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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An extension to V ORO ++ for multithreaded computation of Voronoi cells

V ORO ++ is a software library written in C++ for computing the Voronoi tessellation, a technique in computational geometry that is widely used for analyzing systems of particles. V ORO ++ was released in 2009 and is based on computing the Voronoi cell for each particle individually. Here, we take advantage of modern computer hardware, and extend the original serial version to allow for multithreaded computation of Voronoi cells via the OpenMP application programming interface. We test the performance of the code, and demonstrate that it can achieve parallel efficiencies greater than 95% in many cases. Further, the multithreaded extension follows standard OpenMP programming paradigms, allowing it to be incorporated into other programs. We provide an example of this using the VoroTop software library, performing a multithreaded Voronoi cell topology analysis of up to 102.4 million particles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Design and Preliminary Experimental Results on a Uniform-Field Test Fixture for Power Flow Experiments

Electrodes that produce a uniform electric field across their surfaces are desired for a variety of high-voltage systems including spark-gap switches and transversely excited atmospheric lasers. Here, the implementation of uniform-field electrodes to a parallel-plate load is described for the study of vacuum power flow under pulsed-power applications. We begin with an overview of general uniform-field geometries, and provide an open-source program that generates these geometries as described by Ernst. Following that, a modification to an existing power-flow experimental platform, that adopts a uniform-field geometry, is introduced and its utility is described. Lastly, preliminary experimental results are presented and future steps are outlined.

Capacitors↗
DOE OSTI2026

PRISMA: PARALLEL REFINEMENT AND INTEGRATION SYSTEM FOR MULTI-AZIMUTHAL ANALYSIS

The Parallel Refinement and Integration System for Multi-azimuthal Analysis (PRISMA, version 1.1.0) is a Python application for processing X-ray diffraction (XRD) image data. PRISMA wraps GSAS-II to perform azimuthally-binned peak refinement, computes per-frame strain and d-spacing from those fits, and provides three PyQt5 graphical interfaces: (1) a Recipe Builder for selecting GSAS-II control (.imctrl) files, optional mask (.immask) files or threshold-ased masking, reference and experiment image sets, peaks, zimuthal range and bin size, and an optional ceria-based auto-calibration; (2) a Batch Processor that uses Dask on local workstations and pure MPI (mpi4py.futures.MPICommExecutor) on HPC to distribute GSAS-II refinement across cores or compute nodes and write results to a 4-dimensional (peaks x frames x azimuths x measurements) Zarr dataset; and (3) a Data Analyzer that renders heatmaps of fit parameters, strain, frame-to-frame deltas, and percent-change-vs-reference, and exports user-defined subsections to CSV or Excel. The peak-refinement algorithm is deterministic. Benchmark on ALCF Crux: a 20,000-image set, single-peak fit in frame mode with 44 azimuthal bins on 128 nodes x 128 workers, 48 seconds total wall time.

Lorenzo Martin, Maria De La Cinta [Argonne Nationa↗
DOE OSTI2022

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

We describe the experience of converting a CUDA implementation of a parallel graph coloring algorithm to SYCL. The goals are for our work to be useful to application and compiler developers by providing a detailed description of migration paths between CUDA and SYCL. We will describe how CUDA functions are mapped to SYCL functions. Evaluating the CUDA and SYCL implementations of the algorithm shows that the performance of SYCL and CUDA kernels are comparable over the test graph set on NVIDIA P100 and V100 GPUs. The SYCL program also allows for performance evaluation with the OpenCL and Level Zero interfaces and power profiling on an Intel GPU computing platform.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Adaptable Technology Integration for Enhanced Worker Safety and Efficiency within the SPD Gloveboxes [Slides]

Surplus Plutonium Disposition (SPD) Overview: (1) Application Background – Location: Savannah River Site, Aiken, South Carolina – High backlog of plutonium material to process – Parallel lines of gloveboxes – Highly manual operations requiring full PPE and reader/worker steps; (2) Development Scope – Development of automated processes and opportunities to provide robust engineering design alternatives for baseline design that would meet process improvement goals.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing

