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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Parallel Nonnegative CP Decomposition of Dense Tensors

The CP tensor decomposition is a low-rank approximation of a tensor. We present a distributed-memory parallel algorithm and implementation of an alternating optimization method for computing a CP decomposition of dense tensors that can enforce nonnegativity of the computed low-rank factors. The principal task is to parallelize the Matricized-Tensor Times Khatri-Rao Product (MTTKRP) bottleneck subcomputation. The algorithm is computation efficient, using dimension trees to avoid redundant computation across MTTKRPs within the alternating method. Our approach is also communication efficient, using a data distribution and parallel algorithm across a multidimensional processor grid that can be tuned to minimize communication. We benchmark our software on synthetic as well as hyperspectral image and neuroscience dynamic functional connectivity data, demonstrating that our algorithm scales well to 100s of nodes (up to 4096 cores) and is faster and more general than the currently available parallel software.

Ballard, Grey↗
DOE OSTI2019

Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

Cyber security, Tensor decomposition, graph analyt↗
DOE OSTI2017

An approach to online network monitoring using clustered patterns

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

ROBIN (RuciO/BIgData Express/SENSE) A Next-Generation High-Performance Data Service Platform

Increasingly, scientific research discoverydepends on the analysis of extremely large amounts of data. Thisdata is gathered through very large scientific instruments, verylarge numbers of small devices, and mid-scale instruments.These devices generate exponentially increasing volumes of data.Such data is often obtained, analyzed, stored, visualized, andtransferred at and among sites world-wide by largecollaborations of science communities. Managing and movingextremely large volumes of data across today’s networks is aspecial multidimensional challenge. Also, existing datamanagement and movement services and tools often areinadequate to address all specific requirements. This paperdescribes the various components of this challenge and howthose issues can be addressed by ROBIN, a uniquecomprehensive set of integrated services designed specificallyfor managing and moving extremely large amounts of data overlong distances, e.g., thousands of miles around the globe. Thispaper also describes the results of initial experiments using thatset of integrated services.

Wu, Wenji↗
DOE OSTI2016

A Hartree-Fock Application Using UPC++ and the New DArray Library

The Hartree-Fock (HF) method is the fundamental first step for incorporating quantum mechanics into many-electron simulations of atoms and molecules, and it is an important component of computational chemistry toolkits like NWChem. The GTFock code is an HF implementation that, while it does not have all the features in NWChem, represents crucial algorithmic advances that reduce communication and improve load balance by doing an up-front static partitioning of tasks, followed by work stealing whenever necessary. To enable innovations in algorithms and exploit next generation exascale systems, it is crucial to support quantum chemistry codes using expressive and convenient programming models and runtime systems that are also efficient and scalable. Here, this paper presents an HF implementation similar to GTFock using UPC++, a partitioned global address space model that includes flexible communication, asynchronous remote computation, and a powerful multidimensional array library. UPC++ offers runtime features that are useful for HF such as active messages, a rich calculus for array operations, hardware-supported fetch-and-add, and functions for ensuring asynchronous runtime progress. We present a new distributed array abstraction, DArray, that is convenient for the kinds of random-access array updates and linear algebra operations on block-distributed arrays with irregular data ownership. Finally, we analyze the performance of atomic fetch-and-add operations (relevant for load balancing) and runtime attentiveness, then compare various techniques and optimizations for each. Our optimized implementation of HF using UPC++ and the DArrays library shows up to 20% improvement over GTFock with Global Arrays at scales up to 24,000 cores.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional Data Spaces

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.

Krause, Josua↗
DOE OSTI2022

Hands-On Computer Science: The Array of Things Experimental Urban Instrument

Chicago's Array of Things (AoT) project is aptly described as a technology experiment or a "smart city" prototype. The concept of such an extensible "instrument" arose within a larger translational research vision applying computer science and engineering research for the multidimensional benefit of people and communities in cities. The AoT project hypothesized that wireless intelligent sensor networks could enable both quantitative social science and urban monitoring while also stimulating youth interest in science and technology. Successful deployment of such sensor networks could provide open data from urban measurements not only in support of diverse research questions-in environmental dynamics, urban architecture, engineering, and social sciences-but also informing community groups and city planners. Further, the AoT project and its successor SAGE project are a computer science and engineering experiment, but its success is inextricably tied to community engagement and experiential education. Simply put, community acceptance is a prerequisite to installing and testing the instrument.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A PEPS Plugin for TNQVM

This work introduces an extension to the Tensor Network Quantum Virtual Machine (TNQVM) tool, enhancing the existing stack of ExaScale Tensor Network (ExaTN), ExaScale Accelerator (XACC), and TNQVM. It features a new plugin that enables efficient simulation of a Projected Entangled Pair State (PEPS), a 2D tensor network. To improve simulation efficiency for PEPS, we have implemented the snake boundary contraction algorithm. By integrating this capability into the existing stack, we enhance the overall functionality and versatility of the framework. We tested this new PEPS topology for a simple GHZ bell-pair generation quantum circuit and saw that its runtime is very close to that of the MPS topology. We estimate that the real potential of the PEPS topology becomes discernible when quantum circuits with multidimensional entanglement are simulated using tensor networks. In such cases, 1D tensor networks fail to represent or contract them efficiently.

