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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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DOE OSTI2013

A Framework for Load Balancing of Tensor Contraction Expressions via Dynamic Task Partitioning

In this paper, we introduce the Dynamic Load-balanced Tensor Contractions (DLTC), a domain-specific library for efficient task parallel execution of tensor contraction expressions, a class of computation encountered in quantum chemistry and physics. Our framework decomposes each contraction into smaller unit of tasks, represented by an abstraction referred to as iterators. We exploit an extra level of parallelism by having tasks across independent contractions executed concurrently through a dynamic load balancing run- time. We demonstrate the improved performance, scalability, and flexibility for the computation of tensor contraction expressions on parallel computers using examples from coupled cluster methods.

Tensor contraction, domain-specific language, dyna↗

DOE OSTI2025

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models

Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images from diverse sources—such as varying physical groundings or data acquisition systems—and to learn spatio-temporal correlations using transformer architectures. However, tokenizing and aggregating images can be compute-intensive, a challenge not fully addressed by current distributed methods. In this work, we introduce the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach designed for datasets with a large number of channels across image modalities. Our method is compatible with any model-parallel strategy and any type of vision transformer architecture, significantly improving computational efficiency. We evaluated D-CHAG on hyperspectral imaging and weather forecasting tasks. When integrated with tensor parallelism and model sharding, our approach achieved up to a 75% reduction in memory usage and more than doubled sustained throughput on up to 1,024 AMD GPUs on the Frontier Supercomputer.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗

DOE OSTI2022

Tacho

SAND2022-7470 O Tacho is a performance portable sparse direct solver for use with various sparse matrix problems which typically arise from finite element methods. The code relies on the Kokkos parallel programming model porting to both CPUs and GPUs. It is also part of the Trilinos library. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kim, Kyungjoo↗

DOE OSTI2015

Recommendations for the generation, quantification, storage and handling of peptides used for mass spectrometry-based assays

The Clinical Proteomic Tumor Analysis Consortium (1) (CPTAC) of the National Cancer Institute (NCI) is a comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the application of robust technologies and workflows for the quantitative measurements of proteins. The Assay Development Working Group of the CPTAC Program aims to foster broad uptake of targeted mass spectrometry-based assays employing isotopically labeled peptides for confident assignment and quantification, including multiple reaction monitoring (MRM; also referred to as Selected Reaction Monitoring), parallel reaction monitoring (PRM), and other targeted methods.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2022

Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. Here, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Integrated network design and scheduling problems :

We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.

Nurre, Sarah G.↗

DOE OSTI2018

Generation of Turbulence in Colliding Reconnection Jets

The collision of magnetic reconnection jets is reported on by means of a three-dimensional numerical simulation at the kinetic scale, in the presence of a strong guide field. We demonstrate that turbulence develops due to the collision of jets, producing several current sheets in reconnection outflows, aligned with the guide field direction. The turbulence is mainly two-dimensional, with stronger gradients in the plane perpendicular to the guide field and low wave-like activity in the parallel direction. First, we provide a numerical method to isolate the central turbulent region. Second, we analyze the spatial second-order structure function and prove that turbulence is confined in this region. Lastly, we compute local magnetic and electric frequency spectra, finding a trend in the subion range that differs from typical cases for which the Taylor hypothesis is valid, as well as wave activity in the range between ion and electron cyclotron frequencies. Our findings are relevant to understand observed collisions of reconnection jets in space plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Method of synchronizing independent functional unit

A system for synchronizing parallel processing of a plurality of functional processing units (FPU), a first FPU and a first program counter to control timing of a first stream of program instructions issued to the first FPU by advancement of the first program counter; a second FPU and a second program counter to control timing of a second stream of program instructions issued to the second FPU by advancement of the second program counter, the first FPU is in communication with a second FPU to synchronize the issuance of a first stream of program instructions to the second stream of program instructions and the second FPU is in communication with the first FPU to synchronize the issuance of the second stream program instructions to the first stream of program instructions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Method of synchronizing independent functional unit

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Method of synchronizing independent functional unit

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Parallel Fokker–Planck-DSMC algorithm for rarefied gas flow simulation in complex domains at all Knudsen numbers