Abstract SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model their dynamic evolution during processing. The modular nature of the code allows new models and diagnostic computations to be added without modification to its core functionality, including its parallel algorithms. A variety of models for microstructural evolution (grain growth), solid-state diffusion, thin film deposition, and additive manufacturing (AM) processes are included in the code. SPPARKS can also be used to implement grid-based algorithms such as phase field or cellular automata models, to run either in tandem with a Monte Carlo method or independently. For very large systems such as AM applications, the Stitch I/O library is included, which enables only a small portion of a huge system to be resident in memory. In this paper we describe SPPARKS and its parallel algorithms and performance, explain how new Monte Carlo models can be added, and highlight a variety of applications which have been developed within the code.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Singlet fission for quantum information and quantum computing: the parallel JDE model

Abstract Singlet fission is a photoconversion process that generates a doubly excited, maximally spin entangled pair state. This state has applications to quantum information and computing that are only beginning to be realized. In this article, we construct and analyze a spin-exciton hamiltonian to describe the dynamics of the two-triplet state. We find the selection rules that connect the doubly excited, spin-singlet state to the manifold of quintet states and comment on the mechanism and conditions for the transition into formally independent triplets. For adjacent dimers that are oriented and immobilized in an inert host, singlet fission can be strongly state-selective. We make predictions for electron paramagnetic resonance experiments and analyze experimental data from recent literature. Our results give conditions for which magnetic resonance pulses can drive transitions between optically polarized magnetic sublevels of the two-exciton states, making it possible to realize quantum gates at room temperature in these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

The goal of influence maximization is to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. The problem has a number of important applications such as viral marketing, information spread, and epidemic control. Although computing optimal seed set is NP-Hard, due to the submodular nature of the problem efficient approximation algorithms exist. However, even state-of-the-art parallel implementations are limited by a sampling step that incurs large memory footprints. This in turn limits the problem size reach and approximation quality. In this work, we study the memory footprint of the sampling process collecting reverse reachability information in the IMM algorithm over large real-world social networks. We present an adaptive and memory-efficient optimization approach for a state-of-the-art multi-threaded parallel influence maximization algorithm. Our approach,HuffMax, uses a portion of the reverse reachable (RR) sets collected by the algorithm to learn the characteristics of the graph. Then, it compresses the intermediate reverse reachability information with Huffman coding, and queries directly on the compressed data to preserve the memory savings obtained through compression. We also propose an efficient sampling strategy based on the distribution of RR sets, which can further reduce the computation time for typical social networks with long-tail distributions. Considering a NUMA architecture, we scale up our solution on 128-core CPUs and reduce the memory footprint by up to 45.7% with negligible time overhead (or even faster) and without perceivable loss of accuracy.

Chen, Xinyu↗
DOE OSTI2023

Towards fast, accurate predictions of RF simulations via data-driven modeling: Forward and lateral models

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. More accurate simulations with fast electron diffusion are even slower, requiring multiple hours of run time with parallel processing. The machine learning models use a database of 16,000+ GEN-RAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods implemented in πScope ensure that the database covers the range of 9 input parameters (n e0 , T e0 , I p , B t , R 0 , n ∥︀ , Z e f f , V loop , P LHCD ) with sufficient density in all regions of parameter space. The surrogate models reduce the computation time from minutes-hours to ms with high accuracy across the input parameter space. Data-driven surrogate models also allow for solving inverse and “lateral” problems. A surrogate model for the inverse problem maps from a desired current drive or power deposition profile to a set of input parameters that would result in such a profile, while a surrogate model for the lateral problem maps from a measured experimental quantity such as hard x-ray emission to a current drive or power deposition profile. In conclusion, the πScope database creation workflow is flexible and applicable to other RF simulation codes such as TORIC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Efficient Parallel Sparse Symmetric Tucker Decomposition for High-Order Tensors

