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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Editorial: Themed issue on data-intensive water systems management and operation

The water sector increasingly looks at intensified instrumentation, data collection and automation as tools for daily use. Still, a massive opportunity remains to fully embrace emerging methods and technologies, such as artificial intelligence, data analytics, low-cost sensor hardware, and cloud computing. Indeed, sensing and automation technology has already infiltrated many facets of society. As such, the time is ripe to evaluate the role of novel technologies for systems monitoring, diagnostics, and automation of aquatic processes and large-scale water systems. When leveraged, the water sector will do more with less.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Investigating magnetic van der Waals materials using data-driven approaches

In this work, we investigate magnetic monolayers of the form A i A ii B 4 X 8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi 2 Te 4 (MBT) using first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials’ properties. The dependence of materials’ properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. Finally, this study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Spectroscopic studies on two mono nuclear iron (III) complexes derived from a schiff base and an azodye

Two new mono nuclear Fe(III) complexes of an azodye (ANSN) and a Schiff base (FAHP) are reported. The azodye is prepared by coupling diazotized 1-amino-2-naphthol-4-sulphonicacid with 2-naphthol and the Schiff base is prepared by condensing 2-amino-3-hydroxy pyridine with furfural. The complexes were synthesized by the reaction of FeCl{sub 3}Ðœ‡2H{sub 2}O with respective ligands. They were characterized on the basis of elemental analysis and spectral studies like IR, NMR, Electronic and M.ssbauer. Magnetic susceptibility and Molar conductance of complexes at room temperature were studied. Based on the spectroscopic evidences and other analytical data the complexes are formulated as[Fe(ANSN)Cl(H{sub 2}O){sub 2}] and [Fe(FAHP)Cl{sub 2}(H{sub 2}O){sub 2}].

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2014

Synthesis and spectral study of new azo dye and its iron complexes derived from 2-naphthol and 2-amino-3-hydroxypyridine

An azodye C{sub 5}H{sub 12}N{sub 3}O{sub 2} [LH] is synthesised by coupling diazotised 2-amino-3-hydroxy pyridine with 2-naphthol in ice cold condition. The Fe(II)and Fe(III) complexes were prepared by mixing ethanol solution of metal salt and azodye in 1:2 molar ratio. The dye and metal complexes are structurally characterised by elemental analysis, molar conductance, magnetic susceptibility measurements and spectral techniques like IR, UV-Vis, and Mössbauer analysis. Analytical data suggests the stoichiometry as [FeL{sub 2}Cl(H{sub 2}O)] for Fe(III) complex and [FeL{sub 2}(H{sub 2}O)] for Fe(II) complex. The IR spectral data suggests that [L{sup −}] is acting as a uninegative bidentate ligand. A high spin octahedral geometry is tentatively proposed for both the complexes with respect to the above studies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2015

Single-particle structure determination by X-ray free-electron lasers: Possibilities and challenges

Single-particle structure recovery without crystals or radiation damage is a revolutionary possibility offered by X-ray free-electron lasers, but it involves formidable experimental and data-analytical challenges. Many of these difficulties were encountered during the development of cryogenic electron microscopy of biological systems. Electron microscopy of biological entities has now reached a spatial resolution of about 0.3 nm, with a rapidly emerging capability to map discrete and continuous conformational changes and the energy landscapes of biomolecular machines. Nonetheless, single-particle imaging by X-ray free-electron lasers remains important for a range of applications, including the study of large “electron-opaque” objects and time-resolved examination of key biological processes at physiological temperatures. After summarizing the state of the art in the study of structure and conformations by cryogenic electron microscopy, we identify the primary opportunities and challenges facing X-ray-based single-particle approaches, and possible means for circumventing them.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Advance study of fiber-reinforced self-compacting concrete

Incorporation in concrete composition of steel macro- and micro – fiber reinforcement with structural function increases the degree of ductility of typically brittle cement-containing composites, which in some cases can replace completely or partially conventional steel reinforcement in the form of rods and meshes. Thus, that can reduce manufacturing, detailing and placement of conventional reinforcement, which enhances productivity and economic efficiency of the building process. In this paper, six fiber-reinforced with different amounts of steel fiber cement-containing self-compacting compositions are investigated. The results of some of their main strength-deformation characteristics are presented. Advance approach for the study of structural and material properties of these type composites is proposed by using the methods of industrial computed tomography. The obtained original tomography results about the microstructure and characteristics of individual structural components make it possible to analyze the effective macro-characteristics of the studied composites. The resulting analytical data are relevant for the purposes of multi-dimensional modeling of these systems. Multifactor structure-mechanical analysis of the obtained with different methods original scientific results is proposed. It is presented a conclusion of the capabilities and effectiveness of complex analysis in the studies to characterize the properties of self-compacting fiber-reinforced concrete.