A major challenge for the conventional Direct Simulation Monte Carlo (DSMC) technique lies in the fact that its computational cost becomes prohibitive in the near continuum regime, where the Knudsen number (Kn)—characterizing the degree of rarefaction—becomes small. In contrast, the Fokker–Planck (FP) based particle Monte Carlo scheme allows for computationally efficient simulations of rarefied gas flows in the low and intermediate Kn regime. The Fokker–Planck collision operator—instead of performing binary collisions employed by the DSMC method—integrates continuous stochastic processes for the phase space evolution in time. This allows for time step and grid cell sizes larger than the respective collisional scales required by DSMC. Dynamically switching between the FP and the DSMC collision operators in each computational cell is the basis of the combined FP-DSMC method, which has been proven successful in simulating flows covering the whole Kn range. Until recently, this algorithm had only been applied to two-dimensional test cases. In this contribution, we present the first general purpose implementation of the combined FP-DSMC method. Utilizing both shared- and distributed-memory parallelization, this implementation provides the capability for simulations involving many particles and complex geometries by exploiting state of the art computer cluster technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2013

Metrological characterization of X-ray diffraction methods at different acquisition geometries for determination of crystallite size in nano-scale materials

Crystallite size values were determined by X-ray diffraction methods for 183 powder samples. The tested size range was from a few to about several hundred nanometers. Crystallite size was calculated with direct use of the Scherrer equation, the Williamson–Hall method and the Rietveld procedure via the application of a series of commercial and free software. The results were statistically treated to estimate the significance of the difference in size resulting from these methods. We also estimated effect of acquisition conditions (Bragg–Brentano, parallel-beam geometry, step size, counting time) and data processing on the calculated crystallite size values. On the basis of the obtained results it is possible to conclude that direct use of the Scherrer equation, Williamson–Hall method and the Rietveld refinement employed by a series of software (EVA, PCW and TOPAS respectively) yield very close results for crystallite sizes less than 60 nm for parallel beam geometry and less than 100 nm for Bragg–Brentano geometry. However, we found that despite the fact that the differences between the crystallite sizes, which were calculated by various methods, are small by absolute values, they are statistically significant in some cases. The values of crystallite size determined from XRD were compared with those obtained by imaging in a transmission (TEM) and scanning electron microscopes (SEM). It was found that there was a good correlation in size only for crystallites smaller than 50 – 60 nm. Highlights: • The crystallite sizes for 183 nanopowders were calculated using different XRD methods • Obtained results were subject to statistical treatment • Results obtained with Bragg-Brentano and parallel beam geometries were compared • Influence of conditions of XRD pattern acquisition on results was estimated • Calculated by XRD crystallite sizes were compared with same obtained by TEM and SEM.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Parallel transport sweeps on two-dimensional cartesian and hexagonal grids

This paper aims to provide a proof of concept for parallel transport sweeps on two-dimensional hexagonal grids for the discrete ordinates transport equation. While the method is an extension of the popular and well-established Koch-Baker-Alcoulffe (KBA) algorithm, there are significant differences between the cartesian and hexagonal grid and thereafter sweep. The most important is the three-way connectivity of hexagons within the grid which creates greater dependencies between the elements. The KBA method in structured orthogonal grids was first implemented in the DRAGON5 code and the method is first described here. The differences in implementation for the hexagonal grid are also described. Benchmark results are also presented, showing roughly 10 times speedup in computational times with roughly 100 processors, in both cases. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

A meshless stochastic method for Poisson–Nernst–Planck equations

A plethora of biological, physical, and chemical phenomena involve transport of charged particles (ions). Its continuum-scale description relies on the Poisson–Nernst–Planck (PNP) system, which encapsulates the conservation of mass and charge. The numerical solution of these coupled partial differential equations is challenging and suffers from both the curse of dimensionality and difficulty in efficiently parallelizing. We present a novel particle-based framework to solve the full PNP system by simulating a drift–diffusion process with time- and space-varying drift. We leverage Green’s functions, kernel-independent fast multipole methods, and kernel density estimation to solve the PNP system in a meshless manner, capable of handling discontinuous initial states. The method is embarrassingly parallel, and the computational cost scales linearly with the number of particles and dimension. We use a series of numerical experiments to demonstrate both the method’s convergence with respect to the number of particles and computational cost vis-à-vis a traditional partial differential equation solver.