Tensor based methods are receiving renewed attention in recent years due to their prevalence in diverse real-world applications. There is considerable literature on tensor representations and algorithms for tensor decompositions, both for dense and sparse tensors. Many applications in hypergraph analytics, machine learning, psychometry, and signal processing result in tensors that are both sparse and symmetric, making it an important class for further study. Similar to the critical Tensor Times Matrix chain operation (TTMc) in general sparse tensors, the Sparse Symmetric Tensor Times Same Matrix chain (S3TTMc) operation is compute and memory intensive due to high tensor order and the associated factorial explosion in the number of non-zeros. In this work, we present a novel compressed storage format CSS for sparse symmetric tensors, along with an efficient parallel algorithm for the S3TTMc operation. We theoretically establish that S3TTMc on CSS achieves a better memory versus run-time trade-off compared to state-of-the-art implementations. We demonstrate experimental findings that confirm these results and achieve up to 2.9× speedup on synthetic and real datasets.

Shivakumar, Shruti↗
DOE OSTI2024

Pretraining Billion-Scale Geospatial Foundational Models on Frontier

As AI workloads increase in scope, generalization capability becomes challenging for small task-specific models and their demand for large amounts of labeled training samples increases. On the contrary, Foundation Models (FMs) are trained with internet-scale unlabeled data via self-supervised learning and have been shown to adapt to various tasks with minimal fine-tuning. Although large FMs have demonstrated significant impact in natural language processing and computer vision, efforts toward FMs for geospatial applications have been restricted to smaller size models, as pretraining larger models requires very large computing resources equipped with state-of-the-art hardware accelerators. Current satellite constellations collect 100+TBs of data a day, resulting in images that are billions of pixels and multimodal in nature. Such geospatial data poses unique challenges opening up new opportunities to develop FMs. We investigate billion scale FMs and HPC training profiles for geospatial applications by pretraining on publicly available data. We studied from end-to-end the performance and impact in the solution by scaling the model size. Our larger 3B parameter size model achieves up to 30% improvement in top1 scene classification accuracy when comparing a 100M parameter model. Moreover, we detail performance experiments on the Frontier supercomputer, America's first exascale system, where we study different model and data parallel approaches using PyTorch's Fully Sharded Data Parallel library. Specifically, we study variants of the Vision Transformer architecture (ViT), conducting performance analysis for ViT models with size up to 15B parameters. By discussing throughput and performance bottlenecks under different parallelism configurations, we offer insights on how to leverage such leadership-class HPC resources when developing large models for geospatial imagery applications.

Tsaris, Aristeidis (aris)↗
DOE OSTI2022

End-to-end Analytics for Grid Arch Design & All-hazard Assessment

Resiliency, reliability, and security of the next-generation smart grid depend upon leveraging advanced communication and computing technologies, integrating them with physical power systems, and developing real-time, fast, data-based applications to help in wide-area monitoring and control of the grid. Using a high sampling data rate from phasor measurement units (PMUs) to develop applications has opened the door to achieving the next-generation grid requirements. The North American Synchrophasor Initiative Network (NASPlnet) was developed in 2007-09 to create a standard and guide for PMU data exchanges. With the advancement in both networking and grid requirements, it is necessary to evaluate the performance of different NASPInet versions and their impact on applications. Therefore, we need a cyber-power cosimulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents a cyber-physical co-simulation testbed using NS3 to model the communication network, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application based on PMU data in an Institute of Electrical and Electronics Engineers 39-bus test system is presented using this co-simulation testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Quantum simulation of excited states from parallel contracted quantum eigensolvers

Abstract Computing excited-state properties of molecules and solids is considered one of the most important near-term applications of quantum computers. While many of the current excited-state quantum algorithms differ in circuit architecture, specific exploitation of quantum advantage, or result quality, one common feature is their rooting in the Schrödinger equation. However, through contracting (or projecting) the eigenvalue equation, more efficient strategies can be designed for near-term quantum devices. Here we demonstrate that when combined with the Rayleigh–Ritz variational principle for mixed quantum states, the ground-state contracted quantum eigensolver (CQE) can be generalized to compute any number of quantum eigenstates simultaneously. We introduce two excited-state (anti-Hermitian) CQEs that perform the excited-state calculation while inheriting many of the remarkable features of the original ground-state version of the algorithm, such as its scalability. To showcase our approach, we study several model and chemical Hamiltonians and investigate the performance of different implementations.

