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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

BigPanDA monitoring system evolution in the ATLAS Experiment

Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Evolution of the ATLAS TDAQ online software framework towards Phase-II upgrade: Use of Kubernetes as an orchestrator of the ATLAS Event Filter computing farm

The ATLAS experiment at the LHC at CERN continuously evolves its TDAQ system to meet the challenges of new physics goals and technological advancements. As ATLAS prepares for the Phase-II Run 4 of the LHC, significant enhancements in the TDAQ Controls and Configuration (TDAQ-CC) tools have been designed to ensure efficient data collection, processing, and management. This abstract presents the evolution of ATLAS TDAQ-CC system leading up to Phase-II Run 4. As part of the evolution towards Phase-II, Kubernetes has been chosen to orchestrate the Event Filter (EF) farm. By leveraging Kubernetes, ATLAS can dynamically allocate computing resources, scale processing capacity in response to changing data taking conditions and ensure high availability of data processing services. The integration of the Kubernetes with the TDAQ Run Control framework enables perfect synchronisation between the experiment’s data acquisition components and the computing infrastructure. We will discuss the architectural considerations and implementation challenges involved in Kubernetes integration with the ATLAS TDAQ-CC system. We will highlight the benefits of using Kubernetes as an EF farm orchestrator, including improved resource utilization, enhanced fault tolerance, and simplified deployment and management of data processing workflows. In addition, we will report on the extensive testing of Kubernetes that was conducted using a farm of 2500 servers within the experiment data taking environment, demonstrating its scalability and robustness in handling the demands of the ATLAS TDAQ system for Phase-II. The adoption of Kubernetes represents a significant step forward in the evolution of ATLAS TDAQ-CC system, aligning with industry best practices in container orchestration.

Corso Radu, Alina [Univ. of California, Irvine, CA↗
DOE OSTI2025

Neural architecture codesign for fast physics applications

We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method employs neural architecture search and network compression in a two-stage approach to discover hardware efficient models. This approach consists of a global search stage that explores a wide range of architectures while considering hardware constraints, followed by a local search stage that fine-tunes and compresses the most promising candidates. We exceed performance on various tasks and show further speedup through model compression techniques such as quantization-aware-training and neural network pruning. We synthesize the optimal models to high level synthesis code for FPGA deployment with the hls4ml library. Additionally, our hierarchical search space provides greater flexibility in optimization, which can easily extend to other tasks and domains. We demonstrate this with two case studies: Bragg peak finding in materials science and jet classification in high energy physics, achieving models with improved accuracy, smaller latencies, or reduced resource utilization relative to the baseline models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Community composition and abundance of wild bees at row crop-grassland interfaces in west central Nebraska

Abstract Perennial mixed forb and grassland habitats are crucial to conservation of pollinators and connectivity of habitats in intensely farmed landscapes. This study aims to understand the effects of land use on the pollinator community by describing bee abundance, species richness and community composition in perennial conservation grasslands and adjacent annual row crops located in west central Nebraska. In 2022 and 2023, we collected and identified bees via sticky traps at 4 locations (center and edge of adjacent grasslands and crop fields) at 6 replicated sites. We collected 1,768 specimens from sticky traps, resulting in 70 species within 28 genera. Halictidae accounted for 84% of the specimens collected. Bee abundance was influenced by the simple effects of land use (grassland vs. crops), edge adjacency, and the month and year of collection. Differences in bee abundance within a collection date were found mostly in early 2022 (May and June) and late 2023 (September), when the crop center location was generally the lowest, with some evidence for spillover of bees from the grassland into the crop edge during the early summer months. Bee species richness was affected only by month and was not significantly different by land use and edge adjacency. Bee community composition overlapped across the 4 locations, although there were significant dissimilarities between crop fields and grasslands. Surveys of the plant community revealed very low abundance of blooming stems and plant taxonomic richness at crop locations for all sampling periods, while grassland locations were comparatively high and varied over time. Plant communities showed no overlap between crop field and grassland locations. Overall, we found that conservation grasslands, while not seeded specifically with pollinator-attractive forbs, provide diverse resources to support wild bee communities in west central Nebraska; crop edges may also provide non-plant resources such as nesting sites and irrigation water. Going forward, better understanding pollinator species composition and resource utilization relative to land use characteristics and drought conditions will allow for better tailoring of conservation efforts and management strategies in Nebraska and across the larger region.

Entomology↗
DOE OSTI2026

Customized Bayesian optimization for efficient beam tuning at the facility for rare isotope beams

Bayesian optimization (BO) has recently emerged as a powerful approach for on-line beam tuning, and it is rapidly gaining adoption across accelerator facilities due to its flexibility and efficiency in handling complex optimization tasks. At the Facility for Rare Isotope Beams, rapid and reliable tuning is essential to support the delivery of diverse ion species. To improve the practicality of BO in this setting, we implemented several enhancements, including scalarized composite objective construction for multicriteria optimization, asynchronous evaluation for better resource utilization, prior-mean-assisted optimization to accelerate convergence, and a local search strategy for rapid completion of the task. We present the details of these methods, discuss challenges-encountered, and share our experience applying them to specific beam-tuning tasks.

