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DOE OSTI2023

Slicing with deep learning models at ProtoDUNE-SP

DUNE is a cutting-edge experiment aiming to study neutrinos in detail, with a special focus on the flavor oscillation mechanism. The prototype of the DUNE Far Detector Single Phase TPC (ProtoDUNE-SP) was built and operated at CERN with a full set of reconstruction tools. To implement these reconstruction tools, Pandora, a multi-algorithm framework, has been developed. A large number of these algorithms, some of them being exploiting traditional clustering, detector physics and deep learning approaches, have been applied to images to gradually build up a picture out of singular events. One of such algorithms is the Pandora slicing algorithm which aims to partition the detector hits of an event in sets called slices. Each slice represents a single interaction in the detector and should identify all the hits related to the interacting particle and its subsequent decay products. We expect the order of tens of slices per event in ProtoDUNE-SP. In this paper we present a deep learning approach to the problem, designing a model able to outperform the state-of-the-art slicing algorithm which is currently implemented within Pandora. We assess the performance of our tool in terms of efficiency and accuracy, while exploiting hardware accelerating setups. The ultimate goal is to incorporate this deep learning approach in the Pandora reconstruction tool.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

4-Clique network minor embedding for quantum annealers

Quantum annealing is a quantum algorithm for computing solutions to combinatorial optimization problems. This study proposes a method for minor embedding optimization problems onto sparse quantum annealing hardware graphs called 4-clique network minor embedding. This method is in contrast to the standard minor embedding technique of using a path of linearly connected qubits in order to represent a logical variable state. The 4-clique minor embedding is possible on Pegasus graph connectivity, which is the native hardware graph for some of the current D-Wave quantum annealers. The Pegasus hardware graph contains many cliques of size 4, making it possible to form a graph composed entirely of paths of connected 4-cliques on which a problem can be minor-embedded. The 4-clique chains come at the cost of additional qubit usage on the hardware graph, but they allow for stronger coupling within each chain, thereby increasing chain integrity, reducing chain breaks, and allow for greater usage of the available energy scale for programming logical problem coefficients on current quantum annealers. The 4-clique minor embedding technique is compared with the standard linear path minor embedding with experiments on two D-Wave quantum annealing processors with Pegasus hardware graphs. We show proof-of-concept experiments where the 4-clique minor embeddings can use weak chain strengths while successfully carrying out the computation of minimizing random all-to-all spin glass problem instances. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Particle-Tracking Proton Computed Tomography—Data Acquisition, Preprocessing, and Preconditioning

Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.

42 ENGINEERING↗

DOE OSTI2022

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

In the graph data structure like adjacency matrix, the connectivity of two nodes can be sufficiently represented using only 1 bit, but they are generally treated as 32-bit full-precision in state-of-the-art graph frameworks to adopt common sparse format such as CSR. Meanwhile, bit-level parallelism has recently be explored to have high-performance potential and low storage requirement on GPUs with dense bit-tiles. To fill the gap, our solution is a hierarchical storage format that contains the bit-indexing base and dense bit-tile units. Inherently, the granularity of the bit-tile is an essential factor in achieving both storage compression and GPU parallelism. How to find a sweet spot that trades off between avoiding sparsity and exploiting is comprehensively researched in this work. In the experiment, we evaluate the proposed storage format and algorithms on modern generation GPUs, including Pascal and Volta, to figure out critical software co-designs in conjunction with existing hardware-specific optimization.

Chen, Jou-An↗

DOE OSTI2025

FloatGuard: Efficient Whole-Program Detection of Floating-Point Exceptions in AMD GPUs

FloatGuard is a tool that captures floating-point exceptions in AMD HIP kernels. FloatGuard leverages AMD GPU hardware registers to detect floating-point exceptions, overcoming the limitations of AMD's built-in trapping mechanisms through a novel algorithm that combines assembly- and source-level instrumentation with debugger-guided execution.

