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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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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Discovery of a Gamma-Ray Black Widow Pulsar by GPU-accelerated Einstein@Home

We report the discovery of 1.97 ms period gamma-ray pulsations from the 75 minute orbital-period binary pulsar now named PSR J1653-0158. The associated Fermi Large Area Telescope gamma-ray source 4FGL J1653.6-0158 has long been expected to harbor a binary millisecond pulsar. Despite the pulsar-like gamma-ray spectrum and candidate optical/X-ray associations—whose periodic brightness modulations suggested an orbit—no radio pulsations had been found in many searches. The pulsar was discovered by directly searching the gamma-ray data using the GPU-accelerated Einstein@Home distributed volunteer computing system. The multidimensional parameter space was bounded by positional and orbital constraints obtained from the optical counterpart. More sensitive analyses of archival and new radio data using knowledge of the pulsar timing solution yield very stringent upper limits on radio emission. Any radio emission is thus either exceptionally weak, or eclipsed for a large fraction of the time. The pulsar has one of the three lowest inferred surface magnetic-field strengths of any known pulsar with B surf ≈ 4 × 10 7 G. The resulting mass function, combined with models of the companion star's optical light curve and spectra, suggests a pulsar mass gsim 2 M ⊙ . The companion is lightweight with mass ~0.01 M ⊙ , and the orbital period is the shortest known for any rotation-powered binary pulsar. This discovery demonstrates the Fermi Large Area Telescope's potential to discover extreme pulsars that would otherwise remain undetected.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

EJFAT: Towards Intelligent Compute Destination Load Balancing

To handle increased data flow, Jefferson Lab (JLab) is partnering with ESnet for development of an AI/ML directed compute work Load Balancer (LB) of UDP streamed data. The LB is FPGA based featuring dynamically configurable, low latency and high throughput destination address switching. The LB provides integration of edge and core computing to support JLab experimental programs, the Electron-Ion Collider, as well as data centers of the future. In the ESnet/JLab FPGA Accelerated Transport (EJFAT) initiative, the function of the LB Data Plane (DP) is to redirect data streams to selectable (but unknown to sender) destination hosts based on current worload and within that host to destination ports as a function of sub- stream id. This effects hierarchical scaling, first across compute machines for processing over a series of events and second, across ports so different data source sub-streams may be assigned to different processors for further parallelization. The LB Control Plane (CP) programs the DP using compute farm telemetry to direct and balance workloads across a compute cluster as the operating conditions require. While Proportional/Integrative/Derivative (PID) controllers are often seen in similar applications, here we investigate the feasibility of a Reinforcement Learning (RL) based schedule manager running in the CP to provide dynamic updates to the DP scheduling policy.

Lawrence, David↗
DOE OSTI2021

Porting HEP Parameterized Calorimeter Simulation Code to GPUs

The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. As the LHC is upgraded to allow for higher luminosity, resulting in much higher data rates, purely relying on CPUs may not provide enough computing power to support the simulation and data analysis needs. As a proof of concept, we investigate the feasibility of porting a HEP parameterized calorimeter simulation code to GPUs. We have chosen to use FastCaloSim, the ATLAS fast parametrized calorimeter simulation. While FastCaloSim is sufficiently fast such that it does not impose a bottleneck in detector simulations overall, significant speed-ups in the processing of large samples can be achieved from GPU parallelization at both the particle (intra-event) and event levels; this is especially beneficial in conditions expected at the high-luminosity LHC, where extremely high per-event particle multiplicities will result from the many simultaneous proton-proton collisions. We report our experience with porting FastCaloSim to NVIDIA GPUs using CUDA. A preliminary Kokkos implementation of FastCaloSim for portability to other parallel architectures is also described.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Scaling Inference Using Triton to Accelerate Particle Physics at the LHC and DUNE