Physics↗
DOE OSTI2022

Supercurrent diode effect and finite-momentum superconductors

When both inversion and time-reversal symmetries are broken, the critical current of a superconductor can be nonreciprocal. In this work, we show that, in certain classes of two-dimensional superconductors with antisymmetric spin–orbit coupling, Cooper pairs acquire a finite momentum upon the application of an in-plane magnetic field, and, as a result, critical currents in the direction parallel and antiparallel to the Cooper pair momentum become unequal. This supercurrent diode effect is also manifested in the polarity dependence of in-plane critical fields induced by a supercurrent. These nonreciprocal effects may be found in polar SrTiO 3 film, few-layer MoTe 2 in the Td phase, and twisted bilayer graphene in which the valley degree of freedom plays a role analogous to spin.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Evaluating CUDA Portability with HIPCL and DPCT

HIPCL is expanding the scope of the CUDA portability route from an AMD platform to an OpenCL platform. In the meantime, the Intel DPC++ Compatibility Tool (DPCT) is migrating a CUDA program to a data parallel C++ (DPC++) program. Towards the goal of portability enhancement, we evaluate the performance of the CUDA applications from Rodinia, SHOC, and proxy applications ported using HIPCL and DPCT on Intel GPUs. After profiling the ported programs, we aim to understand their performance gaps, and optimize codes converted by DPCT to improve their performance. The open-source repository for the CUDA, HIP, and DPCT programs will be useful for the development of a translator.

Jin, Zheming↗
DOE OSTI2022

Planar 2D wireframe DNA origami

Two-dimensional (2D) DNA origami is widely used for applications ranging from excitonics to single-molecule biophysics. Conventional, single-layer 2D DNA origami exhibits flexibility and curvature in solution; however, that may limit its suitability as a 2D structural template. In contrast, 2D wireframe DNA origami rendered with six-helix bundle edges offers local control over duplex orientations with enhanced in-plane rigidity. Here, we investigate the 3D structure of these assemblies using cryo–electron microscopy (cryo-EM). 3D reconstructions reveal a high degree of planarity and homogeneity in solution for polygonal objects with and without internal mesh, enabling 10-Å resolution for a triangle. Coarse-grained simulations were in agreement with cryo-EM data, offering molecular structural insight into this class of 2D DNA origami. Our results suggest that these assemblies may be valuable for 2D material applications and geometries that require high structural fidelity together with local control over duplex orientations, rather than parallel duplex assembly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Electrofracturing of Shale at Elevated Pressure

Electrofracturing deeply buried shale formations could be used to increase reservoir permeability and improve reservoir production without requiring large volumes of freshwater. This paper describes a novel experimental system and initial test results to electrofracture shale under high confining pressures. Core-scale laboratory testing was performed on twelve rock samples recovered from a shale gas reservoir. Each sample was subjected to confining pressures of 20.7 MPa (3000 psi) or 58.6 MPa (8000 psi), representative of overburden pressures at depth. Samples were then subjected to application of high voltage until specimen fracture. The experiments produced deformed samples with multiple fracture types, both parallel and oblique to bedding planes. Electrofracturing increased permeabilities by up to nine orders of magnitude for extended time periods. Rock fracture and throughgoing fractures were demonstrated. Computed tomography images revealed the creation of fractures and tube/tunnel flow channels, which resisted closure under hydrostatic pressures up to 58.6 MPa. The breakdown energy and permeability changes in the sample were independent of applied confining pressure. The cumulative energy input required for fracture depended on applied confining pressure and sample length. The energy required to fracture samples up to 9 cm in length is generally more than 0.5 kJ/cm, but no greater than 1 kJ/cm. Our results show that electrofracture of shales under confining pressure is possible and could be a possible water-free mechanism for reservoir stimulation.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2023

PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis

Abstract. This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options, and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (∼100s m) to mesoscale (∼10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.

54 ENVIRONMENTAL SCIENCES↗