MIAO, WENJUN [Lawrence Livermore National Laborato↗

DOE OSTI2021

JLAB LLRF 3.0 Development and Tests

The Jefferson Lab LLRF 3.0 system is being developed to replace legacy LLRF systems in the CEBAF accelerator. The new design builds upon 25 years of design and operational RF control experience, and our recent collaboration in the design of the LCLSII LLRF system. The new cavity control algorithm is a fully functional phase and amplitude locked Self Exciting Loop (SEL). This paper discusses the progress of the LLRF 3.0 hardware design, FPGA firmware development, User Datagram Protocol (UDP) operation, and recent LLRF 3.0 system tests on the CEBAF Booster cryomodule in the Upgrade Injector Test Facility (UITF).

Plawski, T. E.↗

DOE OSTI2022

Simulating the X-ARAPUCA, DUNE's next generation light sensors

The word arapuca refers to a bird trap built by the indigenous peoples of Brazil. On the other hand, our ARAPUCA is a photon trap for Noble Gases' scintillation light and it is the sensitive element upon which DUNE's whole photon detection system is based on. Here we present a glimpse of our state-of-the-art simulation of such device, the ArapucaSim, highlighting its philosophy and main components. As an application, we present here one of the studies being carried out, on the effects of light reflections inside the light guide and how affects the overall photon detection efficiency. Based on these results, we are able to not only acquire a better understanding of the device but also impose limits on the quality of the mechanical polishing of such surfaces. The ArapucaSim was developed with two main goals in mind: to assist in R&D by allowing short turnout times between new ideas and experimental tests, and to bridge the gap between high-precision time-consuming hardware simulations and the full-scale models for DUNE's Photon Detection System. This research also led to the development new algorithms for simulating wavelength shifting/scintillation processes and dichroic filters, which will be properly described in future publications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

A Review of Quantum Computing Technologies in Power System Optimization

As modern power grids increasingly integrate variable renewable generation, distributed energy resources, and energy storage systems, classical optimization techniques are facing unprecedented challenges. This review examines the emerging application of quantum computing to overcome these challenges in power system optimization, including optimal power flow (OPF), unit commitment (UC), economic dispatch (ED), and intelligent switching and topology optimization (IS-TO). Recent research has introduced various quantum methodologies—such as gate-based, annealing-based, variational algorithms, and quantum-inspired algorithms—to address the combinatorial complexity inherent in grid reconfiguration and energy management. The review summaries the quantum algorithms, quantum devices and the power system test cases, highlighting hybrid quantum–classical strategies that leverage the complementary strengths of both paradigms. Some quantum advantages have been observed, including theoretical speedup, accurate simulation results, scalable qubit usage, efficient QUBO mapping. In particular, the review emphasizes the importance of integrating quantum optimization techniques with classical control frameworks, these hybrid approaches demonstrate the potential to improve real-time grid management and operational reliability. A significant portion of the analysis is devoted to the practical limitations of current quantum devices. Present-day quantum hardware, operating in the noisy intermediate-scale quantum (NISQ) era, remains highly sensitive to noise and limited in qubit connectivity, which constrains the scale and accuracy of implemented algorithms. The review delves into specific challenges such as the need for qubit-efficient encoding techniques and error mitigation strategies that are critical for handling real-world grid optimization problems. In addition, the work draws attention to the performance discrepancies between theoretical quantum speedups and experimental validations, underscoring the importance of rigorous benchmark studies using representative power grid test cases. In summary, this review highlights both the promise and limitations of quantum computing for power system optimization. It provides a comprehensive overview of the state-of-the-art technologies, categorizes recent advancements in algorithm design, and discusses practical considerations for implementation, and serves as an informative resource on current research. Future research directions include developing robust hybrid frameworks, advancing qubit-efficient formulations, and scaling up experimental demonstrations to confirm the theoretical advantages of quantum methods in large-scale power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Advanced Models and Fast Simulation Algorithms for MARS Plants

With the successful commercialization of modular multilevel converters (MMC) and rapid increase in photovoltaic (PV) penetration and energy storage systems (ESS), one of the research areas being explored dynamically is integration of PV systems and ESS systems to MMCs. On this front, an integrated development of PV plants, ESS, and alternating- and direct-current (ac/dc) systems through Multi-Port Autonomous Reconfigurable Solar (MARS) plant is proposed and being researched upon. However, simulation of MARS systems on traditional existing software takes very long time (order of days) due to the presence of a large number of states and non-linear devices. Therefore, development of fast high fidelity models is of paramount importance to test the hardware design, control design and planning of power system expansions. In this context, two research objectives are addressed in this paper: firstly, an ultra fast single- or multi- CPU simulation algorithm to simulate the MARS system based on state-space models, hybrid discretization algorithm with a relaxation technique that reduces the imposed computational burden and a multi-rate method is presented. Secondly, a reduced order model of MARS is also presented. Both the developed techniques are validated with respect to reference PSCAD/EMTDC model.