Accelerators & storage rings↗
DOE OSTI2024

Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. Here, we develop particle-flow neural-assisted simulations (parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the Compact Muon Solenoid (CMS) experiment. We show that parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

The seventh blind test of crystal structure prediction: structure ranking methods

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol −1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

Chemistry↗
DOE OSTI2025

Dynamic Boundary Microgrids Under Privatization Considerations

Microgrids have physical, electrical, and logical (data, network, and ownership) boundaries. To power unserved customer loads during an outage, microgrids can extend the traditional operational boundaries. This can become complex when considering microgrid-to-microgrid (M2M) interactions where sensitive information such as competitive microgrid operational data is not shared. This work proposes an optimization method coordinated between microgrid controllers and distribution management systems that limits data sharing. The method involves a competitive bidding strategy that maximizes unserved load coverage while minimizing resource utilization and sensitive operational data sharing among entities. The work is validated on a two-microgrid system with photovoltaic and energy storage systems and curves of load derived from real world residential buildings datasets. Results show that the proposed method, when applied for three distinct use cases of energy storage sufficiency to cover the predefined boundary and/or the expanded boundary, can successfully select and bid the available load coverage.

Starke, Michael [ORNL] (ORCID:0000000221211195)↗
DOE OSTI2024

Integrating Quantum Computing with High-Performance Computing: A Streamlined Approach

In recent years, quantum computing has demon-strated the potential to revolutionize specific algorithms and applications by solving problems exponentially faster than classical computers. However, its widespread adoption for general computing remains a future prospect. This paper discusses the integration of quantum computing within High-Performance Computing (HPC) environments, focusing on a resource management framework designed to streamline quantum simulators' use and enhance runtime performance and efficiency. The proposed framework facilitates hybrid applications' transition from simulation backends to real quantum hardware, optimizing resource utilization and providing a flexible infrastructure for developing and testing quantum algorithms.

Shehata, Amir↗
DOE OSTI2024

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]↗
DOE OSTI2025

Improving leaf spring phenology modelling for temperate tree species: An integration of the Farquhar–Medlyn photosynthesis model with the optimality‐based approach

Spring leaf phenology in temperate tree species is highly sensitive to climate change and significantly affects plant photosynthetic performance, resource utilization, competition and trophic interactions, thereby impacting various ecosystem functions. Although optimality-based (OPT) approaches for modelling spring phenology are increasingly recognized, the optimal representation of the underlying principle (balancing photosynthesis gains with chilling risks) remains controversial. Here, we integrated a coupled Farquhar–Medlyn photosynthesis model into an existing OPT model, and termed the resulting model R-OPT, and evaluated its performance using the PEP725 dataset, which includes 409,144 site-species-year records from across Europe. Our results show that R-OPT outperforms both the default OPT and non-optimality-based models (e.g. the chilling-forcing trade-off and growing degree day models). This improved performance is consistent within and across five focal tree species but varies by region: R-OPT excels in lowland, moist environments but is less effective in high-altitude, cold, and dry areas, possibly due to an incomplete representation of environmental constraints on photosynthetic carbon gain in these regions. Our research advances leaf spring phenology modelling by emphasizing an optimality principle that balances photosynthetic carbon gain with chilling risk, improving the representation of plant photosynthesis processes and enhancing understanding of environmental factors influencing phenology in the context of climate change.

coupled Farquhar–Medlyn photosynthesis model↗
DOE OSTI2025

S-QGPU: Shared quantum gate processing unit for distributed quantum computing

We propose a distributed quantum computing (DQC) architecture in which individual small-sized quantum computers are connected to a shared quantum gate processing unit (S-QGPU). The S-QGPU comprises a collection of hybrid two-qubit gate modules for remote gate operations. In contrast to conventional DQC systems, where each quantum computer is equipped with dedicated communication qubits, S-QGPU effectively pools the resources (e.g., the communication qubits) together for remote gate operations, and, thus, significantly reduces the cost of not only the local quantum computers but also the overall distributed system. Our preliminary analysis and simulation show that S-QGPU's shared resources for remote gate operations enable efficient resource utilization. When not all computing qubits (also called data qubits) in the system require simultaneous remote gate operations, S-QGPU-based DQC architecture demands fewer communication qubits, further decreasing the overall cost. Alternatively, with the same number of communication qubits, it can support a larger number of simultaneous remote gate operations more efficiently, especially when these operations occur in a burst mode.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling

Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate sched- ulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understand the impact of parameter configurations and scheduling decisions on the physical assets, even before deployment, or regarching changes not easily realizable in production. We (1) provide the first digital twin framework extended with scheduling capabilities, (2) integrate various top-tier HPC systems given their publicly available datasets, (3) implement extensions to integrate external scheduling simulators. Finally, we show how to (4) implement and evaluate incentive structures, as- well-as (5) evaluate machine learning based scheduling, in such novel digital-twin based meta-framework to prototype scheduling. Our work enables what-if scenarios of HPC systems to evaluate sustainability, and the impact on the simulated system.