DUNE and the LHC experiments consist of unique and cutting-edge particle detectors that create massive, complex, and rich datasets with billions of events. They require sophisticated algorithms to reconstruct and interpret the data. Modern machine learning algorithms provide a powerful toolset to detect and classify particles, from familiar image processing convolutional neural networks to newer graph neural network architectures. A full reconstruction of these particle collisions requires novel approaches to handle the computing challenge of processing so much raw data. In a series of studies, physicists from Fermilab, CERN, and university groups explored how to accelerate their data processing using the Triton Inference Server.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Combined ambient vibration and surface displacement measurements for improved progressive failure monitoring at a toppling rock slab in Utah, USA

Seismic resonance and surface displacement measurements can be implemented in tandem to improve landslide characterization and progressive failure monitoring. Crack aperture data are frequently used in rock slope stability monitoring and often exhibit recognizable trends prior to failure, such as accelerated crack opening. Alternatively, ambient resonance data offer multiple parameters including modal frequencies, damping, and polarization that can be monitored alongside crack aperture and may respond differently to environmental forcings and complex failure evolution. We analyzed data from continuous ambient vibration monitoring and concomitant crack aperture measurements at the Courthouse Mesa instability, a large toppling sandstone slab in Utah, USA. Three years of data revealed crack aperture increases of 2–4 mm/year with no clearly detectable irreversible changes in modal parameters, including frequency. Annually, frequency and displacement varied by 29% and 19% of the mean, respectively, with average and maximum daily frequency fluctuations of 6.5% and 16%, respectively. These reversible cyclic changes were primarily temperature-driven, but annually, frequency was in-phase with temperature whereas crack aperture lagged temperature changes by ~37 days. Polarization and damping also varied seasonally but were less strongly correlated with temperature. Conceptual 3D finite element modeling demonstrated consistent frequency decreases associated with crack propagation but variable changes in crack aperture measured at a single point; i.e., crack propagation did not always result in increased crack opening but always generated a resonance frequency decrease. Taken together, our data suggest a possible thermal wedging-ratcheting mechanism at the Courthouse Mesa instability, where annual thermoelastic crack closure is impeded by debris infill but the absence of downward crack propagation during the monitoring period is evidenced by no permanent resonance frequency changes. Our study demonstrates that combined seismic resonance and crack aperture data provide an improved description of rock slope instability behavior, supporting refined characterization and monitoring of changes accompanying progressive failure.

58 GEOSCIENCES↗
DOE OSTI2022

A Modern Ethernet Data Acquisition Architecture for Fermilab Beam Instrumentation

The Fermilab Accelerator Division, Instrumentation Department is adopting an open-source framework to replace our embedded VME-based data acquisition systems. Utilizing an iterative methodology, we first moved to embedded Linux, removing the need for VxWorks. Next, we adopted Ethernet on each data acquisition module eliminating the need for the VME backplane in addition to communicating with a rack mount server. Development of DDCP (Distributed Data Communications Protocol), allowed for an abstraction between the firmware and software layers. Each data acquisition module was adapted to read out using 1 GbE and aggregated at a switch which up linked to a 10 GbE network. Current development includes scaling the system to aggregate more modules, to increase bandwidth to support multiple systems and to adopt MicroTCA as a crate technology. The architecture was utilized on various beamlines around the Fermilab complex including PIP2IT, FAST/IOTA and the Muon Delivery Ring. In summary, we were able to develop a data acquisition framework which incrementally replaces VxWorks & VME hardware as well as increases our total bandwidth to 10 Gbit/s using off the shelf Ethernet technology.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Harnessing ML Privacy by Design Through Crossbar Array Non-idealities

Deep Neural Networks (DNNs), handling computeand data-intensive tasks, often utilize accelerators like Resistiveswitching Random-access Memory (RRAM) crossbar for energyefficient in-memory computation. Despite RRAM’s inherent nonidealities causing deviations in DNN output, this study transforms the weakness into strength. By leveraging RRAM non-idealities, the research enhances privacy protection against Membership Inference Attacks (MIAs), which reveal private information from training data. RRAM non-idealities disrupt MIA features, increasing model robustness and revealing a privacy-accuracy tradeoff. Empirical results with four MIAs and DNNs trained on different datasets demonstrate significant privacy leakage reduction with a minor accuracy drop (e.g., up to 2.8% for ResNet-18 with CIFAR-100).