42 ENGINEERING↗

DOE OSTI2011

NHC-based Self-assembled Monolayers on Solid Gold Substrates

Thin films of 1,3-diethylbenzimidazol-2-ylidene (BIEt) were fabricated from THF solution on solid gold substrates and characterised by high-resolution X-ray photoelectron and near-edge X-ray absorption fine structure spectroscopy. The surface-analytical data are in accord with the formation of self-assembled monolayers of BIEt molecules exhibiting an approximately vertical orientation on the substrate. The crystal structure of (BIEt){sub 2} was also determined.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Accelerating the rate of discovery: toward high-repetition-rate HED science

As high-intensity short-pulse lasers that can operate at high-repetition-rate (HRR) (>10 H z ) come online around the world, the high energy density (HED) science they enable will experience a radical paradigm shift. The >10 3 increase in shot rate over today's shot-per-hour drivers translates into dramatically faster data acquisition, more experiments, and the ability to exploit machine learning, and thus the potential to significantly accelerate the advancement of HED science. A wide range of HED experiments, from opacity investigations to secondary source generation to plasma nuclear physics, will benefit from the increased statistics, precision, and exploration of phase space. Besides increasing the rate at which scientific experiments can be performed, HRR also allows for the rapid delivery of optimal experiments supported by simulations and modeling augmented by close coupling to empirical data. To fully realize such an HRR framework, numerous subsystems must be developed and brought together, including feedback laser control loops, high-throughput targetry and diagnostics, cognitive simulation, enhanced HED codes, and advanced data analytics. This paper describes the vision for an integrated HRR laser experimental HED system and outlines some of the major considerations and challenges for realizing it.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Defining the Synthetic Biology Supply Chain

In this article, a team of experts in synthetic biology, data analytics, and national security describe the overall supply chain surrounding synthetic biology. The team analyzes selected interactions within that network to better understand the risks raised by synthetic biology and identifies opportunities for risk mitigation. To introduce the concept, the article will briefly describe how an understanding of supply chains has been important in promoting nuclear nonproliferation objectives. The article concludes by assessing the structure and networks identified in the supply chains to reveal potential opportunities for future biodefense research and development; options for additional information exchange; and means to interdict, detect, or deter suspicious activity.

nuclear supply chain↗

DOE OSTI2024

Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

A Distribution Level Wide Area Monitoring System for the Electric Power Grid–FNET/GridEye

The wide area monitoring system (WAMS) is considered a pivotal component of future electric power grids. As a pilot WAMS that has been operated for more than a decade, the frequency monitoring network FNET/GridEye makes use of hundreds of global positioning system-synchronized phasor measurement sensors to capture the increasingly complicated grid behaviors across the interconnected power systems. In this paper, the FNET/GridEye system is overviewed and its operation experiences in electric power grid wide area monitoring are presented. Particularly, the implementation of a number of data analytics applications will be discussed in details. FNET/GridEye lays a firm foundation for the later WAMS operation in the electric power industry.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

EGBTER: Capturing Degree Distribution, Clustering Coefficients, and Community Structure in a Single Random Graph Model

Random graph models are important constructs for data analytic applications as well as pure mathematical developments, as they provide capabilities for network synthesis and principled analysis. Several models have been developed with the aim of faithfully preserving important graph metrics and substructures. With the goal of capturing degree distribution, clustering coefficient, and communities in a single random graph model, we propose a new model to address shortcomings in a progression of network modeling capabilities. The Block Two-Level Erdos-Rényi (BTER) model of Seshadhri et aI., designed to allow prescription of expected degree and clustering coefficient distributions, neglects community modeling, while the Generalized BTER (GBTER) model of Bridges et aI., designed to add community modeling capabilities to BTER, struggles to faithfully represent all three characteristics simultaneously. In this work, we fit BTER and two GBTER configurations to several real-world networks and compare the results with that of our new model, the Extended GBTER (EGBTER) model. Our results support that EBGTER adds a community-modeling flexibility to BTER, while retaining a satisfactory level of accuracy in terms of degree and clustering coefficient. Our insights and empirical testing of previous models as well as the new model are novel contributions to the literature.

El-Daghar, Omar↗

DOE OSTI2018

A Flexible-blocking Based Approach for Performance Tuning of Matrix Multiplication Routines for Large Matrices with Edge Cases

Efficient and scalable matrix operations are being highly demanding in the recent era of Machine Learning, Deep Learning, and Big Data Analytics. The two commonly used matrix-matrix operations in the Basic Linear Algebra Subprograms (BLAS) specification are General Matrix-Matrix multiplication (GEMM) and Symmetric Rank-k update (SYRK). The SYRK routine is a specialization of the GEMM routine, where half of the multiplications are skipped as the resultant matrix is known to be symmetric. Fortunately, several linear algebra libraries implement these BLAS routines quite efficiently. The libraries usually partition the input matrices into blocks and place them in processor caches, thus improving performance by leveraging the caches. However, the contemporary libraries are highly optimized for squarish matrices, but the performance degrades significantly for the matrices with edge case (strictly thin or strictly fat shapes) in the multicore machine. The primary reason is that the current state-of-the-art libraries make fixed block shapes based on a processor architecture, and do not consider the shape of the input matrices. In this paper, we propose a new blocking approach, we name it Flexible-blocking, to mitigate the scalability issues. In contrast to the contemporary libraries, our approach formulates the blocks of the input matrices based on the shapes of the matrices as well as the number of threads used in the implementation. Our proposed technique shows noticeable performance improvement on multicore shared-memory machines for the edge case matrices.

