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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fission with Exotic Nuclei (Full Technical Report)

Despite its importance for stockpile stewardship or nuclear forensics, data on fission is fragmentary: most experiments have only been performed on stable actinide nuclei and are often incomplete, and theoretical simulations often contain many parameters hard to constrain, which results in large uncertainties. Consequently, nuclear libraries have large gaps for important isotopes. The goal of this project was to prepare to take advantage of the unprecedented yields of radioactive isotopes at the upcoming DOE Facility for Rare Ion Beams and to leverage recent progress in the field of machine learning to develop a comprehensive program of fission studies that could address some of the most pressing problems in nuclear fission over the next several decades. Our project leveraged synergies between experimental nuclear physics, nuclear theory, and data science expertise at LLNL. On the experimental front, our main objective was to develop in-house expertise for inverse kinematics reactions with relativistic beams as well as to field and test new detectors to perform correlated measurements of fission properties. On the theoretical side, the objective was to develop a novel, high-fidelity and scalable approach of fissionfragment calculations based on emulating computationally expensive, quantum-mechanical calculations of nuclear properties with deep neural networks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

OpenPET Hardware, Firmware, Software, and Board Design Files

OpenPET is an open source, flexible, high-performance, and modular data acquisition system for a variety of applications. The OpenPET electronics are capable of reading analog voltage or current signals from a wide variety of sensors. The electronics boards make extensive use of field programmable gate arrays (FPGAs) to provide flexibility and scalability. Firmware and software for the FPGAs and computer are used to control and acquire data from the system. The command and control flow is similar to the data flow, however, the commands are initiated from the computer similar to a tree topology (i.e., from top-to-bottom). Each node in the tree discovers its parent and children, and all addresses are configured accordingly. A user (or a script) initiates a command from the computer. This command will be translated and encoded to the corresponding child (e.g., SB, MB, DB, etc.). Consecutively, each node will pass the command to its corresponding child(ren) by looking at the destination address. Finally, once the command reaches its desired destination(s) the corresponding node(s) execute(s) the command and send(s) a reply, if required. All the firmware, software, and the electronics board design files are distributed through the OpenPET website (http://openpet.lbl.gov).

Abu-Nimeh, Faisal↗
DOE OSTI2014

A Runtime Environment for Supporting Research in Resilient HPC System Software & Tools

The high-performance computing (HPC) community continues to increase the size and complexity of hardware platforms that support advanced scientific workloads. The runtime environment (RTE) is a crucial layer in the software stack for these large-scale systems. The RTE manages the interface between the operating system and the application running in parallel on the machine. The deployment of applications and tools on large-scale HPC computing systems requires the RTE to manage process creation in a scalable manner, support sparse connectivity, and provide fault tolerance. We have developed a new RTE that provides a basis for building distributed execution environments and developing tools for HPC to aid research in system software and resilience. This paper describes the software architecture of the Scalable runTime Component Infrastructure (STCI), which is intended to provide a complete infrastructure for scalable start-up and management of many processes in large-scale HPC systems. We highlight features of the current implementation, which is provided as a system library that allows developers to easily use and integrate STCI in their tools and/or applications. The motivation for this work has been to support ongoing research activities in fault-tolerance for large-scale systems. We discuss the advantages of the modular framework employed and describe two use cases that demonstrate its capabilities: (i) an alternate runtime for a Message Passing Interface (MPI) stack, and (ii) a distributed control and communication substrate for a fault-injection tool.

Computer Science↗
DOE OSTI2025

Surrogate-constructed scalable-circuits adaptive variational quantum eigensolver in the Schwinger model

Inspired by recent advancements in simulating periodic systems on quantum computers, we develop an approach to further advance the simulation of these systems, named (SC) 2 -ADAPT-VQE. Our approach extends the scalable-circuits ADAPT-VQE framework, which builds an ansatz from a pool of coordinate-invariant operators defined for arbitrarily large, though not arbitrarily small, volumes. Our method uses a classically tractable “surrogate constructed” method to remove irrelevant operators from the pool, reducing the minimum size for which the scalable circuits are defined. Bringing together the scalable circuits and the surrogate constructed approaches forms the core of the (SC) 2 methodology. Our approach allows for a wider set of classical computations on small volumes, which can be used for a more robust extrapolation protocol. While developed in the context of lattice models, the surrogate construction portion is applicable to a wide variety of problems where information about the relative importance of operators in the pool is available. As an example, we use it to compute the properties of the Schwinger model—quantum electrodynamics for a single, massive fermion in 1 +1 dimensions—and show that our method can be used to accurately extrapolate to the continuum limit.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A Backend-agnostic, Quantum-classical Framework for Simulations of Chemistry in C ++