artificial intelligence↗
DOE OSTI2023

Integrated Research Infrastructure Architecture Blueprint Activity (Final Report 2023)

The complexity of scientific pursuits is increasing rapidly with aspects that require dynamic integration of experiment, observation, theory, modeling, simulation, visualization, machine learning (ML), artificial intelligence (AI), and analysis. Research projects across the Department of Energy (DOE) are increasingly data and compute intensive. Innovative research teams are accelerating the pace of discovery by using high-performance computational and data tools in their research workflows and leveraging multiple research infrastructures. Additionally, several recent high-level U.S. government reports underscore the necessity of a new advanced computing ecosystem for international competitiveness and national security. International competitors are moving forward with major research infrastructure integration efforts that seek to capture a competitive advantage in the global innovation race. Owing to its unparalleled constellation of world-class experimental and observational facilities and high-performance and extreme-scale computational, data, and networking infrastructure, DOE is positioned to be a global leader in this new era of integrated science. However, this new integration paradigm will demand continuing evolution to ensure the U.S. remains a global leader in research and innovation. The DOE Office of Science (SC) has seized on the strategic importance of integration and has adopted a vision for Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. To respond to the evolving computational requirements of research and the competitive international innovation landscape, experimental facilities could be connected with high performance computing resources for near real-time analysis, and resources should be provided for merging enormous and diverse data for AI/ML techniques and analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Introducing SpaceNet 9 - Cross-Modal Satellite Imagery Registration for Natural Disaster Responses

Computer vision algorithms are increasingly leveraged to accelerate geospatial analysis for disaster response and recovery. As the diversity of remote sensing imagery grows with optical, SAR, and other modalities, a perquisite for analytics is cross-modal image registration. There is a high potential to harness computer vision for this pre-processing requirement toward enabling downstream analytics such as heterogeneous change detection, automated feature extraction, and data fusion. Advancement in these areas has the potential to simplify data wrangling tasks and further accelerate disaster response timelines. The SpaceNet 9 challenge (launching in mid-2024) focuses on addressing the cross-modal image registration problem and demonstrating the utility of such modules on earthquake impacted scenarios. This paper describes the motivation for the SpaceNet 9 and provides a first overview of the dataset, the baseline algorithm, and implications for seeking cross-modal image registration in Earth observation. Code is available at https://github.com/SpaceNetChallenge/SpaceNet9.

Hansch, Ronny↗
DOE OSTI2025

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.

Pathak, Abhishek [Fermilab]↗
DOE OSTI2020

Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

density functional theory↗
DOE OSTI2022

Exploring the Use of Novel Spatial Accelerators in Scientific Applications

Driven by the need to find alternative accelerators which can viably replace GPUs in next-generation Supercomputing systems, this paper proposes a methodology to enable agile application/hardware co-design. The application-first methodology provides the ability to come up with design of accelerators while working with real-world workloads, available accelerators, and system software. The iterative design process targets a set of kernels in a workload for performance estimates that can prune the design space for later phases of detailed architectural evaluations. To this effect, in this paper, a novel data-parallel device model is introduced that simulates the latency of performance-sensitive operations in an accelerator including data transfers and kernel computation using multi-core CPUs. The use of off-the-shelf simulators, such as pre-RTL simulator Aladdin or multiple tools available for exploring the design of deep neural network accelerators (e.g., Timeloop) is demonstrated for evaluation of various accelerator designs using applications with realistic inputs. Examples of multiple device configurations that are instantiable in a system are explored to evaluate the performance benefit of deploying novel accelerators. The proposed device is integrated with a programming model and system software to potentially explore the impacts of high-level programming languages/compilers and low-level effects such as task scheduling on multiple accelerators. We analyze our methodology for a set of applications that represent high-performance computing (HPC) and graph analytics. The applications include a computational chemistry kernel realized using tensor contractions, triangle counting, GraphSAGE and Breadth-first Search. These applications include kernels such as dense matrix-dense matrix multiplication, sparse matrix-spare matrix multiplication, and sparse matrix-dense vector multiplication. Our results indicate potential performance benefits and insights for system design by including accelerators that realize these kernels along-side general purpose accelerators.