Chundury, Srikar↗
DOE OSTI2024

OpenACC offloading of the MFC compressible multiphase flow solver on AMD and NVIDIA GPUs

GPUs are the heart of the latest generations of supercomputers. We efficiently accelerate a compressible multiphase flow solver via OpenACC on NVIDIA and AMD Instinct GPUs. Optimization is accomplished by specifying the directive clauses gang vector and collapse. Further speedups of six and ten times are achieved by packing user-defined types into coalesced multidimensional arrays and manual inlining via metaprogramming. Additional optimizations yield seven-times speedup of array packing and thirty-times speedup of select kernels on Frontier. Weak scaling efficiencies of 97% and 95% are observed when scaling to 50% of Summit and 87% of Frontier. Strong scaling efficiencies of 84% and 81% are observed when increasing the device count by a factor of 8 and 16 on V100 and MI250X hardware. The strong scaling efficiency of AMD’s MI250X increases to 92% when increasing the device count by a factor of 16 when GPU-aware MPI is used for communication.

Wilfong, Benjamin↗
DOE OSTI2020

Improving the Performance of Integrated Power-Hardware-in-the-Loop and Quasi-Static Time-Series Simulations

This paper introduces a novel multirate cosimulation architecture that overcomes previous challenges integrating faster real-time, microseconds-scale, power-hardware-in-the-loop (PHIL) with larger scale but slower, real-time, seconds-scale, quasi-static time-series (QSTS) simulation. Specifically, an intermediate reduced-equivalent electromagnetic transient (EMT) model duplicates the key power system topology to capture high-speed dynamics and converge hardware-power system interactions between QSTS updates. Exchanging multidimensional parameter vectors (loads, control status, etc.) between the QSTS and EMT models enables capturing the interactions of the rich, high-node-count (thousands of electrical nodes) QSTS model with the hardware. This architecture offers full spatial resolution from QSTS, including individual load dynamics, actual distribution management system controls, and nodal voltages. Simultaneously, the intermediate EMT model provides more detailed high-speed transients of key distributed energy resource hardware interactions. In addition, we use careful PHIL interface design and the exchange of complex power data, rather than current, to further improve performance. This cosimulation architecture is demonstrated by testing a simulated distribution system with an interconnected 500-kVA advanced photovoltaic (PV) inverter in PHIL. In conclusion, the architecture successfully captured PV local volt-volt ampere reactive (volt-VAR) control interactions with the larger network under time-varying electrical and weather conditions.

42 ENGINEERING↗
DOE OSTI2020

Ion Number Density Quantification Utilizing Pulsing Frequency in Negative Differential Resistance (NDR) Regime of Microplasma Operation

A mathematical model is presented for quantifying the ion number density over a range of discharge current in microplasma discharges. The ion number density is determined from measured discharge voltage, current, pulsing frequency, and trace impurity that is injected into the system deliberately. Experiments are conducted in pure helium with nitrogen being introduced as the trace species. The ion number density is estimated over a range of discharge current and is compared with multidimensional simulation results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

A Framework for Robust Long-Term Voltage Stability of Distribution Systems

Power injection uncertainties in distribution power grids, which are mostly induced by aggressive introduction of intermittent renewable sources, may drive the system away from normal operating regimes and potentially lead to the loss of long-term voltage stability (LTVS). Naturally, there is an ever increasing need for a tool for assessing the LTVS of a distribution system. This paper presents a fast and reliable tool for constructing inner approximations of LTVS regions in multidimensional injection space such that every point in our constructed region is guaranteed to be solvable. Numerical simulations demonstrate that our approach outperforms all existing inner approximation methods in most cases. Furthermore, the constructed regions are shown to cover substantial fractions of the true voltage stability region. In conclusion, the paper will later discuss a number of important applications of the proposed technique, including fast screening for viable injection changes, constructing an effective solvability index and rigorously certified loadability limits.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement

Visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POIs). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Improving Property Graph Layouts by Leveraging Attribute Similarity for Structurally Equivalent Nodes

Many real-world networks contain structurally-equivalent nodes. These are defined as vertices that share the same set of neighboring nodes, making them interchangeable with a traditional graph layout approach. However, many real-world graphs also have properties associated with nodes, adding additional meaning to them. We present an approach for swapping locations of structurally-equivalent nodes in graph layout so that those with more similar properties have closer proximity to each other. This improves the usefulness of the visualization from an attribute perspective without negatively impacting the visualization from a structural perspective. We include an algorithm for finding these sets of nodes in linear time, as well as methodologies for ordering nodes based on their attribute similarity, which works for scalar, ordinal, multidimensional, and categorical data.

graph drawing, network visualization, property gra↗
DOE OSTI2016

Bioenergy harvest impacts to biodiversity and resilience vary across aspen‐dominated forest ecosystems in the Lake States region, USA