Hossain, Md Mosharaf↗

DOE OSTI2021

A Lakehouse Architecture for the Management and Analysis of Heterogeneous Data for Biomedical Research and Mega-biobanks

Data Lakehouse is a new paradigm in data architectures that embodies and integrates already established concepts for the systematic management of disparate, large-scale data – a data lake for heterogeneous data management, use of open standards for high-performance querying, and systematic maintenance of the data "freshness". In addition to being a new concept, the data lakehouse is also still a conceptual construct. Many projects that use the lakehouse require maturing, empirical studies, and specific implementations. In this paper, we present our implementation of the data lakehouse concept in a biomedical research and health data analytics domain, and we discuss the implementation of some unique and novel features such as support for specialized access controls in support of HIPAA regulation and IRB protocols, and support for the FAIR standard.

Begoli, Edmon↗

DOE OSTI2017

AnalyzeThat: A Programmable Shared-Memory System for an Array of Processing-In-Memory Devices

Processing In Memory (PIM), the concept of integrating processing directly with memory, has been attracting a lot of attention since PIM can assist in overcoming the throughput limitation caused by data movement between CPU and memory. The challenge, however, is that it requires the programmers to have a deep understanding of the PIM architecture to maximize the benefits such as data locality and parallel thread execution on multiple PIM devices. In this study, we present AnalyzeThat, a programmable shared-memory system for parallel data processing with PIM devices. Thematic to AnalyzeThat is a rich PIM-Aware Data Structure (PADS), which is an encapsulation that integrally ties together the data, the analysis tasks and the runtime needed to interface with the PIM device array. The PADS abstraction provides (i) a key-value data container that allows programmers to easily store data on multiple PIMs, (ii) a suite of parallel operations with which users can easily implement data analysis applications, and (iii) a runtime, hidden to programmers, which provides the mechanisms needed to overlay both the data and the tasks on the PIM device array in an intelligent fashion, based on PIM-specific information collected from the hardware. We have developed a PIM emulation framework called AnalyzeThat. Our experimental evaluation with representative data analytics applications suggests that the proposed system can significantly reduce the PIM programming effort without losing its technology benefits.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Parallel I/O Evaluation Techniques and Emerging HPC Workloads: A Perspective

Emerging workloads such as artificial intelligence, big data analytics and complex multi-step workflows alongside future exascale applications are anticipated future HPC workloads, which will result in a more diverse I/O system workload and even less predictable I/O behavior and access patterns. Along with the ever increasing gap between the compute and storage performance capabilities, the in-depth understanding of extreme-scale I/O behavior and the I/O performance modeling and prediction are essential tools of the large-scale I/O evaluation process for addressing the needs of extreme-scale hybrid workloads. In this survey article, we focus on the state-of-the-art of the I/O behavior and performance analysis process for HPC systems in a 5-year time window and identify future research challenges.

Neuwirth, Sarah↗

DOE OSTI2020

CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters

The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments , acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing in-frastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.

batch job preemption↗

DOE OSTI2021

Parallel Algorithms for Efficient Computation of High-Order Line Graphs of Hypergraphs

This paper considers structures of systems beyond dyadic (pairwise) interactions and investigates mathematical modeling of multi-way interactions and connections as hypergraphs, where captured relationships among system entities are set-valued. To date, in most situations, entities in a hypergraph are considered connected as long as there is at least one common ``neighbor''. However, minimal commonality sometimes discards the ``strength'' of connections and interactions among groups. To this end, considering the ``width'' of a connection, referred to as the \emph{$s$-overlap} of neighbors, provides more meaningful insights into how closely the communities or entities interact with each other. In addition, $s$-overlap computation is the fundamental kernel to construct the line graph of a hypergraph, a low-order approximation of the hypergraph which can carry significant information about the original hypergraph. Subsequent stages of a data analytics pipeline then can apply highly-tuned graph algorithms on the line graph to reveal important features. Given a hypergraph, computing the $s$-overlaps by exhaustively considering all pairwise entities can be computationally prohibitive. To tackle this challenge, we develop efficient algorithms to compute $s$-overlaps and the corresponding line graph of a hypergraph. We propose several heuristics to avoid execution of redundant work and improve performance of the $s$-overlap computation. Our parallel algorithm, combined with these heuristics, is orders of magnitude (more than $10\times$) faster than the naive algorithm in all cases and the SpGEMM algorithm with filtration in most cases (especially with large $s$ value).

hypergraph algorithms, graph algorithms, parallel ↗