Marthi, Phani Ratna Vanamali↗

DOE OSTI2021

Performance Portability in the Exascale Computing Project: Exploration Through a Panel Series

Performance portability is a critical issue for the Exascale Computing Project (ECP) because of nontrivial architectural differences between machines available today and those expected at exascale. Many ECP project teams are working toward performance portability, and would expect to benefit from sharing lessons learned, identifying gaps, and discovering opportunities for partnerships. To facilitate this communication, the IDEAS-ECP project partnered with the three focus areas of ECP (application development, software technology, and hardware and integration), and Department of Energy computing facilities, to lead a series of panel discussions on performance portability. The panels were organized around broadly common themes of algorithmic and data locality challenges. In this article, we describe the panel series, its objectives, and perspectives from the various areas of the project. Finally, we also discuss use cases that are distinctive, as well as conclusions drawn from the collective experience of the participants.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

The analysis of connected data is an increasingly important application in high-performance computing. Such analyses can reveal fraudulent patterns in financial transactions, optimize telecommunications networks, predict information flow in social networks, etc. However, the landscape of graph analytics is highly diverse. Graph algorithms stress processor architectures differently, and no one graph can represent all topologies. Consequently, no single approach or framework is expected to be optimal for all graph analytics problems. To help make sense of this diverse landscape, we evaluated four approaches to graph analytics: GraphBLAS, Galois, BGL17, GraphIt; and compare them against hand-tuned implementations that take advantage of hardware features on our test platform. Graph- BLAS formulates graph analytics as sparse linear algebra. Galois provides syntactic constructs for data parallelism over irregular data structures. BGL17 is a generic C++ template library for implementing graph algorithms. GraphIt provides a domain- specific language to describe and optimize graph algorithms. We use the GAP Benchmark Suite to establish baseline performance and guide the side-by-side evaluation of each framework. GAP consists of 30 tests: six graph analytics algorithms (breadth- first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) run on five graphs, each with different topological characteristics (e.g., high diameter, skewed degree distribution, high average degree). High-performance reference implementations are included for each benchmark algorithm. Because a graph can be loaded into memory a number of ways (e.g., flat file on disk, compressed sparse format, data frames, retrieved from SQL or NoSQL databases), our evaluation focused on computational performance rather than I/O. Our results show the relative strengths of each framework.

Graph algorithms, Benchmarking, shared-memory prog↗

DOE OSTI2023

Hardware-in-the-Loop Evaluation of Grid-Edge DER Chip Integration Into Next-Generation Smart Meters

To facilitate the implementation of distributed energy resource management systems (DERMS), we propose to insert a grid-edge distributed energy resource (DER) chip hosting a DERMS algorithm into the next generation of smart meters. This will create a pathway for the wide adoption of DERMS technology because many utilities plan to invest in advanced metering infrastructure in the near future. This will also bridge the gap between an electrical power utility and DERs behind the meter. The DER chip is designed to follow power direction signals from the DERMS coordinator while balancing its local objectives. We tested the chip using a controller- and power-hardware-in-the-loop evaluation under three scenarios that a DERMS could face in the real world. The DER chip was capable of and effective at directing four heterogeneous DERs to respond to a DERMS coordinator for grid services (e.g., voltage regulation and a virtual power plant).

distributed energy resource management system↗

DOE OSTI2023

Hardware-in-the-Loop Evaluation of Grid-Edge DER Chip Integration Into Next-Generation Smart Meters: Preprint

distributed energy resource management system↗

DOE OSTI2025

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Design and Optimization of High-frequency Transformer for Isolated Single-Stage Three-Phase AC/DC Converter using Bidirectional Switches

This paper presents the design and optimization of the high-frequency transformer for an isolated single-stage threephase AC/DC converter enabled by the 4H-SiC BiDirectional Field Effect Transistor (BiDFET). Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to perform a multiobjective optimization to minimize loss, weight, and volume. A 20 kW, 50 kHz hardware prototype based on the selected optimal point is characterized by a power amplifier. The finite element analysis simulation results are also presented to validate the core magnetic flux density and winding electric field strength. The experimental prototype was tested at 800 Vdc/480 Vrms at 10 kW rated power.