Abstract Questions Does the increase in disturbance associated with removing harvest residues negatively impact biodiversity and resilience in aspen‐dominated forest ecosystems? How do responses of functional diversity measures relate to community recovery and standing biomass? Location Aspen ( Populus tremuloides, Michx.) mixedwood forests in Minnesota and Michigan, USA. Methods Three levels for two factors, organic matter removal and compaction, were fully crossed, resulting in nine experimental treatments that spanned a range of disturbance severity. Each treatment was replicated three times at each of three sites dominated by the same tree species but having different soil textures (clay, silty loam, sandy). Community composition and taxonomic diversity (species richness, species evenness, Shannon diversity index) were quantified based on woody species abundance sampled 5, 10 and 15 yr after disturbance. Community composition response was assessed using non‐metric multidimensional scaling. Functional diversity (functional richness, evenness, dispersion and divergence) was also estimated using eight species effect and response traits. Finally, we examined community recovery as well as responses of species and functional diversity to disturbance severity over time using repeated measures ANOVA. Results Two responses indicated a potentially negative impact of whole‐tree harvest relative to conventional, stem‐only harvest: functional richness on silty loam soils and species evenness on clayey soils. Otherwise, negative impacts were restricted to forest floor removal or increased compaction. Recovery in community composition was reduced by the most severe treatments, particularly forest floor removal, across the study, but the responses of functional and taxonomic diversity varied among sites, with some measures increasing as a result of severe disturbance. Conclusions Maximization of standing biomass may mean a short‐term sacrifice in species and functional diversity. Also, examinations of forest management impacts on species and functional diversity and composition should apply multiple metrics and indices to ensure potential impacts are not obscured by the reliance on a single approach.

Curzon, Miranda T.↗
DOE OSTI2026

Statistical generic design of glass and optimization: Selective review on oxide glasses

Designing a single glass composition for a multidimensional property space is challenging, and the difficulty increases with the number of design criteria. Traditionally, the task is accomplished using multiple statistical models that describe the relationships between composition (C) and property (P) values, i.e., C-P models. Recently, the structure (S)-property (P) statistical modeling has emerged as a complementary approach. The S-P modeling approach has also been shown to be a preferred method for modeling glass properties, particularly when a small data set is available, such as in single-component studies, or when strong nonlinearities exist between composition and properties. The combined model package, C-S-P, implements the concept of generic glass design, i.e., designing glass for performance by first selecting a specific or optimized set of glass network structural groups using S-P models and then transferring the designed structures (genes) to a particular composition using C-S models. This article reviews a set of supporting cases from the previous C-S-P modeling studies of phosphate, silicate, and borosilicate glasses, which are relevant for many critical commercial applications. The methodology for developing the statistical C-S-P database is presented, enabling the application of P?S?C to achieve a generic glass design and optimization, targeting multiple design criteria for both performance and processing properties simultaneously.

Network structure↗
DOE OSTI2016

A cell wall reference profile for Miscanthus bioenergy crops highlights compositional and structural variations associated with development and organ origin

Summary Miscanthus spp. are promising lignocellulosic energy crops, but cell wall recalcitrance to deconstruction still hinders their widespread use as bioenergy and biomaterial feedstocks. Identification of cell wall characteristics desirable for biorefining applications is crucial for lignocellulosic biomass improvement. However, the task of scoring biomass quality is often complicated by the lack of a reference for a given feedstock. A multidimensional cell wall analysis was performed to generate a reference profile for leaf and stem biomass from several miscanthus genotypes harvested at three developmentally distinct time points. A comprehensive suite of 155 monoclonal antibodies was used to monitor changes in distribution, structure and extractability of noncellulosic cell wall matrix glycans. Glycan microarrays complemented with immunohistochemistry elucidated the nature of compositional variation, and in situ distribution of carbohydrate epitopes. Key observations demonstrated that there are crucial differences in miscanthus cell wall glycomes, which may impact biomass amenability to deconstruction. For the first time, variations in miscanthus cell wall glycan components were comprehensively characterized across different harvests, organs and genotypes, to generate a representative reference profile for miscanthus cell wall biomass. Ultimately, this portrait of the miscanthus cell wall will help to steer breeding and genetic engineering strategies for the development of superior energy crops.

Plant Sciences↗
DOE OSTI2021

An integrated framework of plant form and function: the belowground perspective

Summary Plant trait variation drives plant function, community composition and ecosystem processes. However, our current understanding of trait variation disproportionately relies on aboveground observations. Here we integrate root traits into the global framework of plant form and function. We developed and tested an overarching conceptual framework that integrates two recently identified root trait gradients with a well‐established aboveground plant trait framework. We confronted our novel framework with published relationships between above‐ and belowground trait analogues and with multivariate analyses of above‐ and belowground traits of 2510 species. Our traits represent the leaf and root conservation gradients (specific leaf area, leaf and root nitrogen concentration, and root tissue density), the root collaboration gradient (root diameter and specific root length) and the plant size gradient (plant height and rooting depth). We found that an integrated, whole‐plant trait space required as much as four axes. The two main axes represented the fast–slow ‘conservation’ gradient on which leaf and fine‐root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth. This perspective on the multidimensional nature of plant trait variation better encompasses plant function and influence on the surrounding environment.

59 BASIC BIOLOGICAL SCIENCES↗