AI, codesign, Accelerated Computing, Modeling and ↗
DOE OSTI2020

Offsite Data Processing for the GlueX Experiment

The Thomas Jefferson National Accelerator Facility (JLab) 12GeV accelerator upgrade completed in 2015 is now producing data at volumes unprecedented for the lab. The resources required to process this data now exceed the capacity of the onsite farm necessitating the use of offsite computing resources for the first time in the history of JLab. GlueX is now utilizing NERSC and PSC for raw data production. Details of the workflow are presented.

Lawrence, David↗
DOE OSTI2026

Status of the Measurement of Neutrino-Electron Elastic Scattering in the NOvA Near Detector

Neutrino–electron elastic scattering is a purely leptonic process with a precisely calculable Standard Model cross section, providing an \textit{in situ} constraint on accelerator neutrino flux predictions. Using data collected with the NOvA Near Detector in forward horn current mode, we perform a measurement of neutrino–electron elastic scattering and apply a flux-constraint technique based on the reconstructed electron-energy spectrum. This approach improves the precision of the predicted $\nu_\mu$ flux and enhances the accuracy of near-detector cross-section measurements. The analysis also demonstrates a practical framework for flux constraints in current and future accelerator neutrino experiments.

Xiao, Yiwen [UC, Irvine (main)] (ORCID:00000001531↗
DOE OSTI2024

New NOvA Results with 10 Years of Data

NOvA is a two-detector accelerator neutrino oscillation experiment. Using Fermilab's newly Megawatt-capable NuMI neutrino beam, NOvA measures the disappearance of muon (anti)neutrinos and the appearance of electron (anti)neutrinos at the far detector, 810 km from the source. These oscillations are observed relative to the unoscillated beam composition measured at the functionally equivalent near detector, also located at Fermilab, which enables significant cancellation of systematic uncertainties. From these, we obtain precision measurements of the larger neutrino mass splitting and the largest neutrino mixing angle, as well as constraints on the octant of that angle, the neutrino mass ordering, and neutrino CP violation. In this talk I will present new measurements of these parameters using 10 years of NOvA data collected between 2013 and 2023, which includes twice the neutrino-mode exposure of our previous results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Metall: A persistent memory allocator for data-centric analytics

Data analytics applications transform raw input data into analytics-specific data structures before performing analytics. Unfortunately, such data ingestion steps are often more expensive than analytics. In addition, various types of NVRAM devices are already used in many HPC systems today. Such devices will be useful for storing and reusing data structures beyond a single process life cycle. We developed Metall, a persistent memory allocator built on top of the memory-mapped file mechanism. Metall enables applications to transparently allocate custom C++ data structures into various types of persistent memories. Metall incorporates a concise and high-performance memory management algorithm inspired by Supermalloc and the rich C++ interface developed by Boost.Interprocess library. On a dynamic graph construction workload, Metall achieved up to 11.7x and 48.3x performance improvements over Boost.Interprocess and memkind (PMEM kind), respectively. We also demonstrate Metall’s high adaptability by integrating Metall into a graph processing framework, GraphBLAS Template Library. Here this study’s outcomes indicate that Metall will be a strong tool for accelerating future large-scale data analytics by allowing applications to leverage persistent memory efficiently.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Characterization of Fine-Pitch Interconnects for Long-Lifetime HI Applications

This conference talk discusses optimizing gold-gold and gold-ENIG fine-pitch interconnections for Heterogeneous Integration applications, with a focus on longevity. We first look at electrical data from gold-gold bumps subjected to highly accelerated lifetime testing. Then, we discuss using chemical and physical data as a predictor for ENIG bump flip-chip success.

Humphrey, Nicholas James [Sandia National Laborato↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