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DOE OSTI2025

dCache: The Storage System of Choice for Data-Intensive Applications

The ever-increasing volumes of data produced by modern scientific facilities like EuXFEL and LHC put significant stress on data management infrastructure operated by laboratories and research centers. The challenges to be addressed span the entire data life cycle, from ingest and efficient data analysis to long-term preservation, typically involving large tape libraries. dCache, a storage system developed in collaboration between the Deutsches Elektronen-Synchrotron (DESY), Fermi National Accelerator Laboratory, and Nordic e-Infrastructure Collaboration (NeIC), is designed to manage a large number of disk servers and to facilitate transparent data migration to and from archival storage. Its multifaceted approach offers a unified method to support a variety of scientific use cases with the same storage infrastructure, including high-throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters, and long-term data preservation on tertiary storage. Initially developed for high energy physics (HEP) experiments, dCache is now used by various scientific communities, including astrophysics, biomedical research, and life sciences, each having specific requirements. This paper presents architecture, deployment strategies, performance and scalability enhancements, and recent advancements in dCache addressing the needs of scientific communities. Finally, we touch on the development and release process, ensuring the software’s high quality.

DCache↗

DOE OSTI2022

A Visual Comparison of Silent Error Propagation

High-performance computing (HPC) systems play a critical role in facilitating scientific discoveries. Their scale and complexity (e.g., the number of computational units and software stack) continue to grow as new systems are expected to process increasingly more data and reduce computing time. However, with more processing elements, the probability that these systems will experience a random bit-flip error that corrupts a program's output also increases, which is often recognized as silent data corruption. Analyzing the resiliency of HPC applications in extreme-scale computing to silent data corruption is crucial but difficult. An HPC application often contains a large number of computation units that need to be tested, and error propagation caused by error corruption is complex and difficult to interpret. Here, to accommodate this challenge, we propose an interactive visualization system that helps HPC researchers understand the resiliency of HPC applications and compare their error propagation. Our system models an application's error propagation to study a program's resiliency by constructing and visualizing its fault tolerance boundary. Coordinating with multiple interactive designs, our system enables domain experts to efficiently explore the complicated spatial and temporal correlation between error propagations. At the end, the system integrated a nonmonotonic error propagation analysis with an adjustable graph propagation visualization to help domain experts examine the details of error propagation and answer such questions as why an error is mitigated or amplified by program execution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Designing FAIR Workflows at OLCF: Building Scalable and Reusable Ecosystems for HPC Science

High Performance Computing (HPC) centers, such as the Oak Ridge Leadership Computing Facility (OLCF), provide advanced infrastructure that enables scientific research at extreme scale. These centers operate with unique hardware configurations, specialized software environments, and elevated security re quirements that differ substantially from what most users encounter on their local systems. As a result, users often develop customized digital artifacts that are tightly coupled to the specific configuration of a given HPC center. Although necessary, this practice can lead to significant duplication of effort as multiple users independently create similar solutions to common problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence (University of Delaware)

This report summarizes the activities, technical accomplishments, and outcomes of the RAPIDS2 Institute project at the University of Delaware (UD). The RAPIDS2 Institute was a large multi-institution project with the objective of assisting SciDAC and Office of Science application teams in the use of DOE supercomputing resources to achieve scientific breakthroughs. The UD team contributed to this effort through work on formal software verification. This thrust aims to reduce software developer time and effort, especially regarding debugging and testing, and to increase confidence in the correctness of the results computed by the software.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

DuraMAT Data Hub

The DuraMAT Data Hub has been supporting the consortium for the past six years. The Data Hub has had success in supporting the projects, providing a platform for sharing data within projects and to the public, and learning how to better leverage the existing software platform and the available Amazon Web Services environment. During this new generation of the Data Hub, we are looking at ways to help improve the data hub architecture, user experience, and improve operations by taking advantage of new technology platforms and software that will be more impactful on the consortium researchers and the broader scientific community. In this poster we will look at the current operational capabilities, data dissemination, and development that will improve the system in the near and far future.

