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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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31 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Gaussian Mixture Models for Temporal Depth Fusion

Sensing the 3D environment of a moving robot is essential for collision avoidance. Most 3D sensors produce dense depth maps, which are subject to imperfections due to various environmental factors. Temporal fusion of depth maps is crucial to overcome those. Temporal fusion is traditionally done in 3D space with voxel data structures, but it can be approached by temporal fusion in image space, with potential benefits in reduced memory and computational cost for applications like reactive collision avoidance for micro air vehicles. In this paper, we present an efficient Gaussian Mixture Models based depth map fusion approach, introducing an online update scheme for dense representations. The environment is modeled from an ego-centric point of view, where each pixel is represented by a mixture of Gaussian inverse-depth models. Consecutive frames are related to each other by transformations obtained from visual odometry. This approach achieves better accuracy than alternative image space depth map fusion techniques at lower computational cost.

Matthies, Larry↗
DOE OSTI2020

Highly Asynchronous Visitor Queue Graph Toolkit

HavoqGT (Highly Asynchronous Visitor Queue Graph Toolkit) is a framework for expressing asynchronous vertex-centric graph algorithms, and executing them on High Performance Computing (HPC) systems. It provides a vertex 'visitor' interface, where actions are defined at an individual vertex level, and contains a suite of classic graph algorithms. HavoqGT is capable of processing large graphs stored in NVRAM (SSDs) using a memory mapped interface.

Reza, TahsinA.↗
NASA NTRS2012

Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System

As communication and networking technologies advance, networks will become highly complex and heterogeneous, interconnecting different network domains. There is a need to provide user authentication and data protection in order to further facilitate critical mission operations, especially in the tactical and mission-critical net-centric networking environment. The Autonomous Information Unit (AIU) technology was designed to provide the fine-grain data access and user control in a net-centric system-testing environment to meet these objectives. The AIU is a fundamental capability designed to enable fine-grain data access and user control in the cross-domain networking environments, where an AIU is composed of the mission data, metadata, and policy. An AIU provides a mechanism to establish trust among deployed AIUs based on recombining shared secrets, authentication and verify users with a username, X.509 certificate, enclave information, and classification level. AIU achieves data protection through (1) splitting data into multiple information pieces using the Shamir's secret sharing algorithm, (2) encrypting each individual information piece using military-grade AES-256 encryption, and (3) randomizing the position of the encrypted data based on the unbiased and memory efficient in-place Fisher-Yates shuffle method. Therefore, it becomes virtually impossible for attackers to compromise data since attackers need to obtain all distributed information as well as the encryption key and the random seeds to properly arrange the data. In addition, since policy can be associated with data in the AIU, different user access and data control strategies can be included. The AIU technology can greatly enhance information assurance and security management in the bandwidth-limited and ad hoc net-centric environments. In addition, AIU technology can be applicable to general complex network domains and applications where distributed user authentication and data protection are necessary. AIU achieves fine-grain data access and user control, reducing the security risk significantly, simplifying the complexity of various security operations, and providing the high information assurance across different network domains.

Chow, Edward T.↗
DOE OSTI2022

Reproduced Computational Results Report for “Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing”

The article titled “Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing” by Anzt et al. presents a modern, linear operator centric, C++ library for sparse linear algebra. Experimental results in the article demonstrate that Ginkgo is a flexible and user-friendly framework capable of achieving high-performance on state-of-the-art GPU architectures. In this report, the Ginkgo library is installed and a subset of the experimental results are reproduced. Specifically, the experiment that shows the achieved memory bandwidth of the Ginkgo Krylov linear solvers on NVIDIA A100 and AMD MI100 GPUs is redone and the results are compared to what presented in the published article. Upon completion of the comparison, the published results are deemed reproducible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Autonomous Byte Stream Randomizer

Net-centric networking environments are often faced with limited resources and must utilize bandwidth as efficiently as possible. In networking environments that span wide areas, the data transmission has to be efficient without any redundant or exuberant metadata. The Autonomous Byte Stream Randomizer software provides an extra level of security on top of existing data encryption methods. Randomizing the data s byte stream adds an extra layer to existing data protection methods, thus making it harder for an attacker to decrypt protected data. Based on a generated crypto-graphically secure random seed, a random sequence of numbers is used to intelligently and efficiently swap the organization of bytes in data using the unbiased and memory-efficient in-place Fisher-Yates shuffle method. Swapping bytes and reorganizing the crucial structure of the byte data renders the data file unreadable and leaves the data in a deconstructed state. This deconstruction adds an extra level of security requiring the byte stream to be reconstructed with the random seed in order to be readable. Once the data byte stream has been randomized, the software enables the data to be distributed to N nodes in an environment. Each piece of the data in randomized and distributed form is a separate entity unreadable on its own right, but when combined with all N pieces, is able to be reconstructed back to one. Reconstruction requires possession of the key used for randomizing the bytes, leading to the generation of the same cryptographically secure random sequence of numbers used to randomize the data. This software is a cornerstone capability possessing the ability to generate the same cryptographically secure sequence on different machines and time intervals, thus allowing this software to be used more heavily in net-centric environments where data transfer bandwidth is limited.

