery across numerous areas of computational, experimental, and observational science, including major investments in national user facilities that support these activities. In December 2021, the DOE-SC Office of Advanced Scientific Computing Research (ASCR) convened a workshop on basic research needs for the Science of Scientific Software Development and Use (SSSDU). Through keynote presentations, lightning talks, and breakout groups, participants discussed the current practice of software development, maintenance, evolution, and use, and considered how the scientific method could be used to examine these practices and develop more evidence-based approaches to enhance the impact of software and computing on all areas of science. Workshop participants identified three priority research directions (PRDs) and three important crosscutting themes that center on the following overarching insight: software has become an essential part of modern science that impacts new discovery, policy, and technological development. To have full confidence in science delivered via software, we must improve the processes and tools that help us create and use it, and this enhancement requires a deep understanding of the diverse array of teams and individuals doing the work. The full workshop report will be available at https://doi.org/10.2172/1846009.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Budget's dynamic vegetation models

Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Gregor, Konstantin [Technical Univ. of Munich (Ger↗
DOE OSTI2022

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich data challenges existing data storage and access technologies, therefore precluding informatics advancements. Here we present MZA™ (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed by a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and efficient programmatic access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Towards a Software Development Framework for Interconnected Science Ecosystems

The innovative science of the future must be multi-domain and interconnected to usher in the next generation of “self-driving” laboratories enabling consequential discoveries and transformative inventions. Such a disparate and interconnected ecosystem of scientific instruments will need to evolve using a system-of-systems (SoS) approach. The key to enabling application integration with such an SoS will be the use of Software Development Kits (SDKs). Currently, SDKs facilitate scientific research breakthroughs via algorithmic automation, databases and storage, optimization and structure, pervasive environmental monitoring, among others. However, existing SDKs lack instrument-interoperability and reusability capabilities, do not effectively work in an open federated architectural environment, and are largely isolated within silos of the respective scientific disciplines. Inspired by the scalable SoS framework, this work proposes the development of INTERSECT-SDK to provide a coherent environment for multi-domain scientific applications to benefit from the open federated architecture in an interconnected ecosystem of instruments. This approach will decompose functionality into loosely coupled software services for interoperability among several solutions that do not scale beyond a single domain and/or application. Furthermore, the proposed environment will allow operational and managerial inter-dependence while providing opportunities for the researchers to reuse software components from other domains and build universal solution libraries. We demonstrate this research for microscopy use-case, where we show how INTERSECT-SDK is developing the tools necessary to enable advanced scanning methods and accelerate scientific discovery.

Malviya, Addi Thakur↗