14 SOLAR ENERGY↗

DOE OSTI2025

SITCOMTN-154: Initial studies of photometric redshifts with LSSTComCam from DP1

This technote holds reports based on the first analyses of the Data Preview 1 (DP1) data by the Science Unit for photometric redshifts. Although photometric redshifts are not an official DP1 data product, the "Photo-z Science Unit" generated photo-z estimates for every galaxy in DP1 using the available multi-band imaging on a best-effort basis. This work included developing training and test datasets by matching DP1 data to high-quality reference redshifts obtained with spectroscopy, Grism data, and multi-band photometry. The Science Unit used the RAIL software package to make photometric redshift estimates using eight different algorithms, developed simple scientific performance metrics, used those metrics to explore how the performance of the algorithms varied with configuration changes, derived more optimized configurations of the algorithms and tested the performance of those configurations. This work, the resulting data products and expected data distribution mechanism are all described there.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1979

Design of a verifiable subset for HAL/S

An attempt to evaluate the applicability of program verification techniques to the existing programming language, HAL/S is discussed. HAL/S is a general purpose high level language designed to accommodate the software needs of the NASA Space Shuttle project. A diversity of features for scientific computing, concurrent and real-time programming, and error handling are discussed. The criteria by which features were evaluated for inclusion into the verifiable subset are described. Individual features of HAL/S with respect to these criteria are examined and justification for the omission of various features from the subset is provided. Conclusions drawn from the research are presented along with recommendations made for the use of HAL/S with respect to the area of program verification.

Browne, J. C.↗

NASA NTRS2002

Science Goal Driven Observing and Spacecraft Autonomy

Spacecraft autonomy will be an integral part of mission operations in the coming decade. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will meet the science goals of a program, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the onboard software can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download a subset of observations at a reduced resolution, the spacecraft's overall efficiency will be dramatically increased. The science goal monitoring (SGM) system is a proof- of-concept effort to address the above challenge. The SGM will have an interface to help capture higher-level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. We are developing an interactive distributed system that will use on-board processing and storage combined with event-driven interfaces with ground-based processing and operations, to enable fast re-prioritization of observing schedules, and to minimize time spent on non-optimized observations. This paper will focus on our strategy for developing SGM and the technical challenges that we have encountered. We will discuss the SGM architecture as it applies to the proposed MIDEX-class mission Kronos. However, the architecture and interfaces will also be designed for easy adaptability to other observing platforms, including ground-based systems and to work with different scheduling and pipeline processing systems.

Jones, Jeremy↗

NASA NTRS2002

Science Goal Driven Observing and Spacecraft Autonomy

Spacecraft autonomy will be an integral part of mission operations in the coming decade. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will meet the science goals of a program, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the onboard software can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download a subset of observations at a reduced resolution, the spacecraft's overall efficiency will be dramatically increased. The science goal monitoring (SGM) system is a proof-of-concept effort to address the above challenge. The SGM will have an interface to help capture higher level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. We are developing an interactive distributed system that will use on-board processing and storage combined with event-driven interfaces with ground-based processing and operations, to enable fast re-prioritization of observing schedules, and to minimize time spent on non-optimized observations.

Koratkar, Amuradha↗

NASA NTRS2009

Virtual Planetary Analysis Environment for Remote Science

All of the data for NASA's current planetary missions and most data for field experiments are collected via orbiting spacecraft, aircraft, and robotic explorers. Mission scientists are unable to employ traditional field methods when operating remotely. We have developed a virtual exploration tool for remote sites with data analysis capabilities that extend human perception quantitatively and qualitatively. Scientists and mission engineers can use it to explore a realistic representation of a remote site. It also provides software tools to "touch" and "measure" remote sites with an immediacy that boosts scientific productivity and is essential for mission operations.

Keely, Leslie↗

NASA NTRS2020

Data Assimilation Enhancements to Air Force Weather’s Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoD’s Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoD’s only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information.

Wegiel, Jerry↗

DOE OSTI2018

HPC Software Verification in Action: A Case Study with Tensor Transposition

As HPC platforms get increasingly complex, the complexity of software optimized for these platforms has also increased. There is a pressing need to ensure correctness of scientific applications to enhance our confidence in the results they produce. In this paper, we focus on checking the correctness of libraries providing a small but important functionality— tensor transposition—used in computational chemistry applications. While several correctness tools have been developed and deployed, there are several practical challenges in using them to verify production HPC software. We present our experiences using two tools—CIVL and CodeThorn—in checking the correctness of two index permutation libraries.

Model checking, equivalence checking, index permut↗

NASA NTRS2009

Autonomous Instrument Placement for Mars Exploration Rovers

Autonomous Instrument Placement (AutoPlace) is onboard software that enables a Mars Exploration Rover to act autonomously in using its manipulator to place scientific instruments on or near designated rock and soil targets. Prior to the development of AutoPlace, it was necessary for human operators on Earth to plan every motion of the manipulator arm in a time-consuming process that included downlinking of images from the rover, analysis of images and creation of commands, and uplinking of commands to the rover. AutoPlace incorporates image analysis and planning algorithms into the onboard rover software, eliminating the need for the downlink/uplink command cycle. Many of these algorithms are derived from the existing groundbased image analysis and planning algorithms, with modifications and augmentations for onboard use.