Paloulian, George K.↗
DOE OSTI2024

FPGA-Accelerated Range-Limited Molecular Dynamics

Long timescale Molecular Dynamics (MD) simulation of small molecules is crucial in drug design and basic science. To accelerate a small data set that is executed for a large number of iterations, high-efficiency is required. Recent work in this domain has demonstrated that among COTS devices only FPGA-centric clusters can scale beyond a few processors. The problem addressed here is that, as the number of on-chip processors has increased from fewer than 10 into the hundreds, previous intra-chip routing solutions are no longer viable. We find, however, that through various design innovations, high efficiency can be maintained. These include replacing the previous broadcast networks with ring-routing and then augmenting the rings with out-of-order and caching mechanisms. Others are adding a level of hierarchical filtering and memory recycling. Two novel optimized architectures emerge, together with a number of variations. These are validated, analyzed, and evaluated. We find that in the domain of interest speed-ups over GPUs are achieved. Finally, the potential impact is that this system promises to be the basis for scalable long timescale MD with commodity clusters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

JANA2 LDRD (Final Project Report)

The project sought to develop a second generation of the JANA reconstruction framework that leverages modern programming features (C++11 and C++14) while maintaining its basic user centric design. The goals included making the project easy to deploy to large HPC facilities such as NERSC and to include detailed documentation to allow scientists outside of Jefferson Lab to use it at such a facility. This would position JLab to play a key role in the larger Laboratory based computing ecosystem under the ASCR initiative. The experience gained will help inform the design of Exascale class applications for experimental nuclear physics. Developing a next generation JANA provides an advantage over older serial frameworks or ones retro-fitted to provide some multi-threading support. That technique requires significantly more memory than multi-threading which leads to compute jobs being limited by available RAM, and not fully utilizing the available CPU. The smaller memory footprint of multi-threaded programs also leads to increased efficiency due to fewer cache misses. It is for these and other reasons that multi-threading is the more accepted industry standard in the modern era.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2018

Guideline for Single-Event Effect (SEE) Testing of System on a Chip (SOC) Devices

The use of complex single and multicore processors with significant cache memory, on-chip peripherals, memory controllers, and high speed input/output (IO) that integrate many of the parts of a traditional computer system is becoming more common in space applications. Such devices are often referred to as system on a chip devices (SOCs), even though the term is used somewhat inaccurately due to the lack of analog and mixed signal subcircuits. These devices are complex combinations of single- or multi-core processors with memory controllers, high-speed input/output (IO), and other peripheral structures that formerly would have been handled by off-chip resources. In the past the processors were tested for single event effects (SEE) separately, and the peripherals were often put into custom application-specific integrated circuits (ASICs) along with other resources required by the user. Performance and cost pressures have pushed commercial devices to incorporate many of the functional blocks into a single chip, an SOC. The NASA Electronic Parts and Packaging Program (NEPP) has been examining ways to perform SEE radiation hardness assurance (RHA) testing of these processor-centric SOCs to achieve reasonable understanding of their performance in space missions.

Guertin, Steven M.↗
DOE OSTI2025

Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. Here, in this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach.

Li, Jinyang [George Mason Univ., Fairfax, VA (Unit↗
NASA NTRS2012

NASA Tech Briefs, April 2012

Topics include: Computational Ghost Imaging for Remote Sensing; Digital Architecture for a Trace Gas Sensor Platform; Dispersed Fringe Sensing Analysis - DFSA; Indium Tin Oxide Resistor-Based Nitric Oxide Microsensors; Gas Composition Sensing Using Carbon Nanotube Arrays; Sensor for Boundary Shear Stress in Fluid Flow; Model-Based Method for Sensor Validation; Qualification of Engineering Camera for Long-Duration Deep Space Missions; Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms; Bump Bonding Using Metal-Coated Carbon Nanotubes; In Situ Mosaic Brightness Correction; Simplex GPS and InSAR Inversion Software; Virtual Machine Language 2.1; Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction; Pandora Operation and Analysis Software; Fabrication of a Cryogenic Bias Filter for Ultrasensitive Focal Plane; Processing of Nanosensors Using a Sacrificial Template Approach; High-Temperature Shape Memory Polymers; Modular Flooring System; Non-Toxic, Low-Freezing, Drop-In Replacement Heat Transfer Fluids; Materials That Enhance Efficiency and Radiation Resistance of Solar Cells; Low-Cost, Rugged High-Vacuum System; Static Gas-Charging Plug; Floating Oil-Spill Containment Device; Stemless Ball Valve; Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs; Oxygen-Methane Thruster; Lunar Navigation Determination System - LaNDS; Launch Method for Kites in Low-Wind or No-Wind Conditions; Supercritical CO2 Cleaning System for Planetary Protection and Contamination Control Applications; Design and Performance of a Wideband Radio Telescope; Finite Element Models for Electron Beam Freeform Fabrication Process Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System; Vehicle Detection for RCTA/ANS (Autonomous Navigation System); Image Mapping and Visual Attention on the Sensory Ego-Sphere; HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis; and IMAGESEER - IMAGEs for Education and Research.

Source record↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