As quantum computing hardware systems continue to advance, the research and development of performant, scalable, and extensible software architectures, languages, models, and compilers is equally as important to bring this novel coprocessing capability to a diverse group of domain computational scientists. For the field of quantum chemistry, applications and frameworks exist for modeling and simulation tasks that scale on heterogeneous classical architectures, and we envision the need for similar frameworks on heterogeneous quantum-classical platforms. Furthermore, we present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software that specifically targets chemistry applications. We review the fundamental design features in XACC, with special attention to its extensibility and modularity for key quantum programming workflow interfaces and provide an overview of the interfaces most relevant to simulations of chemistry. A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented, while also illustrating the various APIs that would enable the community to extend, modify, and devise new algorithms and applications in the realm of chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

High-bandwidth CMOS-voltage-level electro-optic modulation of 780 nm light in thin-film lithium niobate

Integrated photonics operating at visible-near-infrared (VNIR) wavelengths offer scalable platforms for advancing optical systems for addressing atomic clocks, sensors, and quantum computers. The complexity of free-space control optics causes limited addressability of atoms and ions, and this remains an impediment on scalability and cost. Networks of Mach-Zehnder interferometers can overcome challenges in addressing atoms by providing high-bandwidth electro-optic control of multiple output beams. Here, we demonstrate a VNIR Mach-Zehnder interferometer on lithium niobate on sapphire with a CMOS voltage-level compatible full-swing voltage of 4.2 V and an electro-optic bandwidth of 2.7 GHz occupying only 0.35 mm 2 . Our waveguides exhibit 1.6 dB/cm propagation loss and our microring resonators have intrinsic quality factors of 4.4 × 10 5 . This specialized platform for VNIR integrated photonics can open new avenues for addressing large arrays of qubits with high precision and negligible cross-talk.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Realizing string-net condensation: Fibonacci anyon braiding for universal gates and sampling chromatic polynomials

Abstract The remarkable complexity of a topologically ordered many-body quantum system is encoded in the characteristics of its anyons. Quintessential predictions emanating from this complexity employ the Fibonacci string net condensate (Fib SNC) and its anyons: sampling Fib-SNC would estimate chromatic polynomials while exchanging its anyons would implement universal quantum computation. However, physical realizations remained elusive. We introduce a scalable dynamical string net preparation (DSNP) that constructs Fib SNC and its anyons on reconfigurable graphs suitable for near-term superconducting processors. Coupling the DSNP approach with composite error-mitigation on deep circuits, we create, measure, and braids Fibonacci anyons; charge measurements show 94% accuracy, and exchanging the anyons yields the expected golden ratioϕwith 98% average accuracy. We then sample the Fib SNC to estimate chromatic polynomial atϕ + 2 for several graphs. Our results establish the proof of principle for using Fib-SNC and its anyons for fault-tolerant universal quantum computation and aim at a classically hard problem.

Science & Technology - Other Topics↗
DOE OSTI2023

Distributed approximate minimal Steiner trees with millions of seed vertices on billion-edge graphs

In this report, we present a parallel 2-approximation Steiner minimal tree algorithm and its MPI-based distributed implementation. In place of expensive distance computations between all pairs of seed vertices, the solution we employ exploits a cheaper Voronoi cell computation. Our design leverages asynchronous processing and message prioritization to accelerate convergence of distance computations, and harnesses vertex and edge centric processing to offer fast time-to-solution. We demonstrate scalability and performance using real-world graphs with up to 128 billion edges and 512 compute nodes, and show the ability to find Steiner trees with up to one million seed vertices. Using 12 data instances, we present comparison with the state-of-the-art exact solver, SCIP-Jack, and two sequential 2-approximate algorithms. We empirically show that, on average, the total distance of the Steiner tree identified by our solution is 1.1290 times greater than the Steiner minimal tree – well within the theoretical approximation bound of 2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

NWChem

NWChem is a computational chemistry software package which includes quantum chemical and molecular dynamics functionality. It aims to be scalable both in its ability to treat large problems efficiently, and in its usage of available parallel computing resources.