Leger, P. Chris↗

DOE OSTI2014

New Software Architecture Options for the TCL Data Acquisition System

The Turbulent Combustion Laboratory (TCL) conducts research on combustion in turbulent flow environments. To conduct this research, the TCL utilizes several pulse lasers, a traversable wind tunnel, flow controllers, scientific grade CCD cameras, and numerous other components. Responsible for managing these different data-acquiring instruments and data processing components is the Data Acquisition (DAQ) software. However, the current system is constrained to running through VXI hardware—an instrument-computer interface—that is several years old, requiring the use of an outdated version of the visual programming language, LabVIEW. A new Acquisition System is being programmed which will borrow heavily from either a programming model known as the Current Value Table (CVT) System or another model known as the Server-Client System. The CVT System model is in essence, a giant spread sheet from which data or commands may be retrieved or written to, and the Server-Client System is based on network connections between a server and a client, very much like the Server-Client model of the Internet. Currently, the bare elements of a CVT DAQ Software have been implemented, consisting of client programs in addition to a server program that the CVT will run on. This system is being rigorously tested to evaluate the merits of pursuing the CVT System model and to uncover any potential flaws which may result in further implementation. If the CVT System is chosen, which is likely, then future work will consist of build up the system until enough client programs have been created to run the individual components of the lab. The advantages of such a System will be flexibility, portability, and polymorphism. Additionally, the new DAQ software will allow the Lab to replace the VXI with a newer instrument interface—the PXI—and take advantage of the capabilities of current and future versions of LabVIEW.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2016

MONTE: the Next Generation of Mission Design and Navigation Software

The Mission Analysis, Operations and Navigation Toolkit Environment (MONTE) is an astrodynamic toolkit produced by the Mission Design and Navigation Software Group at the Jet Propulsion Laboratory. It provides a single integrated environment for all phases of deep space and Earth orbiting missions. Capabilities include: trajectory optimization and analysis, operational orbit determination, flight path control, and 2D/3D visualization. MONTE is presented to the user as an importable Python language module. This allows a simple but powerful user interface via CLUI or script. In addition, the Python interface allows MONTE to be used seamlessly with other canonical scientific programming tools such as SciPy, NumPy, and Matplotlib. MONTE is the prime operational orbit determination software for all JPL navigated missions.

Optimization↗

DOE OSTI2024

Integrating quantum computing resources into scientific HPC ecosystems

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era’s inherent external noise issues. Here, this paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC) systems. By leveraging a broad spectrum of simulators and hardware technologies, we propose a hardware-agnostic framework for augmenting classical HPC with QC capabilities. Drawing on the HPC expertise of the Oak Ridge National Laboratory (ORNL) and the HPC lifecycle management of the Department of Energy (DOE), our approach focuses on the strategic incorporation of QC capabilities and acceleration into existing scientific HPC workflows. This includes detailed analyses, benchmarks, and code optimization driven by the needs of the DOE and ORNL missions. Our comprehensive framework integrates hardware, software, workflows, and user interfaces to foster a synergistic environment for quantum and classical computing research. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Is the Supporting Information the Venue for Reproducibility and Transparency?

Making research data, software, and data processing tools readily available to the public could significantly enhance the impact of scientific publications. Openness and transparency could address reproducibility concerns and accelerate scientific progress.(1, 2) While archives and repositories would be preferable for securing data in the long term, the Supporting Information (SI) already allocates significant space to provide auxiliary files, links, and essential information needed to make scientific findings immediately reproducible as well as data processing protocols and numerical procedures executable. Furthermore, the SI document could ensure that data that might not fit in the tight confines of a journal article lives online even when laboratories move on to other projects, close, and lose track of their data. The change for openness and transparency might be difficult to embrace, depending on the specific nature of the project. Nevertheless, the trend to make raw research data and open-source software packages available to the public has been building steady momentum. Openness in research data and software sharing is already making transformative contributions to the communication of research findings, critical for ensuring reproducibility as well as training of the next generation of scientists.

Rudshteyn, Benjamin↗

DOE OSTI2017

Is the Supporting Information the Venue for Reproducibility and Transparency?

Rudshteyn, Benjamin↗