BiDFET, High-frequency transformer, Multiobjective↗

DOE OSTI2020

Verifying Deep Reductions in the Nuclear Arsenals: Development and Demonstration of a Motion-detection Subsystem for a “Buddy Tag” Using Non-export Controlled Accelerometers

Future nuclear arms-control agreements may place numerical limits on items that are difficult to monitor with national technical means, even when complemented with onsite inspections. Such items could include small objects and mobile assets, such as non-deployed nuclear warheads and mobile missile launchers. Typically, this verification task can be addressed with unique identifiers, but standard tagging techniques may be unacceptable in this case due to host concerns about safety and intrusiveness. First proposed in the late 1980s and partially developed by Sandia National Laboratories in the early 1990s, the “Proximity Tag” or “Buddy Tag” concept seeks to overcome these concerns by separating the tag from the treaty accountable item itself. A buddy tag has two key elements: a tamper-indicating enclosure and a motion-detection system designed to detect illicit movements in a stand-down period. As part of this project, we have built a buddy-tag prototype for demonstration and evaluation purposes. This paper reviews the design choices and functionalities of the tag’s motion-detection subsystem.We pursue a modular approach for the tag’s hardware, built around an Arduino-class microcontroller and a non-export-controlled low-noise accelerometer, and use open-source algorithms for the motion-detection software. Finally, we discuss the results of an extensive experimental campaign involving both indoor and outdoor measurements assessing the performance of the tag under real-world environmental conditions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2026

Chemical Recommender System: Replacement Suggestions for Small Molecules

The Chemical Recommender System (CRS) is an open-source, high-performance toolkit that enables real-time similarity searches across the complete PubChem database (over 50 million molecules) using commodity hardware. The CRS addresses critical limitations in existing chemical informatics platforms through a novel vector database infrastructure, extensible model integration capabilities, and complete algorithmic transparency. The system implements a vector database deployment with partitioned indexing that achieves a ~60x speedup over traditional approaches. A containerized model integration framework allows researchers to seamlessly incorporate custom predictive models into the full-scale search and scoring pipeline, while complete configurability of search parameters, filtering logic, and scoring functions provides capabilities not available in existing black-box solutions. Beyond structural similarity, the CRS integrates OPERA QSAR models for thermophysical and toxicity predictions, RDKit synthetic accessibility scoring, and user-defined models to compute weighted final replacement scores. The complete system is accessible through an interactive web application supporting real-time progress monitoring, post-processing score re-weighting, automated PDF reporting, and batch processing capabilities.

Nair, Parthiv Anand [Sandia National Laboratories ↗

DOE OSTI2026

Exploring the Energy Frontier through Precision Tests and Fast Tracking with the CMS Detector (Final Technical Report)

This Early Career Award supported a research program using the CMS experiment at the CERN LHC to probe physics beyond the Standard Model in the top quark and Higgs boson sectors, alongside detector and trigger developments for the High-Luminosity LHC (HL-LHC) upgrade. The program (i) searched for charged lepton flavor violation (LFV) in the top quark sector with the full CMS Run-2 data set, placing the world’s strongest limits to date on the $t → eµq\ (q = u/c)$ branching fraction; (ii) developed preliminary analysis methods toward a boosted $t\bar{t}H(b\bar{b})$ measurement of the top quark Yukawa coupling and its CP properties; (iii) made leading contributions to the hardware-based Level-1 (L1) track finding system for the upgraded CMS detector for HL-LHC; and (iv) developed novel L1 trigger algorithms, notably a displaced vertex trigger enabling new searches for exotic long-lived particles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