Apra, Edoardo [Pacific Northwest National Laborato↗
DOE OSTI2025

NWChem

NWChem is a computational chemistry software package which includes quantum chemical and molecular dynamics functionality. It aims to be scalable both in its ability to treat large problems efficiently, and in its usage of available parallel computing resources.

Apra, Edoardo [Pacific Northwest National Laborato↗
DOE OSTI2023

A Scalable PDC Placement Technique for Fast and Resilient Monitoring of Large Power Grids

The wide-area measurement system (WAMS) is a key enabler of real-time monitoring of power grids. The essential goals of WAMS design are fast and resilient data transfer from phasor measurement units (PMU) to phasor data concentrators (PDC). We propose a scalable two-stage PDC placement technique for minimizing the end-to-end delay while maintaining resiliency. In the prescreening stage, the plausible candidates of PDC configurations are identified based on a graph theory-based multi-median function (MMF). Here, in this article, a computationally efficient meta-heuristic algorithm is used to address scalability. In the candidate selection stage, two different algorithms, namely, Suurballe's and Dijkstra's, are employed to identify the best of those plausible PDC configurations as the final design. This technique not only minimizes the hop paths between PMUs and PDCs, but also ensures network resiliency against single PMU, PDC, or communication link failure by incorporating the roles of PMUs in power grid observability into routing policy. Simulation results on the IEEE 57-bus test power system and the 2000-bus test power system demonstrate the effectiveness and scalability of the proposed technique.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Harnessing ferro-valleytricity in pentalayer rhombohedral graphene for memory and compute

Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such as valley-dependent Hall effects and orbital magnetism, which could revolutionize next-generation electronics. However, achieving independent control over valley polarization and orbital magnetism has been a challenge due to the need for large electric fields. A recent breakthrough involving pentalayer rhombohedral graphene has demonstrated the ability to individually manipulate anomalous Hall signals and orbital magnetic hysteresis, forming what is known as a valley-magnetic quartet. Here, we leverage the electrically tunable ferro-valleytricity of pentalayer rhombohedral graphene to develop nonvolatile memory and in-memory computation applications. We propose an architecture for a dense, scalable, and selector-less nonvolatile memory array that harnesses the electrically tunable ferro-valleytricity. In our designed array architecture, nondestructive read and write operations are conducted by sensing the valley state through two different pairs of terminals, allowing for independent optimization of read/write peripheral circuits. The power consumption of our PRG-based array is remarkably low, with only ∼6 nW required per write operation and ∼2.3 nW per read operation per cell. This consumption is orders of magnitude lower than that of the majority of state-of-the-art cryogenic memories. Additionally, we engineer in-memory computation by implementing majority logic operations within our proposed nonvolatile memory array without modifying the peripheral circuitry. In conclusion, our framework presents a promising pathway toward achieving ultra-dense cryogenic memory and in-memory computation capabilities.

2D materials↗
DOE OSTI2025

Network Anomaly Detection in Distributed Edge Computing Infrastructure

As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. We show that by leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by MannWhitney U and Kolmogorov-Smirnov tests (p<0.05).

Marfo, William [University of Texas at El Paso,Dep↗
DOE OSTI2024

Scalable Implicit Solvers with Dynamic Mesh Adaptation for a Relativistic Drift-Kinetic Fokker–Planck–Boltzmann Model

In this work we consider a relativistic drift-kinetic model for runaway electrons along with a Fokker–Planck operator for small-angle Coulomb collisions, a radiation damping operator, and a secondary knock-on (Boltzmann) collision source. Here, we develop a new scalable fully implicit solver utilizing finite volume and conservative finite difference schemes and dynamic mesh adaptivity. A new data management framework in the PETSc library based on the p4est library is developed to enable simulations with dynamic adaptive mesh refinement (AMR), distributed memory parallelization, and dynamic load balancing of computational work. This framework and the runaway electron solver building on the framework are able to dynamically capture both bulk Maxwellian at the low-energy region and a runaway tail at the high-energy region. To effectively capture features via the AMR algorithm, a new AMR indicator prediction strategy is proposed that is performed alongside the implicit time evolution of the solution. This strategy is complemented by the introduction of computationally cheap feature-based AMR indicators that are analyzed theoretically. Numerical results quantify the advantages of the prediction strategy in better capturing features compared with nonpredictive strategies; and we demonstrate trade-offs regarding computational costs. The robustness with respect to model parameters, algorithmic scalability, and parallel scalability are demonstrated through several benchmark problems including manufactured solutions and solutions of different physics models. We focus on demonstrating the advantages of using implicit time stepping and AMR for runaway electron simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Scalable preconditioning for the stabilized contact mechanics problem