Maiterth, Matthias [ORNL] (ORCID:000000018698460X)↗
DOE OSTI2025

ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement

Agentic Retrieval-Augmented Code Synthesis with Iterative RefinementIn supercomputing, efficient and optimized code generation is essential to leverage high-performance systems effectively. We have developed Agentic Retrieval-Augmented Code Synthesis (ARCS), an advanced framework for accurate, robust, and efficient code generation, completion, and translation. ARCS integrates Retrieval-Augmented Generation (RAG) with Chain-of-Thought (CoT) reasoning to systematically break down and iteratively refine complex programming tasks. An agent-based RAG mechanism retrieves relevant code snippets, while real-time execution feedback drives the synthesis of candidate solutions. This process is formalized as a state-action search tree optimization, balancing code correctness with editing efficiency. Evaluations on the Geeks4Geeks and HumanEval benchmarks demonstrate that ARCS significantly outperforms traditional prompting methods in translation and generation quality. By enabling scalable and precise code synthesis, ARCS offers transformative potential for automating and optimizing code development in supercomputing applications, enhancing computational resource utilization

Bhattarai, Manish [Los Alamos National Labs]↗
DOE OSTI2025

An HPC benchmark survey and taxonomy for characterization

The field of High-Performance Computing (HPC) is defined by providing computing devices with highest performance for a variety of demanding scientific users. The tight co-design relationship between HPC providers and users propels the field forward, paired with technological improvements, achieving continuously higher performance and resource utilization. A key device for system architects, architecture researchers, and scientific users are benchmarks, allowing for well-defined assessment of hardware, software, and algorithms. Many benchmarks exist in the community, from individual niche benchmarks testing specific features, to large-scale benchmark suites for whole procurements. We survey the available HPC benchmarks, summarizing them in table form with key details and concise categorization, also through an interactive website. For categorization, we present a benchmark taxonomy for well-defined characterization of benchmarks.

Benchmarking↗
DOE OSTI2025

Evaluating Function-as-a-Service (FaaS) frameworks for the Accelerator Control System

As particle accelerator control systems evolve in complexity and scale, the need for responsive, scalable, and cost-effective computational infrastructure becomes increasingly critical. Function-as-a-Service (FaaS) offers an alternative to traditional monolithic architecture by enabling event-driven execution, automatic scaling, and fine-grained resource utilization. This paper explores the applicability and performance of FaaS frameworks in the context of a modern particle accelerator control system, with the objective of evaluating their suitability for short lived and triggered workloads. In this paper, we evaluate prominent open-source FaaS platforms in executing functional logic, triggers, and diagnostics routines. Evaluation metrics consist of cold-start latency, scalability, performance, integration with other open-source tools like Kafka. Experimental workloads were designed to simulate real-world control tasks when implemented as stateless FaaS functions. These workloads were benchmarked under various invocation loads and network conditions. Self-hosted FaaS platforms, when deployed within accelerator networks, offer greater control over execution environment, better integration with legacy systems, and support for real-time guarantees when paired with message queues. Based on lessons learned and evaluation metrics, this paper describes reliability of the FaaS framework for the Accelerator Control Systems (ACS).

Jaikar, A. [Fermilab] (ORCID:0000000332046217)↗
DOE OSTI2024

Establishing and Maintaining Engagement for a Distributional Equity Analysis [Slides]

The Distributional Equity Analysis (DEA) is an analytical framework that allows for the evaluation of the distributional equity of utility resource investments in combination with benefit-cost analysis. This guide serves as a resource for organizations to develop an engagement strategy for a DEA that is accessible, inclusive, and grounded in community interests. It is organized by the guiding principles of engagement for a DEA, summarized in the figure on the right, and offers a compendium of resources for further support and learning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

​​Integrity Enhancing Protocols (IEP) Evaluation Framework​

​Protocols play an essential role in Advance Reactor systems. A diverse set of protocols are available to these reactors. Advanced Reactors benefit from technologies that can minimize their resource utilization and costs. Evaluation frameworks are often used when assessing protocols and processes related to cryptographic security systems. The following report discusses the various characteristics associated with these protocol evaluation frameworks, and derives a novel evaluative framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