Chemistry↗

DOE OSTI2021

Detailed study of the eikonal reaction theory for the breakup of one-neutron halo nuclei

Background: One-neutron removal reactions are used to study the single-particle structure of unstable nuclei, and in particular the exotic halo nuclei. The eikonal reaction theory (ERT) has been developed by Yahiro, Ogata, and Minomo [Prog. Theor. Phys. 126, 167 (2011)] to include dynamical effects, which are missing in the usual eikonal description of these reactions. Encouraging results have been obtained for total breakup cross sections in comparison to more elaborate reaction models. Purpose: Here, we extend these comparisons to more differential breakup cross sections expressed as functions of the relative energy or parallel momentum between the core and halo neutron. Method: ERT predictions of these cross sections are compared to state-of-the-art calculations. Results: The hypotheses upon which the ERT is based are confirmed and their range of validity is made clearer. The actual ordering of the evolution operators affects ERT differential cross sections and a specific choice leads to excellent agreement with the reference calculation. Dynamical effects in the treatment of the neutron-target interaction can be significant in the parallel-momentum observable. Conclusions: The role of the different interactions in the dynamics of breakup reactions of one-neutron halo nuclei are better understood and improvements to the ERT are suggested.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Accelerating Bilevel Optimization With Hierarchical Many-Threaded Parallel Differential Evolution

Bilevel optimization is encountered in many relevant real-world applications. The main feature of this type of problem is that an upper-level optimization problem is constrained by a nested lower-level optimization problem. Because of this nested structure, bilevel problems (BLPs) are usually computationally expensive to solve. Differential evolution (DE) has demonstrated promising results in solving BLPs of relatively small scales. As the problem scale increases, the decision space becomes intrinsically larger, requiring a growing number of function evaluations for the method to work properly. In this context, heavy parallelization and high-performance computing techniques are indispensable to enable the resolution of more complex and challenging optimization problems. Hence, we propose a hierarchical many-threaded parallel DE approach for BLPs, where both levels are parallelized. The computational experiments demonstrate that the parallel implementation achieved runtime speeds ranging from 44 to 2559 times faster than the sequential version on a well-known scalable SMD benchmark test problem when executed on an NVIDIA A100 GPU. The findings indicate that the algorithm’s convergence is strongly influenced by the number of both upper- and lower-level generations. Moreover, the success of experiments with large-scale problems is closely linked to the choice of small population sizes.

Dufek, Amanda S↗

DOE OSTI2016

Multiwall carbon nanotube microcavity arrays

Periodic highly dense multi-wall carbon nanotube (MWCNT) arrays can act as photonic materials exhibiting band gaps in the visible regime and beyond terahertz range. MWCNT arrays in square arrangement for nanoscale lattice constants can be configured as a microcavity with predictable resonance frequencies. Here, computational analyses of compact square microcavities (≈0.8 × 0.8 μm{sup 2}) in MWCNT arrays were demonstrated to obtain enhanced quality factors (≈170–180) and narrow-band resonance peaks. Cavity resonances were rationally designed and optimized (nanotube geometry and cavity size) with finite element method. Series (1 × 2 and 1 × 3) and parallel (2 × 1 and 3 × 1) combinations of microcavities were modeled and resonance modes were analyzed. Higher order MWCNT microcavities showed enhanced resonance modes, which were red shifted with increasing Q-factors. Parallel microcavity geometries were also optimized to obtain narrow-band tunable filtering in low-loss communication windows (810, 1336, and 1558 nm). Compact series and parallel MWCNT microcavity arrays may have applications in optical filters and miniaturized optical communication devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Scalable Static and Dynamic Community Detection Using Grappolo

Graph clustering, popularly known as community detection, is a fundamental kernel for several applications of relevance to the Defense Advanced Research Projects Agency’s (DARPA) Hierarchical Identify Verify Exploit (HIVE) Pro- gram. Clusters or communities represent natural divisions within a network that are densely connected within a cluster and sparsely connected to the rest of the network. The need to compute clustering on large scale data necessitates the development of efficient algorithms that can exploit modern architectures that are fundamentally parallel in nature. How- ever, due to their irregular and inherently sequential nature, many of the current algorithms for community detection are challenging to parallelize. In response to the HIVE Graph Challenge, we present several parallelization heuristics for fast community detection using the Louvain method as the serial template. We implement all the heuristics in a software library called Grappolo. Using the inputs from the HIVE Challenge, we demonstrate superior performance and high quality solutions based on four parallelization heuristics. We use Grappolo on static graphs as the first step towards community detection on streaming graphs.

Community Detection↗