We present a family of preconditioning strategies for the contact problem in fractured and faulted porous media. We combine low-order continuous finite elements to simulate the bulk deformation with piecewise constant Lagrange multipliers to impose the frictional contact constraints. This formulation is not uniformly inf-sup stable and requires stabilization. We improve previous work by Franceschini et al. (2020) by introducing a novel jump stabilization technique that requires only local geometrical and mechanical properties. We then design scalable preconditioning strategies that take advantage of the block structure of the Jacobian matrix using a physics-based partitioning of the unknowns by field type, namely displacement and Lagrange multipliers. The key to the success of the proposed preconditioners is a pseudo-Schur complement obtained by eliminating the Lagrange multiplier degrees of freedom, which can then be efficiently solved using an optimal multigrid method. Numerical results, including complex real-world problems, are presented to illustrate theoretical properties, scalability and robustness of the preconditioner. A comparison with other approaches available in the literature is also provided.

58 GEOSCIENCES↗
DOE OSTI2013

Scalable load balancing for massively parallel distributed Monte Carlo particle transport

In order to run computer simulations efficiently on massively parallel computers with hundreds of thousands or millions of processors, care must be taken that the calculation is load balanced across the processors. Examining the workload of every processor leads to an unscalable algorithm, with run time at least as large as O(N), where N is the number of processors. We present a scalable load balancing algorithm, with run time 0(log(N)), that involves iterated processor-pair-wise balancing steps, ultimately leading to a globally balanced workload. We demonstrate scalability of the algorithm up to 2 million processors on the Sequoia supercomputer at Lawrence Livermore National Laboratory. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Scalable Deep Learning via I/O Analysis and Optimization

Scalable deep neural network training has been gaining prominence because of the increasing importance of deep learning in a multitude of scientific and commercial domains. Consequently, a number of researchers have investigated techniques to optimize deep learning systems. Much of the prior work has focused on runtime and algorithmic enhancements to optimize the computation and communication. Despite these enhancements, however, deep learning systems still suffer from scalability limitations, particularly with respect to data I/O. This situation is especially true for training models where the computation can be effectively parallelized, leaving I/O as the major bottleneck. In fact, our analysis shows that I/O can take up to 90% of the total training time. Thus, in this article, we first analyze LMDB, the most widely used I/O subsystem of deep learning frameworks, to understand the causes of this I/O inefficiency. Based on our analysis, we propose LMDBIO—an optimized I/O plugin for scalable deep learning. LMDBIO includes six novel optimizations that together address the various shortcomings in existing I/O for deep learning. Lastly, our experimental results show that LMDBIO significantly outperforms LMDB in all cases and improves overall application performance by up to 65-fold on a 9,216-core system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Frequency-encoded photonic qubits for scalable quantum information processing

Among the objectives for large-scale quantum computation is the quantum interconnect: a device that uses photons to interface qubits that otherwise could not interact. However, the current approaches require photons indistinguishable in frequency—a major challenge for systems experiencing different local environments or of different physical compositions altogether. Here, we develop an entirely new platform that actually exploits such frequency mismatch for processing quantum information. Labeled “spectral linear optical quantum computation” (spectral LOQC), our protocol offers favorable linear scaling of optical resources and enjoys an unprecedented degree of parallelism, as an arbitrary Ν-qubit quantum gate may be performed in parallel on multiple Ν-qubit sets in the same linear optical device. Here, not only does spectral LOQC offer new potential for optical interconnects, but it also brings the ubiquitous technology of high-speed fiber optics to bear on photonic quantum information, making wavelength-configurable and robust optical quantum systems within reach.

97 MATHEMATICS AND COMPUTING↗